[ 4 / 4 ] Application profile is long enough (649.78 s)

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

[ 1.50 / 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.

[ 1.50 / 3 ] Most of time spent in analyzed modules comes from functions without compilation information

Functions without compilation information (typically not compiled with -g) cumulate 50.13% 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.

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

[ 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) ).

[ 2 / 2 ] Application is correctly profiled ("Others" category represents 14.31 % 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 / 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.

[ 4 / 4 ] Enough time of the experiment time spent in analyzed loops (50.68%)

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

[ 4 / 4 ] CPU activity is good

CPU cores are active 99.63% of time

[ 1 / 4 ] A significant amount of threads are idle (50.18%)

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 ] Affinity stability is lower than 90% (81.59%)

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.

[ 4 / 4 ] Loop profile is not flat

At least one loop coverage is greater than 4% (24.02%), representing an hotspot for the application

[ 4 / 4 ] Enough time of the experiment time spent in analyzed innermost loops (24.28%)

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 / 3 ] Cumulative Outermost/In between loops coverage (26.39%) greater than cumulative innermost loop coverage (24.28%)

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

[ 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 BLAS2 operations

BLAS2 calls usually could make a poor cache usage and could benefit from inlining.

[ 2 / 2 ] Less than 10% (0.26%) is spend in Libm/SVML (special functions)