[ 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.

[ 0 / 3 ] Architecture specific options are unknown for some functions (compilation options info are not available)

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

[ 0 / 3 ] Architecture specific options are unknown for some functions (compilation options info are not available)

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

[ 0 / 3 ] Architecture specific options are unknown for some functions (compilation options info are not available)

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

[ 0 / 3 ] Architecture specific options are unknown for some functions (compilation options info are not available)

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 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.

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

Functions without compilation options information cumulate 27.60% of the time spent in analyzed modules. For xy_model, NVHPC cannot record compilation options.

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

Functions without compilation options information cumulate 54.65% of the time spent in analyzed modules. For xy_model, NVHPC cannot record compilation options.

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

Functions without compilation options information cumulate 54.23% of the time spent in analyzed modules. For xy_model, NVHPC cannot record compilation options.

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

Functions without compilation options information cumulate 42.15% of the time spent in analyzed modules. For xy_model, NVHPC cannot record compilation options.

[ 0 / 3 ] Optimization level is unknown for some functions (compilation options info are not available)

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.

[ 0 / 3 ] Optimization level is unknown for some functions (compilation options info are not available)

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.

[ 0 / 3 ] Optimization level is unknown for some functions (compilation options info are not available)

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.

[ 0 / 3 ] Optimization level is unknown for some functions (compilation options info are not available)

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.

[ 2 / 2 ] Application is correctly profiled ("Others" category represents 1.80 % 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 15.32 % 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 15.49 % 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 17.14 % 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

[ 4 / 4 ] Application profile is long enough (910.48 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 (92.47 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 (92.45 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 (87.40 s)

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

[ 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 99.93% of time

[ 4 / 4 ] CPU activity is good

CPU cores are active 99.56% of time

[ 4 / 4 ] CPU activity is good

CPU cores are active 99.58% of time

[ 4 / 4 ] CPU activity is good

CPU cores are active 99.42% of time

[ 4 / 4 ] Affinity is good (99.99%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (99.91%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (99.88%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 4 / 4 ] Affinity is good (99.85%)

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 the application profiled time (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 the application profiled time (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 the application profiled time (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 the application profiled time (0.00%)

[ 3 / 3 ] Cumulative Outermost/In between loops coverage (3.34%) lower than cumulative innermost loop coverage (5.86%)

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

[ 0 / 3 ] Cumulative Outermost/In between loops coverage (49.74%) greater than cumulative innermost loop coverage (1.85%)

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

[ 0 / 3 ] Cumulative Outermost/In between loops coverage (48.41%) greater than cumulative innermost loop coverage (2.69%)

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

[ 0 / 3 ] Cumulative Outermost/In between loops coverage (35.73%) greater than cumulative innermost loop coverage (2.72%)

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 99.93% of observed threads are actually active

[ 4 / 4 ] Threads activity is good

On average, more than 99.56% of observed threads are actually active

[ 4 / 4 ] Threads activity is good

On average, more than 99.58% of observed threads are actually active

[ 4 / 4 ] Threads activity is good

On average, more than 99.42% 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 (5.86%)

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 (1.85%)

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 (2.69%)

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 (2.72%)

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

[ 0 / 2 ] More than 10% (70.54%) is spend in Libm/SVML (special functions)

The application is heavily using special math functions (powers, exp, sin etc…) proper library version have to be used. Exact accuracy needs have to be evaluated. Perform input value profiling, first count how many different input values. Recompile with -ffast-math or -Ofast to help/enable vectorization of loops calling math functions.

[ 0 / 2 ] More than 10% (29.47%) is spend in Libm/SVML (special functions)

The application is heavily using special math functions (powers, exp, sin etc…) proper library version have to be used. Exact accuracy needs have to be evaluated. Perform input value profiling, first count how many different input values. Recompile with -ffast-math or -Ofast to help/enable vectorization of loops calling math functions.

[ 0 / 2 ] More than 10% (29.71%) is spend in Libm/SVML (special functions)

The application is heavily using special math functions (powers, exp, sin etc…) proper library version have to be used. Exact accuracy needs have to be evaluated. Perform input value profiling, first count how many different input values. Recompile with -ffast-math or -Ofast to help/enable vectorization of loops calling math functions.

[ 0 / 2 ] More than 10% (40.00%) is spend in Libm/SVML (special functions)

The application is heavily using special math functions (powers, exp, sin etc…) proper library version have to be used. Exact accuracy needs have to be evaluated. Perform input value profiling, first count how many different input values. Recompile with -ffast-math or -Ofast to help/enable vectorization of loops calling math functions.

[ 0 / 4 ] Loop profile is flat

No hotspot found in the application (greatest loop coverage is 3.27%), and the twenty hottest loops cumulated coverage is lower than 20% of the application profiled time (9.19%)

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (27.17%), representing an hotspot for the application

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (30.01%), representing an hotspot for the application

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (20.35%), representing an hotspot for the application

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (9.19%)

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 (51.59%)

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 (51.10%)

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 (38.44%)

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