	*************************************************
	*                                               *
	*          ONE-View report generation           *
	*                                               *
	*************************************************

[MAQAO] Info: Experiment configuration summary is available adding -dbg=1 in command line

* [MAQAO] Warning: Experiment directory /beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/run/oneview_runs/defaults/aocc/oneview_results_1790694592 already exists and is reused.
           It can be replaced using --replace in the command line.
[MAQAO] Info: 
[MAQAO] Info: START THE APPLICATION PROFILING
[MAQAO] Info: -> RUNNING THE PROFILER...
[MAQAO] Info:   LPROF has already been run
[MAQAO] Info: STOP THE APPLICATION PROFILING
[MAQAO] Info: 
[MAQAO] Info: START FUNCTIONS AND LOOPS ANALYSIS ...
[MAQAO] Info: STOP FUNCTIONS AND LOOPS ANALYSIS ...
[MAQAO] Info: 
[MAQAO] Info: START THE REPORT GENERATION
[MAQAO] Info: -> ONE-VIEW EXPERIMENT DIRECTORY: /beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/run/oneview_runs/defaults/aocc/oneview_results_1790694592


+====================================================================================================================+
+                                                    1  -  GLOBAL                                                    +
+====================================================================================================================+


+--------------------------------------------------------------------------------------------------------------------+
+                                             1.1  -  Experiment Summary                                             +
+--------------------------------------------------------------------------------------------------------------------+

  Application:			/beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/run/oneview_runs/defaults/orig/exec
  Timestamp:			2026-09-29 17:09:52
  Universal Timestamp:		1790694592
  Experiment Type:		MPI; OpenMP; Throughput; 
  Machine:			gmz16.benchmarkcenter.megware.com
  Architecture:			x86_64
  Micro Architecture:		ZEN_V4
  Model Name:			AMD EPYC 9654 96-Core Processor
  Cache Size:			1024 KB
  Number of Cores:		96
  OS Version:			Linux 5.14.0-687.29.1.el9_8.x86_64 #1 SMP PREEMPT_DYNAMIC Thu Jul 23 16:18:48 EDT 2026
  Compilation Options:		
		exec:  F90 AOCCAOCC_5.1.0-Build#1994 2025_12_23 &apos;+flang -I/beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/build/CloverLeaf1.3-FC/CloverLeaf_ref/kernels -O3 -march=native -Qunused-arguments -g -fno-omit-frame-pointer -fcf-protection=none -no-pie -fopenmp -c -o -I/cluster/hpcx/2.50/ompi5-aocc-mt/include -I/cluster/hpcx/2.50/ompi5-aocc-mt/lib&apos; 
  Number of processes observed:	8
  Number of threads observed:	192
  MAQAO version:		2026.1.0
  MAQAO build:			6d1be1d51c1e63266254997eb301734a7264775d::20260810-150026




+--------------------------------------------------------------------------------------------------------------------+
+                                               1.2  -  Global Metrics                                               +
+--------------------------------------------------------------------------------------------------------------------+

  Total Time:				52.74 s
  Max (Thread Active Time):		52.41 s
  Average Active Time:			51.79 s
  Activity Ratio:			100.0 %
  Average number of active threads:	188.538
  Affinity Stability:			99.9 %
  Time spent in analyzed loops:		95.9 %
  Time spent in analyzed innermost loops: 95.6 %
  Time spent in user code:		96.1 %
  Compilation Options Score:		100
  Array Access Efficiency:		96.9 %

   Potential Speedups
  ----------------------------------------------------
  Perfect Flow Complexity:		1.09
  Perfect OpenMP/MPI/Pthread/TBB:	1.01
  Perfect OpenMP/MPI/Pthread/TBB + Load Distribution:	1.04
  If No Scalar Integer:
      Potential Speedup:		1.01
      Nb Loops to get 80%:		3
  If FP Vectorized:
      Potential Speedup:		1.12
      Nb Loops to get 80%:		4
  If Fully Vectorized:
      Potential Speedup:		1.17
      Nb Loops to get 80%:		5
  If Only FP Arithmetic:
      Potential Speedup:		1.18
      Nb Loops to get 80%:		8




+--------------------------------------------------------------------------------------------------------------------+
+                                             1.3  -  Potential Speedups                                             +
+--------------------------------------------------------------------------------------------------------------------+

  If No Scalar Integer:
      Number of loops   | 1      | 10     | 20     | 29     | 40     | 
      Cumulated Speedup | 1.0064 | 1.0133 | 1.0133 | 1.0133 | 1.0133 | 
  Top 5 loops:
    exec - 233:	1.0064
    exec - 78:	1.0088
    exec - 63:	1.0111
    exec - 120:	1.0122
    exec - 221:	1.0128

  If FP Vectorized:
      Number of loops   | 1      | 10     | 20     | 29     | 40     | 
      Cumulated Speedup | 1.0310 | 1.1197 | 1.1197 | 1.1197 | 1.1197 | 
  Top 5 loops:
    exec - 134:	1.031
    exec - 120:	1.0638
    exec - 212:	1.0921
    exec - 1141:	1.1075
    exec - 78:	1.1131

  If Fully Vectorized:
      Number of loops   | 1      | 10     | 20     | 29     | 40     | 
      Cumulated Speedup | 1.0329 | 1.1676 | 1.1691 | 1.1691 | 1.1691 | 
  Top 5 loops:
    exec - 120:	1.0329
    exec - 134:	1.0678
    exec - 212:	1.0964
    exec - 78:	1.1202
    exec - 63:	1.1446

  If Only FP Arithmetic:
      Number of loops   | 1      | 10     | 20     | 29     | 40     | 
      Cumulated Speedup | 1.0201 | 1.1647 | 1.1792 | 1.1796 | 1.1796 | 
  Top 5 loops:
    exec - 440:	1.0201
    exec - 416:	1.0407
    exec - 78:	1.0621
    exec - 63:	1.084
    exec - 420:	1.1068



+====================================================================================================================+
+                                                   2  -  SUMMARY                                                    +
+====================================================================================================================+


+--------------------------------------------------------------------------------------------------------------------+
+                                             2.1  -  EXPERIMENT QUALITY                                             +
+--------------------------------------------------------------------------------------------------------------------+

  [4 / 4] Application profile is long enough (52.41 s)
To have good quality measurements, it is advised that the application profiling time is greater than 10 seconds.

  [3 / 3] Most of time spent in analyzed modules comes from functions with source/debug info
-g option gives access to debugging informations, such are source locations.

  [2.9988391734397 / 3] Most of time spent in analyzed modules (99.96%) comes from functions compiled with architecture specialization option
-march=native


  [3 / 3] Most of time spent in analyzed modules comes from functions with compilation options informations and
-fno-omit-frame-pointer is present
-fno-omit-frame-pointer improves the accuracy of callchains found during the application profiling.

  [3 / 3] Optimization level option is correctly used


  [3 / 3] Host configuration allows retrieval of all necessary metrics.


  [2 / 2] Application is correctly profiled ("Others" category represents 0.02 % of the execution time)
To have a representative profiling, it is advised that the category "Others" represents less than 20% of the execution
time in order to analyze as much as possible of the user code

  [1 / 1] Lstopo present. The Topology lstopo report will be generated.



+--------------------------------------------------------------------------------------------------------------------+
+                                                2.2  -  CODE QUALITY                                                +
+--------------------------------------------------------------------------------------------------------------------+

  [4 / 4] Enough time of the experiment time spent in analyzed loops (95.87%)
If the time spent in analyzed loops is less than 30%, standard loop optimizations will have a limited impact on
application performances.

  [4 / 4] Threads activity is good
On average, more than 98.20% of observed threads are actually active 

  [4 / 4] CPU activity is good
CPU cores are active 99.95% of time

  [4 / 4] Loop profile is not flat
At least one loop coverage is greater than 4% (6.29%), representing an hotspot for the application

  [4 / 4] Enough time of the experiment time spent in analyzed innermost loops (95.62%)
If the time spent in analyzed innermost loops is less than 15%, standard innermost loop optimizations such as
vectorisation will have a limited impact on application performances.

  [4 / 4] Affinity is good (99.91%)
Threads are not migrating to CPU cores: probably successfully pinned

  [3 / 3] Less than 10% (0.00%) is spend in BLAS1 operations
It could be more efficient to inline by hand BLAS1 operations

  [3 / 3] Functions mostly use all threads
Functions running on a reduced number of threads (typically sequential code) cover less than 10% of application
walltime (3.07%)

  [3 / 3] Cumulative Outermost/In between loops coverage (0.25%) lower than cumulative innermost loop coverage (95.62%)
Having cumulative Outermost/In between loops coverage greater than cumulative innermost loop coverage will make loop
optimization more complex

  [2 / 2] Less than 10% (0.00%) is spend in BLAS2 operations
BLAS2 calls usually could make a poor cache usage and could benefit from inlining.

  [2 / 2] Less than 10% (0.00%) is spend in Libm/SVML (special functions)



+--------------------------------------------------------------------------------------------------------------------+
+                                               2.3  -  LOOPS OVERVIEW                                               +
+--------------------------------------------------------------------------------------------------------------------+

  Top 5 loops:
   + exec - 39  :
     analysis: Execution Time: 6 % - Vectorization Ratio: 100.00 % - Vector Length Use: 100.00 %
     Loop Computation Issues: 20
        [16] [SA] Presence of expensive FP instructions - Perform hoisting, change algorithm, use SVML or proper
            numerical library or perform value profiling (count the number of distinct input values). There are 4
            issues (= instructions) costing 4 points each.
        [4] [SA] Less than 10% of the FP ADD/SUB/MUL arithmetic operations are performed using FMA - Reorganize
            arithmetic expressions to exhibit potential for FMA. This issue costs 4 points.

   + exec - 304 :
     analysis: Execution Time: 5 % - Vectorization Ratio: 100.00 % - Vector Length Use: 100.00 %
     Loop Computation Issues: 12
        [8] [SA] Presence of expensive FP instructions - Perform hoisting, change algorithm, use SVML or proper
            numerical library or perform value profiling (count the number of distinct input values). There are 2
            issues (= instructions) costing 4 points each.
        [4] [SA] Less than 10% of the FP ADD/SUB/MUL arithmetic operations are performed using FMA - Reorganize
            arithmetic expressions to exhibit potential for FMA. This issue costs 4 points.

   + exec - 35  :
     analysis: Execution Time: 5 % - Vectorization Ratio: 100.00 % - Vector Length Use: 100.00 %
     Loop Computation Issues: 20
        [16] [SA] Presence of expensive FP instructions - Perform hoisting, change algorithm, use SVML or proper
            numerical library or perform value profiling (count the number of distinct input values). There are 4
            issues (= instructions) costing 4 points each.
        [4] [SA] Less than 10% of the FP ADD/SUB/MUL arithmetic operations are performed using FMA - Reorganize
            arithmetic expressions to exhibit potential for FMA. This issue costs 4 points.

   + exec - 51  :
     analysis: Execution Time: 5 % - Vectorization Ratio: 100.00 % - Vector Length Use: 100.00 %
     Loop Computation Issues: 8
        [4] [SA] Presence of expensive FP instructions - Perform hoisting, change algorithm, use SVML or proper
            numerical library or perform value profiling (count the number of distinct input values). There are 1
            issues (= instructions) costing 4 points each.
        [4] [SA] Less than 10% of the FP ADD/SUB/MUL arithmetic operations are performed using FMA - Reorganize
            arithmetic expressions to exhibit potential for FMA. This issue costs 4 points.

   + exec - 117 :
     analysis: Execution Time: 4 % - Vectorization Ratio: 100.00 % - Vector Length Use: 100.00 %
     Loop Computation Issues: 8
        [4] [SA] Presence of expensive FP instructions - Perform hoisting, change algorithm, use SVML or proper
            numerical library or perform value profiling (count the number of distinct input values). There are 1
            issues (= instructions) costing 4 points each.
        [4] [SA] Less than 10% of the FP ADD/SUB/MUL arithmetic operations are performed using FMA - Reorganize
            arithmetic expressions to exhibit potential for FMA. This issue costs 4 points.



+====================================================================================================================+
+                                                 3  -  APPLICATION                                                  +
+====================================================================================================================+


+--------------------------------------------------------------------------------------------------------------------+
+                                               3.1  -  Categorization                                               +
+--------------------------------------------------------------------------------------------------------------------+

   Category | IO     | Exe    | System  | Others  | Memory | String | MPI   | TBB   | OMP   | Pthread | Math  |
  ----------+--------+--------+---------+---------+--------+--------+-------+-------+-------+---------+-------+
   Time (%) | 0.00   | 96.06  | 0.77    | 0.02    | 0.00   | 0.00   | 0.03  | 0.00  | 3.11  | 0.00    | 0.00  |




+--------------------------------------------------------------------------------------------------------------------+
+                                          3.2  -  Function Based Profiling                                          +
+--------------------------------------------------------------------------------------------------------------------+

   Buckets                    | Nb Functions              | Coverage                  | Cumulated Coverage        |
  ----------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                       | 3                         | 67.41                     | 67.41                     |
   4% to 8%                   | 4                         | 19.88                     | 87.28                     |
   2% to 4%                   | 3                         | 8.09                      | 95.37                     |
   1% to 2%                   | 2                         | 3.03                      | 98.40                     |
   0.5% to 1%                 | 1                         | 0.65                      | 99.05                     |
   0.25% to 0.5%              | 1                         | 0.30                      | 99.35                     |
   0.125% to 0.25%            | 1                         | 0.22                      | 99.57                     |
   < 0.125%                   | 54                        | 0.38                      | 99.95                     |




+--------------------------------------------------------------------------------------------------------------------+
+                                            3.3  -  Loop Based Profiling                                            +
+--------------------------------------------------------------------------------------------------------------------+

   Buckets                    | Nb Loops                  | Coverage                  | Cumulated Coverage        |
  ----------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                       | 0                         | 0.00                      | 0.00                      |
   4% to 8%                   | 9                         | 43.34                     | 43.34                     |
   2% to 4%                   | 15                        | 43.83                     | 87.17                     |
   1% to 2%                   | 6                         | 8.04                      | 95.22                     |
   0.5% to 1%                 | 0                         | 0.00                      | 95.22                     |
   0.25% to 0.5%              | 0                         | 0.00                      | 95.22                     |
   0.125% to 0.25%            | 0                         | 0.00                      | 95.22                     |
   < 0.125%                   | 45                        | 0.41                      | 95.62                     |


+====================================================================================================================+
+                                                  4  -  FUNCTIONS                                                   +
+====================================================================================================================+


+--------------------------------------------------------------------------------------------------------------------+
+                                              4.1  -  Top 10 Functions                                              +
+--------------------------------------------------------------------------------------------------------------------+

   Function                                               | Module              | Coverage (%)   | Time (s)       |
  --------------------------------------------------------+---------------------+----------------+----------------+
   __nv_advec_mom_kernel_mod_advec_mom_kernel__PARALLE... | exec                | 36.62          | 18.96          |
   __nv_advec_cell_kernel_module_advec_cell_kernel__PA... | exec                | 19.09          | 9.89           |
   __nv_pdv_kernel_module_pdv_kernel__PARALLEL_F1L67_1    | exec                | 11.70          | 6.06           |
   __nv_ideal_gas_kernel_module_ideal_gas_kernel__PARA... | exec                | 5.41           | 2.80           |
   __nv_accelerate_kernel_module_accelerate_kernel__PA... | exec                | 5.19           | 2.69           |
   __nv_reset_field_kernel_module_reset_field_kernel__... | exec                | 5.12           | 2.65           |
   __nv_flux_calc_kernel_module_flux_calc_kernel__PARA... | exec                | 4.16           | 2.15           |
   __nv_calc_dt_kernel_module_calc_dt_kernel__PARALLEL... | exec                | 3.27           | 1.70           |
   __nv_revert_kernel_module_revert_kernel__PARALLEL_F... | exec                | 2.63           | 1.36           |
   __nv_viscosity_kernel_module_viscosity_kernel__PARA... | exec                | 2.19           | 1.14           |


+====================================================================================================================+
+                                                    5  -  LOOPS                                                     +
+====================================================================================================================+


+--------------------------------------------------------------------------------------------------------------------+
+                                                5.1  -  Top 10 Loops                                                +
+--------------------------------------------------------------------------------------------------------------------+

   Loop Id        | Module              | Source Location                                        | Coverage (%)   |
  ----------------+---------------------+--------------------------------------------------------+----------------+
   39             | exec                | PdV_kernel.f90-pp.f90:113-123,PdV_kernel.f90-pp.f90... | 6.29           |
   304            | exec                | ideal_gas_kernel.f90-pp.f90:50-55                      | 5.41           |
   35             | exec                | PdV_kernel.f90-pp.f90:77-87,PdV_kernel.f90-pp.f90:9... | 5.38           |
   51             | exec                | accelerate_kernel.f90-pp.f90:63-63,accelerate_kerne... | 5.17           |
   117            | exec                | advec_mom_kernel.f90-pp.f90:248-248                    | 4.27           |
   131            | exec                | advec_mom_kernel.f90-pp.f90:184-184                    | 4.26           |
   134            | exec                | advec_mom_kernel.f90-pp.f90:152-177                    | 4.21           |
   120            | exec                | advec_mom_kernel.f90-pp.f90:215-215,advec_mom_kerne... | 4.21           |
   233            | exec                | flux_calc_kernel.f90-pp.f90:57-61                      | 4.15           |
   63             | exec                | advec_cell_kernel.f90-pp.f90:202-204,advec_cell_ker... | 3.43           |





+====================================================================================================================+
+                                                     6  -  CQA                                                      +
+====================================================================================================================+


+--------------------------------------------------------------------------------------------------------------------+
+                                                   6.1  -  Loops                                                    +
+--------------------------------------------------------------------------------------------------------------------+





      6.1.1  -  Loop 39 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/build/build/CMakeFiles/clover_leaf.dir/CloverLeaf_ref/kernels/PdV_kernel.f90-pp.f90:113-123,129-135.

It is main loop of related source loop which is unrolled by 8 (including vectorization).

      6.1.1.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

34% of peak computational performance is used (8.22 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.1.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is fully vectorized, using full register length.


Details
All SSE/AVX instructions are used in vector version (process two or more data elements in vector registers).



      6.1.1.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by execution of divide and square root operations (the divide/square root unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 36.00 to 19.33 cycles (1.86x speedup).


Workaround
 - Reduce the number of division or square root instructions:
  * If denominator is constant over iterations, use reciprocal (replace x/y with x*(1/y)). Check precision impact.
 - Check whether you really need double precision. If not, switch to single precision to speedup execution





      6.1.1.1.3  -  Expensive FP math instructions/calls
  ----------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 36.00 to 20.00 cycles (1.80x speedup).


      6.1.1.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.1.1.5  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - VMOVUPD: 2 occurrences<<list_path_1_complex_1>>



      6.1.1.1.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

37 AVX-512 instructions are processing arithmetic or math operations on double precision FP elements in vector mode (eight at a time).



      6.1.1.1.7  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 296 FP arithmetical operations:
 - 144: addition or subtraction
 - 120: multiply
 - 32: divide
The binary loop is loading 1648 bytes (206 double precision FP elements).
The binary loop is storing 128 bytes (16 double precision FP elements).


      6.1.1.1.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.17 FP operations per loaded or stored byte.







      6.1.2  -  Loop 304 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/build/build/CMakeFiles/clover_leaf.dir/CloverLeaf_ref/kernels/ideal_gas_kernel.f90-pp.f90:50-55.

It is main loop of related source loop which is unrolled by 8 (including vectorization).

      6.1.2.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

12% of peak computational performance is used (2.88 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.2.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is fully vectorized, using full register length.


Details
All SSE/AVX instructions are used in vector version (process two or more data elements in vector registers).



      6.1.2.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by execution of divide and square root operations (the divide/square root unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 25.00 to 8.00 cycles (3.13x speedup).


Workaround
 - Reduce the number of division or square root instructions:
  * If denominator is constant over iterations, use reciprocal (replace x/y with x*(1/y)). Check precision impact.
 - Check whether you really need double precision. If not, switch to single precision to speedup execution





      6.1.2.1.3  -  Expensive FP math instructions/calls
  ----------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 25.00 to 6.00 cycles (4.17x speedup).


      6.1.2.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.2.1.5  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - VMOVUPD: 2 occurrences<<list_path_1_complex_1>>



      6.1.2.1.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

9 AVX-512 instructions are processing arithmetic or math operations on double precision FP elements in vector mode (eight at a time).



      6.1.2.1.7  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 72 FP arithmetical operations:
 - 8: addition or subtraction
 - 48: multiply
 - 8: divide
 - 8: square root
The binary loop is loading 128 bytes (16 double precision FP elements).
The binary loop is storing 128 bytes (16 double precision FP elements).


      6.1.2.1.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.28 FP operations per loaded or stored byte.







      6.1.3  -  Loop 35 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/build/build/CMakeFiles/clover_leaf.dir/CloverLeaf_ref/kernels/PdV_kernel.f90-pp.f90:77-87,93-99.

It is main loop of related source loop which is unrolled by 8 (including vectorization).

      6.1.3.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

34% of peak computational performance is used (8.22 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.3.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is fully vectorized, using full register length.


Details
All SSE/AVX instructions are used in vector version (process two or more data elements in vector registers).



      6.1.3.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by execution of divide and square root operations (the divide/square root unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 36.00 to 23.00 cycles (1.57x speedup).


Workaround
 - Reduce the number of division or square root instructions:
  * If denominator is constant over iterations, use reciprocal (replace x/y with x*(1/y)). Check precision impact.
 - Check whether you really need double precision. If not, switch to single precision to speedup execution





      6.1.3.1.3  -  Expensive FP math instructions/calls
  ----------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 36.00 to 19.00 cycles (1.89x speedup).


      6.1.3.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.3.1.5  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - VMOVUPD: 2 occurrences<<list_path_1_complex_1>>



      6.1.3.1.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

37 AVX-512 instructions are processing arithmetic or math operations on double precision FP elements in vector mode (eight at a time).



      6.1.3.1.7  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 296 FP arithmetical operations:
 - 112: addition or subtraction
 - 152: multiply
 - 32: divide
The binary loop is loading 1112 bytes (139 double precision FP elements).
The binary loop is storing 128 bytes (16 double precision FP elements).


      6.1.3.1.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.24 FP operations per loaded or stored byte.







      6.1.4  -  Loop 51 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/build/build/CMakeFiles/clover_leaf.dir/CloverLeaf_ref/kernels/accelerate_kernel.f90-pp.f90:63,69-75.

It is main loop of related source loop which is unrolled by 8 (including vectorization).

      6.1.4.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

64% of peak computational performance is used (15.58 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.4.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is fully vectorized, using full register length.


Details
All SSE/AVX instructions are used in vector version (process two or more data elements in vector registers).



      6.1.4.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by execution of FP add operations (the FP add unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 19.00 to 18.00 cycles (1.06x speedup).


Workaround
Reduce the number of FP add instructions




      6.1.4.1.3  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
Estimated speedup by perfect pairing: 1.06x.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.4.1.4  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - VMOVUPD: 2 occurrences<<list_path_1_complex_1>>



      6.1.4.1.5  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

37 AVX-512 instructions are processing arithmetic or math operations on double precision FP elements in vector mode (eight at a time).



      6.1.4.1.6  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 296 FP arithmetical operations:
 - 152: addition or subtraction
 - 136: multiply
 - 8: divide
The binary loop is loading 1448 bytes (181 double precision FP elements).
The binary loop is storing 128 bytes (16 double precision FP elements).


      6.1.4.1.7  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.19 FP operations per loaded or stored byte.







      6.1.5  -  Loop 117 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/build/build/CMakeFiles/clover_leaf.dir/CloverLeaf_ref/kernels/advec_mom_kernel.f90-pp.f90:248.

It is main loop of related source loop which is unrolled by 8 (including vectorization).

      6.1.5.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

14% of peak computational performance is used (3.56 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.5.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is fully vectorized, using full register length.


Details
All SSE/AVX instructions are used in vector version (process two or more data elements in vector registers).



      6.1.5.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by execution of divide and square root operations (the divide/square root unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 9.00 to 3.67 cycles (2.45x speedup).


Workaround
 - Reduce the number of division or square root instructions:
  * If denominator is constant over iterations, use reciprocal (replace x/y with x*(1/y)). Check precision impact.
 - Check whether you really need double precision. If not, switch to single precision to speedup execution





      6.1.5.1.3  -  Expensive FP math instructions/calls
  ----------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 9.00 to 3.00 cycles (3.00x speedup).


      6.1.5.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.5.1.5  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - VMOVUPD: 1 occurrences<<list_path_1_complex_1>>



      6.1.5.1.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

4 AVX-512 instructions are processing arithmetic or math operations on double precision FP elements in vector mode (eight at a time).



      6.1.5.1.7  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 32 FP arithmetical operations:
 - 16: addition or subtraction
 - 8: multiply
 - 8: divide
The binary loop is loading 320 bytes (40 double precision FP elements).
The binary loop is storing 64 bytes (8 double precision FP elements).


      6.1.5.1.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.08 FP operations per loaded or stored byte.







      6.1.6  -  Loop 131 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/build/build/CMakeFiles/clover_leaf.dir/CloverLeaf_ref/kernels/advec_mom_kernel.f90-pp.f90:184.

It is main loop of related source loop which is unrolled by 8 (including vectorization).

      6.1.6.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

14% of peak computational performance is used (3.56 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.6.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is fully vectorized, using full register length.


Details
All SSE/AVX instructions are used in vector version (process two or more data elements in vector registers).



      6.1.6.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by execution of divide and square root operations (the divide/square root unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 9.00 to 3.67 cycles (2.45x speedup).


Workaround
 - Reduce the number of division or square root instructions:
  * If denominator is constant over iterations, use reciprocal (replace x/y with x*(1/y)). Check precision impact.
 - Check whether you really need double precision. If not, switch to single precision to speedup execution





      6.1.6.1.3  -  Expensive FP math instructions/calls
  ----------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 9.00 to 3.00 cycles (3.00x speedup).


      6.1.6.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.6.1.5  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - VMOVUPD: 1 occurrences<<list_path_1_complex_1>>



      6.1.6.1.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

4 AVX-512 instructions are processing arithmetic or math operations on double precision FP elements in vector mode (eight at a time).



      6.1.6.1.7  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 32 FP arithmetical operations:
 - 16: addition or subtraction
 - 8: multiply
 - 8: divide
The binary loop is loading 320 bytes (40 double precision FP elements).
The binary loop is storing 64 bytes (8 double precision FP elements).


      6.1.6.1.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.08 FP operations per loaded or stored byte.







      6.1.7  -  Loop 134 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/build/build/CMakeFiles/clover_leaf.dir/CloverLeaf_ref/kernels/advec_mom_kernel.f90-pp.f90:152-177.

The related source loop is not unrolled or unrolled with no peel/tail loop.
This loop has 4 execution paths.

The presence of multiple execution paths is typically the main/first bottleneck.
Try to simplify control inside loop: ideally, try to remove all conditional expressions, for example by (if applicable):
 - hoisting them (moving them outside the loop)
 - turning them into conditional moves, MIN or MAX


Ex: if (x<0) x=0 => x = max(0,x) (Fortran instrinsic procedure)


      6.1.7.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

Warnings:
The number of fused uops of the instruction [CLTQ] is unknown
3% of peak computational performance is used (0.85 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.7.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is probably not vectorized.
Only 14% of vector register length is used (average across all SSE/AVX instructions).
By vectorizing your loop, you can lower the cost of an iteration from 20.00 to 5.00 cycles (4.00x speedup).

Details
Store and arithmetical SSE/AVX instructions are used in scalar version (process only one data element in vector registers).
Since your execution units are vector units, only a vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
Fortran storage order is column-major: do i do j a(i,j) = b(i,j) (slow, non stride 1) => do i do j a(j,i) = b(i,j) (fast, stride 1)<<image_col_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
do i a(i)%x = b(i)%x (slow, non stride 1) => do i a%x(i) = b%x(i) (fast, stride 1)



      6.1.7.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by execution of divide and square root operations (the divide/square root unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 20.00 to 10.17 cycles (1.97x speedup).


Workaround
 - Reduce the number of division or square root instructions:
  * If denominator is constant over iterations, use reciprocal (replace x/y with x*(1/y)). Check precision impact.
 - Check whether you really need double precision. If not, switch to single precision to speedup execution





      6.1.7.1.3  -  Expensive FP math instructions/calls
  ----------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 20.00 to 10.17 cycles (1.97x speedup).


      6.1.7.1.4  -  Masked instructions
  ----------------------------------------------------------------------------------------------------------

Detected masked instructions.

Details
Vector registers are partially exploited, which is expected if your loop is irregular or mixes elements of different sizes.

Workaround
If your loop is irregular, try to remove or hoist conditional structures out of your loop. If it mixes elements of different sizes, try to uniformize them.


      6.1.7.1.5  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.7.1.6  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - VUCOMISD: 2 occurrences<<list_path_1_complex_1>>



      6.1.7.1.7  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

Detected data structures (typically arrays) that cannot be efficiently read/written

Details
 - Constant unknown stride: 1 occurrence(s)
 - Irregular (variable stride) or indirect: 1 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)
 - Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.



      6.1.7.1.8  -  Conversion instructions
  ----------------------------------------------------------------------------------------------------------

Detected expensive conversion instructions.

Details
 - CLTQ: 1 occurrences<<list_path_1_cvt_1>>


Workaround
Avoid mixing data with different types. In particular, check if the type of constants is the same as array elements.


      6.1.7.1.9  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

24 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).



      6.1.7.1.10  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 17 FP arithmetical operations:
 - 7: addition or subtraction
 - 6: multiply
 - 4: divide
The binary loop is loading 96 bytes (12 double precision FP elements).
The binary loop is storing 8 bytes (1 double precision FP elements).


      6.1.7.1.11  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.16 FP operations per loaded or stored byte.




      6.1.7.2  -  Path 2
  ----------------------------------------------------------------------------------------------------------

Warnings:
The number of fused uops of the instruction [CLTQ] is unknown
6% of peak computational performance is used (1.55 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.7.2.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is probably not vectorized.
Only 13% of vector register length is used (average across all SSE/AVX instructions).
By vectorizing your loop, you can lower the cost of an iteration from 5.17 to 1.25 cycles (4.13x speedup).

Details
Store and arithmetical SSE/AVX instructions are used in scalar version (process only one data element in vector registers).
Since your execution units are vector units, only a vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
Fortran storage order is column-major: do i do j a(i,j) = b(i,j) (slow, non stride 1) => do i do j a(j,i) = b(i,j) (fast, stride 1)<<image_col_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
do i a(i)%x = b(i)%x (slow, non stride 1) => do i a%x(i) = b%x(i) (fast, stride 1)



      6.1.7.2.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Found no such bottlenecks but see expert reports for more complex bottlenecks.




      6.1.7.2.3  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
Estimated speedup by perfect pairing: 1.03x.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.7.2.4  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - VUCOMISD: 2 occurrences<<list_path_2_complex_1>>



      6.1.7.2.5  -  Conversion instructions
  ----------------------------------------------------------------------------------------------------------

Detected expensive conversion instructions.

Details
 - CLTQ: 1 occurrences<<list_path_2_cvt_1>>


Workaround
Avoid mixing data with different types. In particular, check if the type of constants is the same as array elements.


      6.1.7.2.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

10 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).



      6.1.7.2.7  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 8 FP arithmetical operations:
 - 4: addition or subtraction
 - 3: multiply
 - 1: divide
The binary loop is loading 48 bytes (6 double precision FP elements).
The binary loop is storing 8 bytes (1 double precision FP elements).


      6.1.7.2.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.14 FP operations per loaded or stored byte.




      6.1.7.3  -  Path 3
  ----------------------------------------------------------------------------------------------------------

Warnings:
The number of fused uops of the instruction [CLTQ] is unknown
3% of peak computational performance is used (0.85 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.7.3.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is probably not vectorized.
Only 14% of vector register length is used (average across all SSE/AVX instructions).
By vectorizing your loop, you can lower the cost of an iteration from 20.00 to 5.00 cycles (4.00x speedup).

Details
Store and arithmetical SSE/AVX instructions are used in scalar version (process only one data element in vector registers).
Since your execution units are vector units, only a vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
Fortran storage order is column-major: do i do j a(i,j) = b(i,j) (slow, non stride 1) => do i do j a(j,i) = b(i,j) (fast, stride 1)<<image_col_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
do i a(i)%x = b(i)%x (slow, non stride 1) => do i a%x(i) = b%x(i) (fast, stride 1)



      6.1.7.3.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by execution of divide and square root operations (the divide/square root unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 20.00 to 10.17 cycles (1.97x speedup).


Workaround
 - Reduce the number of division or square root instructions:
  * If denominator is constant over iterations, use reciprocal (replace x/y with x*(1/y)). Check precision impact.
 - Check whether you really need double precision. If not, switch to single precision to speedup execution





      6.1.7.3.3  -  Expensive FP math instructions/calls
  ----------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 20.00 to 10.17 cycles (1.97x speedup).


      6.1.7.3.4  -  Masked instructions
  ----------------------------------------------------------------------------------------------------------

Detected masked instructions.

Details
Vector registers are partially exploited, which is expected if your loop is irregular or mixes elements of different sizes.

Workaround
If your loop is irregular, try to remove or hoist conditional structures out of your loop. If it mixes elements of different sizes, try to uniformize them.


      6.1.7.3.5  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.7.3.6  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - LEA: 1 occurrences<<list_path_3_complex_1>>
 - VUCOMISD: 2 occurrences<<list_path_3_complex_2>>



      6.1.7.3.7  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

Detected data structures (typically arrays) that cannot be efficiently read/written

Details
 - Constant unknown stride: 3 occurrence(s)
 - Irregular (variable stride) or indirect: 1 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)
 - Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.



      6.1.7.3.8  -  Conversion instructions
  ----------------------------------------------------------------------------------------------------------

Detected expensive conversion instructions.

Details
 - CLTQ: 1 occurrences<<list_path_3_cvt_1>>


Workaround
Avoid mixing data with different types. In particular, check if the type of constants is the same as array elements.


      6.1.7.3.9  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

24 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).



      6.1.7.3.10  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 17 FP arithmetical operations:
 - 7: addition or subtraction
 - 6: multiply
 - 4: divide
The binary loop is loading 88 bytes (11 double precision FP elements).
The binary loop is storing 8 bytes (1 double precision FP elements).


      6.1.7.3.11  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.18 FP operations per loaded or stored byte.




      6.1.7.4  -  Path 4
  ----------------------------------------------------------------------------------------------------------

Warnings:
The number of fused uops of the instruction [CLTQ] is unknown
6% of peak computational performance is used (1.55 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.7.4.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is probably not vectorized.
Only 13% of vector register length is used (average across all SSE/AVX instructions).
By vectorizing your loop, you can lower the cost of an iteration from 5.17 to 1.25 cycles (4.13x speedup).

Details
Store and arithmetical SSE/AVX instructions are used in scalar version (process only one data element in vector registers).
Since your execution units are vector units, only a vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
Fortran storage order is column-major: do i do j a(i,j) = b(i,j) (slow, non stride 1) => do i do j a(j,i) = b(i,j) (fast, stride 1)<<image_col_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
do i a(i)%x = b(i)%x (slow, non stride 1) => do i a%x(i) = b%x(i) (fast, stride 1)



      6.1.7.4.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Found no such bottlenecks but see expert reports for more complex bottlenecks.




      6.1.7.4.3  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
Estimated speedup by perfect pairing: 1.03x.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.7.4.4  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - LEA: 1 occurrences<<list_path_4_complex_1>>
 - VUCOMISD: 2 occurrences<<list_path_4_complex_2>>



      6.1.7.4.5  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

Detected data structures (typically arrays) that cannot be efficiently read/written

Details
 - Constant unknown stride: 2 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)



      6.1.7.4.6  -  Conversion instructions
  ----------------------------------------------------------------------------------------------------------

Detected expensive conversion instructions.

Details
 - CLTQ: 1 occurrences<<list_path_4_cvt_1>>


Workaround
Avoid mixing data with different types. In particular, check if the type of constants is the same as array elements.


      6.1.7.4.7  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

10 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).



      6.1.7.4.8  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 8 FP arithmetical operations:
 - 4: addition or subtraction
 - 3: multiply
 - 1: divide
The binary loop is loading 40 bytes (5 double precision FP elements).
The binary loop is storing 8 bytes (1 double precision FP elements).


      6.1.7.4.9  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.17 FP operations per loaded or stored byte.







      6.1.8  -  Loop 120 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/build/build/CMakeFiles/clover_leaf.dir/CloverLeaf_ref/kernels/advec_mom_kernel.f90-pp.f90:215,227-241.

The related source loop is not unrolled or unrolled with no peel/tail loop.
The structure of this loop is probably <if then [else] end>.

The presence of multiple execution paths is typically the main/first bottleneck.
Try to simplify control inside loop: ideally, try to remove all conditional expressions, for example by (if applicable):
 - hoisting them (moving them outside the loop)
 - turning them into conditional moves, MIN or MAX


Ex: if (x<0) x=0 => x = max(0,x) (Fortran instrinsic procedure)


      6.1.8.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

3% of peak computational performance is used (0.85 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.8.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is probably not vectorized.
Only 14% of vector register length is used (average across all SSE/AVX instructions).
By vectorizing your loop, you can lower the cost of an iteration from 20.00 to 5.00 cycles (4.00x speedup).

Details
Store and arithmetical SSE/AVX instructions are used in scalar version (process only one data element in vector registers).
Since your execution units are vector units, only a vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
Fortran storage order is column-major: do i do j a(i,j) = b(i,j) (slow, non stride 1) => do i do j a(j,i) = b(i,j) (fast, stride 1)<<image_col_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
do i a(i)%x = b(i)%x (slow, non stride 1) => do i a%x(i) = b%x(i) (fast, stride 1)



      6.1.8.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by execution of divide and square root operations (the divide/square root unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 20.00 to 11.17 cycles (1.79x speedup).


Workaround
 - Reduce the number of division or square root instructions:
  * If denominator is constant over iterations, use reciprocal (replace x/y with x*(1/y)). Check precision impact.
 - Check whether you really need double precision. If not, switch to single precision to speedup execution





      6.1.8.1.3  -  Expensive FP math instructions/calls
  ----------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 20.00 to 11.17 cycles (1.79x speedup).


      6.1.8.1.4  -  Masked instructions
  ----------------------------------------------------------------------------------------------------------

Detected masked instructions.

Details
Vector registers are partially exploited, which is expected if your loop is irregular or mixes elements of different sizes.

Workaround
If your loop is irregular, try to remove or hoist conditional structures out of your loop. If it mixes elements of different sizes, try to uniformize them.


      6.1.8.1.5  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.8.1.6  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - VUCOMISD: 3 occurrences<<list_path_1_complex_1>>



      6.1.8.1.7  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

Detected data structures (typically arrays) that cannot be efficiently read/written

Details
 - Constant unknown stride: 1 occurrence(s)
 - Irregular (variable stride) or indirect: 3 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)
 - Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.



      6.1.8.1.8  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

25 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).



      6.1.8.1.9  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 17 FP arithmetical operations:
 - 7: addition or subtraction
 - 6: multiply
 - 4: divide
The binary loop is loading 108 bytes (13 double precision FP elements).
The binary loop is storing 8 bytes (1 double precision FP elements).


      6.1.8.1.10  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.15 FP operations per loaded or stored byte.




      6.1.8.2  -  Path 2
  ----------------------------------------------------------------------------------------------------------

5% of peak computational performance is used (1.41 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.8.2.1  -  Code clean check
  ----------------------------------------------------------------------------------------------------------

Detected a slowdown caused by scalar integer instructions (typically used for address computation).
By removing them, you can lower the cost of an iteration from 5.67 to 5.00 cycles (1.13x speedup).

Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)
 - To reference allocatable arrays, use "allocatable" instead of "pointer" pointers or qualify them with the "contiguous" attribute (Fortran 2008)
 - For structures, limit to one indirection. For example, use a_b%c instead of a%b%c with a_b set to a%b before this loop



      6.1.8.2.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is probably not vectorized.
Only 13% of vector register length is used (average across all SSE/AVX instructions).
By vectorizing your loop, you can lower the cost of an iteration from 5.67 to 1.25 cycles (4.53x speedup).

Details
Store and arithmetical SSE/AVX instructions are used in scalar version (process only one data element in vector registers).
Since your execution units are vector units, only a vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
Fortran storage order is column-major: do i do j a(i,j) = b(i,j) (slow, non stride 1) => do i do j a(j,i) = b(i,j) (fast, stride 1)<<image_col_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
do i a(i)%x = b(i)%x (slow, non stride 1) => do i a%x(i) = b%x(i) (fast, stride 1)



      6.1.8.2.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Found no such bottlenecks but see expert reports for more complex bottlenecks.




      6.1.8.2.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
Estimated speedup by perfect pairing: 1.10x.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.8.2.5  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - VUCOMISD: 2 occurrences<<list_path_2_complex_1>>



      6.1.8.2.6  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

Detected data structures (typically arrays) that cannot be efficiently read/written

Details
 - Irregular (variable stride) or indirect: 2 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.


      6.1.8.2.7  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

10 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).



      6.1.8.2.8  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 8 FP arithmetical operations:
 - 4: addition or subtraction
 - 3: multiply
 - 1: divide
The binary loop is loading 60 bytes (7 double precision FP elements).
The binary loop is storing 8 bytes (1 double precision FP elements).


      6.1.8.2.9  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.12 FP operations per loaded or stored byte.


      6.1.8.2.10  -  Unroll opportunity
  ----------------------------------------------------------------------------------------------------------

Loop is potentially data access bound.

Workaround
Unroll your loop if trip count is significantly higher than target unroll factor and if some data references are common to consecutive iterations. This can be done manually.







      6.1.9  -  Loop 233 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/build/build/CMakeFiles/clover_leaf.dir/CloverLeaf_ref/kernels/flux_calc_kernel.f90-pp.f90:57-61.

It is main loop of related source loop which is unrolled by 8 (including vectorization).

      6.1.9.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

38% of peak computational performance is used (9.23 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.9.1.1  -  Code clean check
  ----------------------------------------------------------------------------------------------------------

Detected a slowdown caused by scalar integer instructions (typically used for address computation).
By removing them, you can lower the cost of an iteration from 8.67 to 7.33 cycles (1.18x speedup).

Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)
 - To reference allocatable arrays, use "allocatable" instead of "pointer" pointers or qualify them with the "contiguous" attribute (Fortran 2008)
 - For structures, limit to one indirection. For example, use a_b%c instead of a%b%c with a_b set to a%b before this loop



      6.1.9.1.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is fully vectorized, using full register length.


Details
All SSE/AVX instructions are used in vector version (process two or more data elements in vector registers).



      6.1.9.1.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by:
 - reading data from caches/RAM (load units are a bottleneck)
 - writing data to caches/RAM (the store unit is a bottleneck)

By removing all these bottlenecks, you can lower the cost of an iteration from 8.67 to 6.00 cycles (1.44x speedup).


Workaround
 - Read less array elements
 - Write less array elements
 - Provide more information to your compiler:
  * hardcode the bounds of the corresponding 'for' loop





      6.1.9.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.9.1.5  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - VMOVUPD: 2 occurrences<<list_path_1_complex_1>>



      6.1.9.1.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

10 AVX-512 instructions are processing arithmetic or math operations on double precision FP elements in vector mode (eight at a time).



      6.1.9.1.7  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 80 FP arithmetical operations:
 - 48: addition or subtraction
 - 32: multiply
The binary loop is loading 672 bytes (84 double precision FP elements).
The binary loop is storing 128 bytes (16 double precision FP elements).


      6.1.9.1.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.10 FP operations per loaded or stored byte.







      6.1.10  -  Loop 63 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/build/build/CMakeFiles/clover_leaf.dir/CloverLeaf_ref/kernels/advec_cell_kernel.f90-pp.f90:202-204,210,216-246.

The related source loop is not unrolled or unrolled with no peel/tail loop.
Warnings:
 - Ignoring paths for analysis
 - Too many paths. Rerun with max-paths=8
 - RecMII not computed since number of paths is unknown or > max_paths
 - Streams not analyzed since number of paths is unknown or > max_paths

Try to simplify control and/or increase the maximum number of paths per function/loop through the 'max-paths-nb' option.

This loop has 8 execution paths.

The presence of multiple execution paths is typically the main/first bottleneck.
Try to simplify control inside loop: ideally, try to remove all conditional expressions, for example by (if applicable):
 - hoisting them (moving them outside the loop)
 - turning them into conditional moves, MIN or MAX


Ex: if (x<0) x=0 => x = max(0,x) (Fortran instrinsic procedure)


      6.1.10.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

14% of peak computational performance is used (3.45 out of 24.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.10.1.1  -  Code clean check
  ----------------------------------------------------------------------------------------------------------

Detected a slowdown caused by scalar integer instructions (typically used for address computation).
By removing them, you can lower the cost of an iteration from 33.67 to 31.50 cycles (1.07x speedup).

Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)
 - To reference allocatable arrays, use "allocatable" instead of "pointer" pointers or qualify them with the "contiguous" attribute (Fortran 2008)
 - For structures, limit to one indirection. For example, use a_b%c instead of a%b%c with a_b set to a%b before this loop



      6.1.10.1.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is partially vectorized.
Only 31% of vector register length is used (average across all SSE/AVX instructions).


Details
59% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 25% of SSE/AVX loads are used in vector version.
 - 61% of SSE/AVX instructions that are not load, store, addition, subtraction nor multiply instructions are used in vector version.


Workaround
Read the "512-bits vectorization" report at "Potential" confidence level.


      6.1.10.1.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Found no such bottlenecks but see expert reports for more complex bottlenecks.




      6.1.10.1.4  -  512-bits vectorization
  ----------------------------------------------------------------------------------------------------------

On some x86 processors supporting 512-bits vectorization, compilers are often too conservative and limit vectorization to 256 bits. Performance can then be improved by enforcing 512-bits vectorization, especially with many vectorized and high trip count loops. 512-bits vectorization performance overhead (compared to 256-bits) is generally lower on newer processors.



      6.1.10.1.5  -  Masked instructions
  ----------------------------------------------------------------------------------------------------------

Detected masked instructions.

Details
Vector registers are partially exploited, which is expected if your loop is irregular or mixes elements of different sizes.

Workaround
If your loop is irregular, try to remove or hoist conditional structures out of your loop. If it mixes elements of different sizes, try to uniformize them.


      6.1.10.1.6  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
Estimated speedup by perfect pairing: 1.06x.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).





      6.1.10.1.7  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - KORTESTB: 3 occurrences<<list_path_1_complex_1>>
 - VPCMPEQQ: 1 occurrences<<list_path_1_complex_2>>
 - VPEXTRQ: 13 occurrences<<list_path_1_complex_3>>



      6.1.10.1.8  -  Vector unaligned load/store instructions
  ----------------------------------------------------------------------------------------------------------

Detected 5 suboptimal vector unaligned load/store instructions.


Details
 - VINSERTF128: 5 occurrences<<list_path_1_vec_align_1>>


Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * Please look into your compiler manual for march=native or equivalent
 - Use vector aligned instructions:
  1) align your arrays on 64 bytes boundaries
  2) inform your compiler that your arrays are vector aligned: read your compiler manual.
<<image_vec_align>>


      6.1.10.1.9  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

44 AVX instructions are processing arithmetic or math operations on double precision FP elements in vector mode (four at a time).



      6.1.10.1.10  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 116 FP arithmetical operations:
 - 48: addition or subtraction
 - 56: multiply
 - 12: divide
The binary loop is loading 572 bytes (71 double precision FP elements).
The binary loop is storing 64 bytes (8 double precision FP elements).


      6.1.10.1.11  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.18 FP operations per loaded or stored byte.


      6.1.10.1.12  -  Unroll opportunity
  ----------------------------------------------------------------------------------------------------------

Loop is potentially data access bound.

Workaround
Unroll your loop if trip count is significantly higher than target unroll factor and if some data references are common to consecutive iterations. This can be done manually.





[MAQAO] Info: STOP THE REPORT GENERATION
[MAQAO] Info: 
[MAQAO] Info: If your application produces files, they can be found in directory "/beegfs/hackathon/users/eoseret/qaas_runs_test/179-069-4340/intel/CloverLeaf1.3-FC/run/oneview_runs/defaults/orig/oneview_run_1790694592"
[MAQAO] Info: 
