[ 0 / 4 ] Application profile is too short (1.63 s)

If the overall application profiling time is less than 10 seconds, many of the measurements at function or loop level will very likely be under the measurement quality threshold (0,1 seconds).
Rerun to increase runtime duration: for example use a larger dataset, include a repetition loop or change profile_start or profile_stop settings.

[ 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 ] 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 ( -march=(target) ).
Architecture specific options are needed to produce efficient code for a specific processor ( -x(target), -ax(target) or -march=(target)).

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

Functions without compilation options information cumulate 47.85% of the time spent in analyzed modules. For libdmumps.so, check that both -g and -grecord-gcc-switches are present. For libmumps_common.so, check that both -g and -grecord-gcc-switches are present. Remark: if such options are 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.

[ 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.

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

[ 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

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

[ 4 / 4 ] Enough time of the experiment time spent in analyzed loops (43.56%)

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

[ 0 / 4 ] A significant amount of threads are idle (77.20%)

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.

[ 0 / 4 ] CPU activity is below 90% (22.80%)

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 ] Loop profile is not flat

At least one loop coverage is greater than 4% (11.04%), representing an hotspot for the application

[ 4 / 4 ] Enough time of the experiment time spent in analyzed innermost loops (31.90%)

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 ] Affinity stability is lower than 90% (23.34%)

Threads are often migrating to other CPU cores/threads. For OpenMP, typically set (OMP_PLACES=cores OMP_PROC_BIND=close) or (OMP_PLACES=threads OMP_PROC_BIND=spread). With OpenMPI + OpenMP, use --bind-to core --map-by node:PE=$OMP_NUM_THREADS --report-bindings. With IntelMPI + OpenMP, set I_MPI_PIN_DOMAIN=omp:compact or I_MPI_PIN_DOMAIN=omp:scatter and use -print-rank-map.

[ 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 ] 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 (11.66%) lower than cumulative innermost loop coverage (31.90%)

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

[ 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 Libm/SVML (special functions)

[ 0 / 4 ] Application profile is too short (1.00 s)

If the overall application profiling time is less than 10 seconds, many of the measurements at function or loop level will very likely be under the measurement quality threshold (0,1 seconds).
Rerun to increase runtime duration: for example use a larger dataset, include a repetition loop or change profile_start or profile_stop settings.

[ 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 ] 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 ( -march=(target) ).
Architecture specific options are needed to produce efficient code for a specific processor ( -x(target), -ax(target) or -march=(target)).

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

Functions without compilation options information cumulate 37.34% of the time spent in analyzed modules. For libdmumps.so, check that both -g and -grecord-gcc-switches are present. For libmumps_common.so, check that both -g and -grecord-gcc-switches are present. Remark: if such options are 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.

[ 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.

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

[ 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

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

[ 4 / 4 ] Enough time of the experiment time spent in analyzed loops (31.58%)

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

[ 0 / 4 ] A significant amount of threads are idle (70.43%)

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.

[ 0 / 4 ] CPU activity is below 90% (14.91%)

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 ] Loop profile is not flat

At least one loop coverage is greater than 4% (7.27%), representing an hotspot for the application

[ 4 / 4 ] Enough time of the experiment time spent in analyzed innermost loops (23.06%)

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 ] Affinity stability is lower than 90% (15.48%)

Threads are often migrating to other CPU cores/threads. For OpenMP, typically set (OMP_PLACES=cores OMP_PROC_BIND=close) or (OMP_PLACES=threads OMP_PROC_BIND=spread). With OpenMPI + OpenMP, use --bind-to core --map-by node:PE=$OMP_NUM_THREADS --report-bindings. With IntelMPI + OpenMP, set I_MPI_PIN_DOMAIN=omp:compact or I_MPI_PIN_DOMAIN=omp:scatter and use -print-rank-map.

[ 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 ] 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 (8.52%) lower than cumulative innermost loop coverage (23.06%)

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

[ 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 Libm/SVML (special functions)

[ 0 / 4 ] Application profile is too short (0.65 s)

If the overall application profiling time is less than 10 seconds, many of the measurements at function or loop level will very likely be under the measurement quality threshold (0,1 seconds).
Rerun to increase runtime duration: for example use a larger dataset, include a repetition loop or change profile_start or profile_stop settings.

[ 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 ] 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 ( -x(target), -ax(target) or -march=(target)).

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

Functions without compilation options information cumulate 29.38% of the time spent in analyzed modules. For libdmumps.so, check that both -g and -grecord-gcc-switches are present. For libmumps_common.so, check that both -g and -grecord-gcc-switches are present. Remark: if such options are 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.

[ 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.

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

[ 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

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

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (26.65%)

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

[ 1 / 4 ] A significant amount of threads are idle (59.93%)

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.

[ 0 / 4 ] CPU activity is below 90% (10.17%)

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 ] Loop profile is not flat

At least one loop coverage is greater than 4% (4.47%), representing an hotspot for the application

[ 4 / 4 ] Enough time of the experiment time spent in analyzed innermost loops (20.04%)

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 ] Affinity stability is lower than 90% (10.77%)

Threads are often migrating to other CPU cores/threads. For OpenMP, typically set (OMP_PLACES=cores OMP_PROC_BIND=close) or (OMP_PLACES=threads OMP_PROC_BIND=spread). With OpenMPI + OpenMP, use --bind-to core --map-by node:PE=$OMP_NUM_THREADS --report-bindings. With IntelMPI + OpenMP, set I_MPI_PIN_DOMAIN=omp:compact or I_MPI_PIN_DOMAIN=omp:scatter and use -print-rank-map.

[ 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 ] 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 (6.61%) lower than cumulative innermost loop coverage (20.04%)

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

[ 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 Libm/SVML (special functions)

[ 0 / 4 ] Application profile is too short (0.43 s)

If the overall application profiling time is less than 10 seconds, many of the measurements at function or loop level will very likely be under the measurement quality threshold (0,1 seconds).
Rerun to increase runtime duration: for example use a larger dataset, include a repetition loop or change profile_start or profile_stop settings.

[ 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 ] 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 ( -march=(target) ).
Architecture specific options are needed to produce efficient code for a specific processor ( -x(target), -ax(target) or -march=(target)).

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

Functions without compilation options information cumulate 23.94% of the time spent in analyzed modules. For libdmumps.so, check that both -g and -grecord-gcc-switches are present. For libmumps_common.so, check that both -g and -grecord-gcc-switches are present. Remark: if such options are 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.

[ 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.

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

[ 2 / 2 ] Application is correctly profiled ("Others" category represents 0.15 % 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

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

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (19.82%)

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

[ 2 / 4 ] A significant amount of threads are idle (45.13%)

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.

[ 0 / 4 ] CPU activity is below 90% (6.98%)

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 ] Loop profile is not flat

At least one loop coverage is greater than 4% (4.55%), representing an hotspot for the application

[ 0 / 4 ] Too little time of the experiment time spent in analyzed innermost loops (14.54%)

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 ] Affinity stability is lower than 90% (7.60%)

Threads are often migrating to other CPU cores/threads. For OpenMP, typically set (OMP_PLACES=cores OMP_PROC_BIND=close) or (OMP_PLACES=threads OMP_PROC_BIND=spread). With OpenMPI + OpenMP, use --bind-to core --map-by node:PE=$OMP_NUM_THREADS --report-bindings. With IntelMPI + OpenMP, set I_MPI_PIN_DOMAIN=omp:compact or I_MPI_PIN_DOMAIN=omp:scatter and use -print-rank-map.

[ 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 ] 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 (5.29%) lower than cumulative innermost loop coverage (14.54%)

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

[ 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 Libm/SVML (special functions)

[ 0 / 4 ] Application profile is too short (0.37 s)

If the overall application profiling time is less than 10 seconds, many of the measurements at function or loop level will very likely be under the measurement quality threshold (0,1 seconds).
Rerun to increase runtime duration: for example use a larger dataset, include a repetition loop or change profile_start or profile_stop settings.

[ 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 ] 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 ( -march=(target) ).
Architecture specific options are needed to produce efficient code for a specific processor ( -x(target), -ax(target) or -march=(target)).

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

Functions without compilation options information cumulate 14.46% of the time spent in analyzed modules. For libdmumps.so, check that both -g and -grecord-gcc-switches are present. For libmumps_common.so, check that both -g and -grecord-gcc-switches are present. Remark: if such options are 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.

[ 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.

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

[ 2 / 2 ] Application is correctly profiled ("Others" category represents 0.43 % 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

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

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (12.03%)

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

[ 4 / 4 ] Threads activity is good

On average, more than 92.84% of observed threads are actually active

[ 0 / 4 ] CPU activity is below 90% (5.92%)

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.

[ 0 / 4 ] Loop profile is flat

No hotspot found in the application (greatest loop coverage is 2.60%), and the twenty hottest loops cumulated coverage is lower than 20% of the application profiled time (9.18%)

[ 0 / 4 ] Too little time of the experiment time spent in analyzed innermost loops (9.44%)

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 ] Affinity stability is lower than 90% (6.41%)

Threads are often migrating to other CPU cores/threads. For OpenMP, typically set (OMP_PLACES=cores OMP_PROC_BIND=close) or (OMP_PLACES=threads OMP_PROC_BIND=spread). With OpenMPI + OpenMP, use --bind-to core --map-by node:PE=$OMP_NUM_THREADS --report-bindings. With IntelMPI + OpenMP, set I_MPI_PIN_DOMAIN=omp:compact or I_MPI_PIN_DOMAIN=omp:scatter and use -print-rank-map.

[ 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 ] 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 (2.60%) lower than cumulative innermost loop coverage (9.44%)

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

[ 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 Libm/SVML (special functions)

[ 0 / 4 ] Application profile is too short (0.37 s)

If the overall application profiling time is less than 10 seconds, many of the measurements at function or loop level will very likely be under the measurement quality threshold (0,1 seconds).
Rerun to increase runtime duration: for example use a larger dataset, include a repetition loop or change profile_start or profile_stop settings.

[ 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 ] 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 ( -march=(target) ).
Architecture specific options are needed to produce efficient code for a specific processor ( -x(target), -ax(target) or -march=(target)).

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

Functions without compilation options information cumulate 7.65% of the time spent in analyzed modules. For libdmumps.so, check that both -g and -grecord-gcc-switches are present. For libmumps_common.so, check that both -g and -grecord-gcc-switches are present. Remark: if such options are 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.

[ 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.

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

[ 2 / 2 ] Application is correctly profiled ("Others" category represents 0.91 % 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

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

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (6.00%)

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

[ 4 / 4 ] Threads activity is good

On average, more than 176.15% of observed threads are actually active

[ 0 / 4 ] CPU activity is below 90% (5.62%)

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.

[ 0 / 4 ] Loop profile is flat

No hotspot found in the application (greatest loop coverage is 0.83%), and the twenty hottest loops cumulated coverage is lower than 20% of the application profiled time (4.56%)

[ 0 / 4 ] Too little time of the experiment time spent in analyzed innermost loops (4.52%)

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.

[ 0 / 4 ] Affinity stability is lower than 90% (5.99%)

Threads are often migrating to other CPU cores/threads. For OpenMP, typically set (OMP_PLACES=cores OMP_PROC_BIND=close) or (OMP_PLACES=threads OMP_PROC_BIND=spread). With OpenMPI + OpenMP, use --bind-to core --map-by node:PE=$OMP_NUM_THREADS --report-bindings. With IntelMPI + OpenMP, set I_MPI_PIN_DOMAIN=omp:compact or I_MPI_PIN_DOMAIN=omp:scatter and use -print-rank-map.

[ 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 ] 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 (1.48%) lower than cumulative innermost loop coverage (4.52%)

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

[ 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 Libm/SVML (special functions)

[ 0 / 4 ] Application profile is too short (0.43 s)

If the overall application profiling time is less than 10 seconds, many of the measurements at function or loop level will very likely be under the measurement quality threshold (0,1 seconds).
Rerun to increase runtime duration: for example use a larger dataset, include a repetition loop or change profile_start or profile_stop settings.

[ 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 ] 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 ( -march=(target) ).
Architecture specific options are needed to produce efficient code for a specific processor ( -x(target), -ax(target) or -march=(target)).

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

Functions without compilation options information cumulate 4.15% of the time spent in analyzed modules. For libdmumps.so, check that both -g and -grecord-gcc-switches are present. For libmumps_common.so, check that both -g and -grecord-gcc-switches are present. Remark: if such options are 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.

[ 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.

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

[ 2 / 2 ] Application is correctly profiled ("Others" category represents 1.14 % 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

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

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (3.16%)

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

[ 4 / 4 ] Threads activity is good

On average, more than 372.46% of observed threads are actually active

[ 0 / 4 ] CPU activity is below 90% (5.93%)

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.

[ 0 / 4 ] Loop profile is flat

No hotspot found in the application (greatest loop coverage is 0.42%), and the twenty hottest loops cumulated coverage is lower than 20% of the application profiled time (2.58%)

[ 0 / 4 ] Too little time of the experiment time spent in analyzed innermost loops (2.48%)

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.

[ 0 / 4 ] Affinity stability is lower than 90% (5.89%)

Threads are often migrating to other CPU cores/threads. For OpenMP, typically set (OMP_PLACES=cores OMP_PROC_BIND=close) or (OMP_PLACES=threads OMP_PROC_BIND=spread). With OpenMPI + OpenMP, use --bind-to core --map-by node:PE=$OMP_NUM_THREADS --report-bindings. With IntelMPI + OpenMP, set I_MPI_PIN_DOMAIN=omp:compact or I_MPI_PIN_DOMAIN=omp:scatter and use -print-rank-map.

[ 3 / 3 ] Less than 10% (0.02%) is spend in BLAS1 operations

It could be more efficient to inline by hand BLAS1 operations

[ 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 (0.68%) lower than cumulative innermost loop coverage (2.48%)

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

[ 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 Libm/SVML (special functions)

[ 0 / 4 ] Application profile is too short (0.62 s)

If the overall application profiling time is less than 10 seconds, many of the measurements at function or loop level will very likely be under the measurement quality threshold (0,1 seconds).
Rerun to increase runtime duration: for example use a larger dataset, include a repetition loop or change profile_start or profile_stop settings.

[ 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 ] 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 ( -march=(target) ).
Architecture specific options are needed to produce efficient code for a specific processor ( -x(target), -ax(target) or -march=(target)).

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

Functions without compilation options information cumulate 2.78% of the time spent in analyzed modules. For libdmumps.so, check that both -g and -grecord-gcc-switches are present. For libmumps_common.so, check that both -g and -grecord-gcc-switches are present. Remark: if such options are 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.

[ 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.

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

[ 2 / 2 ] Application is correctly profiled ("Others" category represents 0.41 % 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

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

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (1.99%)

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

[ 4 / 4 ] Threads activity is good

On average, more than 968.70% of observed threads are actually active

[ 0 / 4 ] CPU activity is below 90% (7.71%)

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.

[ 0 / 4 ] Loop profile is flat

No hotspot found in the application (greatest loop coverage is 0.52%), and the twenty hottest loops cumulated coverage is lower than 20% of the application profiled time (1.68%)

[ 0 / 4 ] Too little time of the experiment time spent in analyzed innermost loops (1.22%)

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 ] Affinity stability is lower than 90% (7.54%)

Threads are often migrating to other CPU cores/threads. For OpenMP, typically set (OMP_PLACES=cores OMP_PROC_BIND=close) or (OMP_PLACES=threads OMP_PROC_BIND=spread). With OpenMPI + OpenMP, use --bind-to core --map-by node:PE=$OMP_NUM_THREADS --report-bindings. With IntelMPI + OpenMP, set I_MPI_PIN_DOMAIN=omp:compact or I_MPI_PIN_DOMAIN=omp:scatter and use -print-rank-map.

[ 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 ] 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 (0.78%) lower than cumulative innermost loop coverage (1.22%)

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

[ 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 Libm/SVML (special functions)

[ 0 / 4 ] Application profile is too short (0.70 s)

If the overall application profiling time is less than 10 seconds, many of the measurements at function or loop level will very likely be under the measurement quality threshold (0,1 seconds).
Rerun to increase runtime duration: for example use a larger dataset, include a repetition loop or change profile_start or profile_stop settings.

[ 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 ] 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 ( -march=(target) ).
Architecture specific options are needed to produce efficient code for a specific processor ( -x(target), -ax(target) or -march=(target)).

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

Functions without compilation options information cumulate 2.69% of the time spent in analyzed modules. For libdmumps.so, check that both -g and -grecord-gcc-switches are present. For libmumps_common.so, check that both -g and -grecord-gcc-switches are present. Remark: if such options are 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.

[ 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.

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

[ 2 / 2 ] Application is correctly profiled ("Others" category represents 0.12 % 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

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

[ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (2.10%)

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

[ 4 / 4 ] Threads activity is good

On average, more than 1385.86% of observed threads are actually active

[ 0 / 4 ] CPU activity is below 90% (8.20%)

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.

[ 0 / 4 ] Loop profile is flat

No hotspot found in the application (greatest loop coverage is 0.92%), and the twenty hottest loops cumulated coverage is lower than 20% of the application profiled time (1.91%)

[ 0 / 4 ] Too little time of the experiment time spent in analyzed innermost loops (0.99%)

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 ] Affinity stability is lower than 90% (6.75%)

Threads are often migrating to other CPU cores/threads. For OpenMP, typically set (OMP_PLACES=cores OMP_PROC_BIND=close) or (OMP_PLACES=threads OMP_PROC_BIND=spread). With OpenMPI + OpenMP, use --bind-to core --map-by node:PE=$OMP_NUM_THREADS --report-bindings. With IntelMPI + OpenMP, set I_MPI_PIN_DOMAIN=omp:compact or I_MPI_PIN_DOMAIN=omp:scatter and use -print-rank-map.

[ 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 ] 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%)

[ 0 / 3 ] Cumulative Outermost/In between loops coverage (1.11%) greater than cumulative innermost loop coverage (0.99%)

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

[ 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 Libm/SVML (special functions)