options

Stylizer

G++ O3 + Vectorize + Funroll + FfastmathACFL O3 + Vectorize + Funroll + Ffastmath

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

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

[ 3 / 3 ] Architecture specific option -mcpu is used

[ 0 / 3 ] Compilation of some functions is not optimized for the target processor

Architecture specific options are needed to produce efficient code for a specific processor ( -mcpu=native ).

[ 3 / 3 ] Most of time spent in analyzed modules comes from functions compiled with -g and -fno-omit-frame-pointer

-g option gives access to debugging informations, such are source locations. -fno-omit-frame-pointer improves the accuracy of callchains found during the application profiling.

[ 0 / 3 ] Most of time spent in analyzed modules comes from functions without compilation information

Functions without compilation information (typically not compiled with -g) cumulate 100.00% of the time spent in analyzed modules. Check that -g is present. Remark: if -g is indeed used, this can also be due to some compiler built-in functions (typically math) or statically linked libraries. This warning can be ignored in that case.

[ 4 / 4 ] Application profile is long enough (10.66 s)

To have good quality measurements, it is advised that the application profiling time is greater than 10 seconds.

[ 4 / 4 ] Application profile is long enough (10.67 s)

To have good quality measurements, it is advised that the application profiling time is greater than 10 seconds.

[ 2 / 2 ] Application is correctly profiled ("Others" category represents 0.00 % 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

[ 2 / 2 ] Application is correctly profiled ("Others" category represents 0.00 % 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

[ 3 / 3 ] Optimization level option is correctly used

[ 0 / 3 ] Some functions are compiled with a low optimization level (O0 or O1)

To have better performances, it is advised to help the compiler by using a proper optimization level (-O2 of higher). Warning, depending on compilers, faster optimization levels can decrease numeric accuracy.

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

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

Strategizer

G++ O3 + Vectorize + Funroll + FfastmathACFL O3 + Vectorize + Funroll + Ffastmath

[ 0 / 4 ] CPU activity is below 90% (15.07%)

CPU cores are idle more than 10% of time. Threads supposed to run on these cores are probably IO/sync waiting. Some hints: use faster filesystems to read/write data, improve parallel load balancing and/or scheduling.

[ 1 / 4 ] CPU activity is below 90% (49.97%)

CPU cores are idle more than 10% of time. Threads supposed to run on these cores are probably IO/sync waiting. Some hints: use faster filesystems to read/write data, improve parallel load balancing and/or scheduling.

[ 4 / 4 ] Affinity is good (96.17%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (98.10%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Enough time of the experiment time spent in analyzed loops (99.63%)

If the time spent in analyzed loops is less than 30%, standard loop optimizations will have a limited impact on application performances.

[ 4 / 4 ] Enough time of the experiment time spent in analyzed loops (30.44%)

If the time spent in analyzed loops is less than 30%, standard loop optimizations will have a limited impact on application performances.

[ 3 / 3 ] Cumulative Outermost/In between loops coverage (7.26%) lower than cumulative innermost loop coverage (92.38%)

Having cumulative Outermost/In between loops coverage greater than cumulative innermost loop coverage will make loop optimization more complex

[ 3 / 3 ] Cumulative Outermost/In between loops coverage (0.31%) lower than cumulative innermost loop coverage (30.13%)

Having cumulative Outermost/In between loops coverage greater than cumulative innermost loop coverage will make loop optimization more complex

[ 4 / 4 ] Threads activity is good

On average, more than 963.70% of observed threads are actually active

[ 4 / 4 ] Threads activity is good

On average, more than 3193.39% of observed threads are actually active

[ 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 BLAS2 operations

BLAS2 calls usually could make a poor cache usage and could benefit from inlining.

[ 4 / 4 ] Enough time of the experiment time spent in analyzed innermost loops (92.38%)

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 ] Enough time of the experiment time spent in analyzed innermost loops (30.13%)

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.

[ 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 ] Less than 10% (0.00%) is spend in BLAS1 operations

It could be more efficient to inline by hand BLAS1 operations

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

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

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (83.63%), representing an hotspot for the application

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (27.46%), representing an hotspot for the application

Optimizer

Analysisr0r1
Loop Computation IssuesLess than 10% of the FP ADD/SUB/MUL arithmetic operations are performed using FMA12
Presence of a large number of scalar integer instructions32
Control Flow IssuesPresence of 2 to 4 paths02
Presence of more than 4 paths20
Non-innermost loop11
Data Access IssuesPresence of constant non-unit stride data access10
Presence of indirect access11
Vectorization RoadblocksPresence of 2 to 4 paths02
Presence of more than 4 paths20
Non-innermost loop11
Presence of constant non-unit stride data access10
Presence of indirect access11
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