[ 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.

[ 3.00 / 3 ] Architecture specific option -march=native is used

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

-xHost or -march=native should not be used with Intel compilers while targeting (or compiling on) non-Intel processors, use -mavx2 Architecture specific options are needed to produce efficient code for a specific processor ( -x(target) or -ax(target) ).

[ 3.00 / 3 ] Architecture specific option -march=znver is used

[ 3.00 / 3 ] Architecture specific option -march=znver is used

[ 3.00 / 3 ] Architecture specific option -march=znver is used

[ 3.00 / 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.

[ 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.

[ 3.00 / 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.

[ 3.00 / 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.

[ 3.00 / 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 (123.65 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 (108.67 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 (146.62 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 (116.95 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 (151.03 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

[ 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

[ 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

[ 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

[ 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.

[ 3 / 4 ] CPU activity is below 90% (86.77%)

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.

[ 3 / 4 ] CPU activity is below 90% (74.75%)

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.

[ 3 / 4 ] CPU activity is below 90% (76.18%)

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.

[ 3 / 4 ] CPU activity is below 90% (86.27%)

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.

[ 3 / 4 ] CPU activity is below 90% (76.18%)

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 (98.85%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (97.78%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (98.59%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (98.75%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (98.76%)

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 ] 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 (1.53%) lower than cumulative innermost loop coverage (62.82%)

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 (19.55%) lower than cumulative innermost loop coverage (26.60%)

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 (1.81%) lower than cumulative innermost loop coverage (62.46%)

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 (1.69%) lower than cumulative innermost loop coverage (68.67%)

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 (3.07%) lower than cumulative innermost loop coverage (64.82%)

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 (13.43%)

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 (25.44%)

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 (23.92%)

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 (13.95%)

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 (23.91%)

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.

[ 4 / 4 ] Enough time of the experiment time spent in analyzed innermost loops (62.82%)

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 (26.60%)

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 (62.46%)

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 (68.67%)

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 (64.82%)

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

[ 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% (0.00%) 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% (0.00%) is spend in Libm/SVML (special functions)

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (18.89%), representing an hotspot for the application

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (10.92%), representing an hotspot for the application

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (16.07%), representing an hotspot for the application

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (20.26%), representing an hotspot for the application

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (17.31%), representing an hotspot for the application

[ 4 / 4 ] Enough time of the experiment time spent in analyzed loops (64.35%)

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 (46.15%)

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 (64.27%)

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 (70.36%)

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 (67.89%)

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