[ 4 / 4 ] Application profile is long enough (14.75 s)

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

[ 0.03 / 3 ] Most of time spent in analyzed modules comes from functions without compilation information

Functions without compilation information (typically not compiled with -g) cumulate 98.99% 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 ] Optimization level option is correctly used

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

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

Application run on the GRANITE_RAPIDS micro-architecture while the code was specialized for GRANITERAPIDS. 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 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 (2.07%)

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 (55.05%)

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.

[ 4 / 4 ] CPU activity is good

CPU cores are active 95.11% of time

[ 0 / 4 ] Loop profile is flat

No hotspot found in the application (greatest loop coverage is 0.48%), and the twenty hottest loops cumulated coverage is lower than 20% of the application profiled time (2.00%)

[ 0 / 4 ] Too little time of the experiment time spent in analyzed innermost loops (1.88%)

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 ] Affinity is good (98.30%)

Threads are not migrating to CPU cores: probably successfully pinned

[ 3 / 3 ] Less than 10% (0.00%) is spend in BLAS1 operations

It could be more efficient to inline by hand BLAS1 operations

[ 0 / 3 ] Too many functions do not use all threads

Functions running on a reduced number of threads (typically sequential code) cover at least 10% of application walltime (16.08%). Check both "Max Inclusive Time Over Threads" and "Nb Threads" in Functions or Loops tabs and consider parallelizing sequential regions or improving parallelization of regions running on a reduced number of threads

[ 3 / 3 ] Cumulative Outermost/In between loops coverage (0.19%) lower than cumulative innermost loop coverage (1.88%)

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)