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

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

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

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

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

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

[ 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.

[ 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.

[ 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.

[ 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.

[ 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.

[ 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.

[ 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 ] 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 ] 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 ] 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 ] 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 ] 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.

[ 2 / 2 ] Application is correctly profiled ("Others" category represents 0.07 % 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 0.07 % 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 0.24 % 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 0.08 % 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 0.07 % 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 0.25 % 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

[ 3 / 3 ] Optimization level option is correctly used

[ 3 / 3 ] Optimization level option is correctly used

[ 3 / 3 ] Optimization level option is correctly used

[ 3 / 3 ] Optimization level option is correctly used

[ 3 / 3 ] Optimization level option is correctly used

[ 3.00 / 3 ] Most of time spent in analyzed modules (99.88%) comes from functions compiled with architecture specialization option -march=native

[ 3.00 / 3 ] Most of time spent in analyzed modules (99.90%) comes from functions compiled with architecture specialization option -march=native

[ 3.00 / 3 ] Most of time spent in analyzed modules (99.97%) comes from functions compiled with architecture specialization option -march=znver4

[ 3.00 / 3 ] Most of time spent in analyzed modules (99.92%) comes from functions compiled with architecture specialization option -march=znver4

[ 3.00 / 3 ] Most of time spent in analyzed modules (99.89%) comes from functions compiled with architecture specialization option -march=znver4

[ 3.00 / 3 ] Most of time spent in analyzed modules (99.94%) comes from functions compiled with architecture specialization option -march=znver4

[ 4 / 4 ] Application profile is long enough (20.57 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 (17.44 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 (16.17 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 (19.85 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 (19.20 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 (15.38 s)

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

[ 0 / 0 ] Fastmath not used

Consider to add ffast-math to compilation flags (or replace -O3 with -Ofast) to unlock potential extra speedup by relaxing floating-point computation consistency. Warning: floating-point accuracy may be reduced and the compliance to IEEE/ISO rules/specifications for math functions will be relaxed, typically 'errno' will no longer be set after calling some math functions.

Not available for this run

[ 0 / 0 ] Fastmath not used

Consider to add ffast-math to compilation flags (or replace -O3 with -Ofast) to unlock potential extra speedup by relaxing floating-point computation consistency. Warning: floating-point accuracy may be reduced and the compliance to IEEE/ISO rules/specifications for math functions will be relaxed, typically 'errno' will no longer be set after calling some math functions.

Not available for this run

Not available for this run

Not available for this run

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

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

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

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

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

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

[ 4 / 4 ] CPU activity is good

CPU cores are active 98.64% of time

[ 4 / 4 ] CPU activity is good

CPU cores are active 98.94% of time

[ 1 / 4 ] CPU activity is below 90% (33.97%)

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 ] CPU activity is good

CPU cores are active 98.60% of time

[ 4 / 4 ] CPU activity is good

CPU cores are active 99.22% of time

[ 1 / 4 ] CPU activity is below 90% (34.16%)

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 (99.18%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (99.16%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (97.20%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (99.15%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (99.27%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (97.05%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 0 / 3 ] Too many functions do not use all threads

Functions running on a reduced number of threads (typically sequential code) cover at least 10% of application walltime (93.35%). Check both "Max Inclusive Time Over Threads" and "Nb Threads" in Functions or Loops tabs and consider parallelizing sequential regions or improving parallelization of regions running on a reduced number of threads

[ 0 / 3 ] Too many functions do not use all threads

Functions running on a reduced number of threads (typically sequential code) cover at least 10% of application walltime (57.28%). Check both "Max Inclusive Time Over Threads" and "Nb Threads" in Functions or Loops tabs and consider parallelizing sequential regions or improving parallelization of regions running on a reduced number of threads

[ 0 / 3 ] Too many functions do not use all threads

Functions running on a reduced number of threads (typically sequential code) cover at least 10% of application walltime (57.98%). Check both "Max Inclusive Time Over Threads" and "Nb Threads" in Functions or Loops tabs and consider parallelizing sequential regions or improving parallelization of regions running on a reduced number of threads

[ 0 / 3 ] Too many functions do not use all threads

Functions running on a reduced number of threads (typically sequential code) cover at least 10% of application walltime (95.71%). Check both "Max Inclusive Time Over Threads" and "Nb Threads" in Functions or Loops tabs and consider parallelizing sequential regions or improving parallelization of regions running on a reduced number of threads

[ 0 / 3 ] Too many functions do not use all threads

Functions running on a reduced number of threads (typically sequential code) cover at least 10% of application walltime (56.61%). Check both "Max Inclusive Time Over Threads" and "Nb Threads" in Functions or Loops tabs and consider parallelizing sequential regions or improving parallelization of regions running on a reduced number of threads

[ 0 / 3 ] Too many functions do not use all threads

Functions running on a reduced number of threads (typically sequential code) cover at least 10% of application walltime (66.31%). Check both "Max Inclusive Time Over Threads" and "Nb Threads" in Functions or Loops tabs and consider parallelizing sequential regions or improving parallelization of regions running on a reduced number of threads

[ 3 / 3 ] Cumulative Outermost/In between loops coverage (0.27%) lower than cumulative innermost loop coverage (26.97%)

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 (0.57%) lower than cumulative innermost loop coverage (22.52%)

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 (5.31%) lower than cumulative innermost loop coverage (63.15%)

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 (0.27%) lower than cumulative innermost loop coverage (27.19%)

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 (0.28%) lower than cumulative innermost loop coverage (23.93%)

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 (2.20%) lower than cumulative innermost loop coverage (66.26%)

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

[ 3 / 4 ] A significant amount of threads are idle (16.99%)

On average, more than 10% of observed threads are idle. Such threads are probably IO/sync waiting. Some hints: use faster filesystems to read/write data, improve parallel load balancing and/or scheduling.

[ 3 / 4 ] A significant amount of threads are idle (13.35%)

On average, more than 10% of observed threads are idle. Such threads are probably IO/sync waiting. Some hints: use faster filesystems to read/write data, improve parallel load balancing and/or scheduling.

[ 1 / 4 ] A significant amount of threads are idle (66.65%)

On average, more than 10% of observed threads are idle. Such threads are probably IO/sync waiting. Some hints: use faster filesystems to read/write data, improve parallel load balancing and/or scheduling.

[ 3 / 4 ] A significant amount of threads are idle (17.69%)

On average, more than 10% of observed threads are idle. Such threads are probably IO/sync waiting. Some hints: use faster filesystems to read/write data, improve parallel load balancing and/or scheduling.

[ 3 / 4 ] A significant amount of threads are idle (11.62%)

On average, more than 10% of observed threads are idle. Such threads are probably IO/sync waiting. Some hints: use faster filesystems to read/write data, improve parallel load balancing and/or scheduling.

[ 1 / 4 ] A significant amount of threads are idle (66.52%)

On average, more than 10% of observed threads are idle. Such threads are probably IO/sync waiting. Some hints: use faster filesystems to read/write data, improve parallel load balancing and/or scheduling.

[ 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.

[ 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.

[ 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 (26.97%)

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 (22.52%)

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 (63.15%)

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 (27.19%)

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 (23.93%)

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 (66.26%)

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

[ 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

[ 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)

[ 2 / 2 ] Less than 10% (0.01%) is spend in Libm/SVML (special functions)

[ 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)

[ 2 / 2 ] Less than 10% (0.01%) is spend in Libm/SVML (special functions)

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (20.03%), representing an hotspot for the application

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (19.36%), representing an hotspot for the application

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (53.19%), representing an hotspot for the application

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (21.19%), representing an hotspot for the application

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (16.89%), representing an hotspot for the application

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (57.66%), representing an hotspot for the application

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (27.24%)

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

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (23.09%)

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 (68.46%)

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

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (27.46%)

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

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (24.21%)

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 (68.46%)

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