[ 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.

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

[ 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 ( -x(target) or -ax(target) ).

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

-march=x86-64 option is used but it is not specific enough to produce efficient code. Architecture specific options are needed to produce efficient code for a specific processor ( -x(target) or -ax(target) ).

[ 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 ( -x(target) or -ax(target) ).

[ 2.79 / 3 ] Architecture specific option -march=znver is used

[ 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.

[ 2.82 / 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 improve 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.

[ 2.79 / 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 improve the accuracy of callchains found during the application profiling.

[ 4 / 4 ] Application profile is long enough (41.90 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 (37.48 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 (42.33 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 (38.89 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 5.50 % 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 0.04 % 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 5.36 % 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 0.04 % 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

[ 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.

[ 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.

[ 3 / 3 ] Optimization level option is correctly used

[ 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 94.05% of time

[ 4 / 4 ] CPU activity is good

CPU cores are active 96.72% of time

[ 4 / 4 ] CPU activity is good

CPU cores are active 93.95% of time

[ 4 / 4 ] CPU activity is good

CPU cores are active 97.23% of time

[ 4 / 4 ] Affinity is good (91.47%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (98.85%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (91.03%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (96.83%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 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 (0.00%)

[ 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 (0.00%)

[ 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 (0.00%)

[ 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 (0.00%)

[ 3 / 3 ] Cumulative Outermost/In between loops coverage (0.03%) lower than cumulative innermost loop coverage (2.94%)

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 (0.00%) lower than cumulative innermost loop coverage (3.61%)

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 (0.00%) lower than cumulative innermost loop coverage (2.65%)

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 (0.04%) lower than cumulative innermost loop coverage (3.48%)

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 93.46% of observed threads are actually active

[ 4 / 4 ] Threads activity is good

On average, more than 96.50% of observed threads are actually active

[ 4 / 4 ] Threads activity is good

On average, more than 93.40% of observed threads are actually active

[ 4 / 4 ] Threads activity is good

On average, more than 96.98% 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.

[ 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.

[ 0 / 4 ] Too little time of the experiment time spent in analyzed innermost loops (2.94%)

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.

[ 0 / 4 ] Too little time of the experiment time spent in analyzed innermost loops (3.61%)

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.

[ 0 / 4 ] Too little time of the experiment time spent in analyzed innermost loops (2.65%)

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.

[ 0 / 4 ] Too little time of the experiment time spent in analyzed innermost loops (3.48%)

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

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

[ 2 / 2 ] Less than 10% (0.04%) 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% (0.06%) is spend in Libm/SVML (special functions)

[ 0 / 4 ] Loop profile is flat

No hotspot found in the application (greatest loop coverage is 1.52%), and the twenty hottest loops cumulated coverage is lower than 20% of the application profiled time (2.96%)

[ 0 / 4 ] Loop profile is flat

No hotspot found in the application (greatest loop coverage is 1.54%), and the twenty hottest loops cumulated coverage is lower than 20% of the application profiled time (3.61%)

[ 0 / 4 ] Loop profile is flat

No hotspot found in the application (greatest loop coverage is 1.50%), and the twenty hottest loops cumulated coverage is lower than 20% of the application profiled time (2.65%)

[ 0 / 4 ] Loop profile is flat

No hotspot found in the application (greatest loop coverage is 1.47%), and the twenty hottest loops cumulated coverage is lower than 20% of the application profiled time (3.52%)

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (2.96%)

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 (3.61%)

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 (2.65%)

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 (3.52%)

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