	*************************************************
	*                                               *
	*          ONE-View report generation           *
	*                                               *
	*************************************************

[MAQAO] Info: Experiment configuration summary is available adding -dbg=1 in command line

* [MAQAO] Warning: Experiment directory /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/run/oneview_runs/compilers/icx_5/oneview_results_1786695111 already exists and is reused.
           It can be replaced using --replace in the command line.
[MAQAO] Info: 
[MAQAO] Info: START THE APPLICATION PROFILING
[MAQAO] Info: -> RUNNING THE PROFILER...
[MAQAO] Info:   LPROF has already been run
[MAQAO] Info: STOP THE APPLICATION PROFILING
[MAQAO] Info: 
[MAQAO] Info: START FUNCTIONS AND LOOPS ANALYSIS ...
[MAQAO] Info: STOP FUNCTIONS AND LOOPS ANALYSIS ...
[MAQAO] Info: 
[MAQAO] Info: START THE REPORT GENERATION
[MAQAO] Info: -> ONE-VIEW EXPERIMENT DIRECTORY: /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/run/oneview_runs/compilers/icx_5/oneview_results_1786695111


+====================================================================================================================+
+                                                    1  -  GLOBAL                                                    +
+====================================================================================================================+


+--------------------------------------------------------------------------------------------------------------------+
+                                             1.1  -  Experiment Summary                                             +
+--------------------------------------------------------------------------------------------------------------------+

  Application:			/beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/run/binaries/icx_5/exec
  Timestamp:			2026-08-14 10:11:51
  Universal Timestamp:		1786695111
  Experiment Type:		MPI; OpenMP; Throughput; 
  Machine:			gmz12.benchmarkcenter.megware.com
  Architecture:			x86_64
  Micro Architecture:		ZEN_V5
  Model Name:			AMD EPYC 9655 96-Core Processor
  Cache Size:			1024 KB
  Number of Cores:		96
  OS Version:			Linux 5.14.0-687.29.1.el9_8.x86_64 #1 SMP PREEMPT_DYNAMIC Thu Jul 23 16:18:48 EDT 2026
  Compilation Options:		
		exec:  --driver-mode=g++ --intel -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/src -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/icx_5/kripke-build/include -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/tpl/raja/include -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/icx_5/kripke-build/raja/include -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/tpl/raja/tpl/camp/include -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/icx_5/kripke-build/raja/tpl/camp/include -O3 -O3 -march=znver5 -fno-vectorize -fno-slp-vectorize -fno-iopenmp-simd -g -fno-omit-frame-pointer -fcf-protection=none -no-pie -grecord-command-line -O3 -D NDEBUG -std=c++17 -fPIC -fiopenmp -MD -MT CMakeFiles/kripke.dir/beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/src/Kripke/Kernel/Scattering.cpp.o -MF CMakeFiles/kripke.dir/beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/src/Kripke/Kernel/Scattering.cpp.o.d -o CMakeFiles/kripke.dir/beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/src/Kripke/Kernel/Scattering.cpp.o -c /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/src/Kripke/Kernel/Scattering.cpp -I /cluster/hpcx/2.22/ompi5-ifx-mt/include -I /cluster/hpcx/2.22/ompi5-ifx-mt/include/openmpi -fveclib=SVML 
  Number of processes observed:	1
  Number of threads observed:	191
  MAQAO version:		2026.1.0
  MAQAO build:			6d1be1d51c1e63266254997eb301734a7264775d::20260810-150026




+--------------------------------------------------------------------------------------------------------------------+
+                                               1.2  -  Global Metrics                                               +
+--------------------------------------------------------------------------------------------------------------------+

  Total Time:				51.73 s
  Max (Thread Active Time):		51.33 s
  Average Active Time:			51.09 s
  Activity Ratio:			99.5 %
  Average number of active threads:	188.605
  Affinity Stability:			99.8 %
  Time spent in analyzed loops:		55.9 %
  Time spent in analyzed innermost loops: 35.9 %
  Time spent in user code:		77.8 %
  Compilation Options Score:		100
  Array Access Efficiency:		97.1 %

   Potential Speedups
  ----------------------------------------------------
  Perfect Flow Complexity:		1.00
  Perfect OpenMP/MPI/Pthread/TBB:	1.05
  Perfect OpenMP/MPI/Pthread/TBB + Load Distribution:	1.28
  If No Scalar Integer:
      Potential Speedup:		1.22
      Nb Loops to get 80%:		1
  If FP Vectorized:
      Potential Speedup:		1.20
      Nb Loops to get 80%:		2
  If Fully Vectorized:
      Potential Speedup:		1.96
      Nb Loops to get 80%:		3
  If Only FP Arithmetic:
      Potential Speedup:		1.43
      Nb Loops to get 80%:		3




+--------------------------------------------------------------------------------------------------------------------+
+                                             1.3  -  Potential Speedups                                             +
+--------------------------------------------------------------------------------------------------------------------+

  If No Scalar Integer:
      Number of loops   | 1      | 1      | 3      | 3      | 5      | 
      Cumulated Speedup | 1.1874 | 1.1874 | 1.2248 | 1.2248 | 1.2248 | 
  Top 5 loops:
    exec - 1174:	1.1874
    exec - 814:	1.2085
    exec - 948:	1.2248
    exec - 1355:	1.2248
    exec - 1089:	1.2248

  If FP Vectorized:
      Number of loops   | 1      | 1      | 3      | 3      | 5      | 
      Cumulated Speedup | 1.1479 | 1.1479 | 1.1830 | 1.1830 | 1.2007 | 
  Top 5 loops:
    exec - 1174:	1.1479
    exec - 814:	1.1652
    exec - 1355:	1.183
    exec - 948:	1.1966
    exec - 1089:	1.2007

  If Fully Vectorized:
      Number of loops   | 1      | 1      | 3      | 3      | 5      | 
      Cumulated Speedup | 1.2120 | 1.2120 | 1.8794 | 1.8794 | 1.9565 | 
  Top 5 loops:
    exec - 1174:	1.212
    exec - 814:	1.5212
    exec - 948:	1.8794
    exec - 1355:	1.9392
    exec - 1089:	1.9565

  If Only FP Arithmetic:
      Number of loops   | 1      | 1      | 3      | 3      | 5      | 
      Cumulated Speedup | 1.2075 | 1.2075 | 1.4299 | 1.4299 | 1.4299 | 
  Top 5 loops:
    exec - 1174:	1.2075
    exec - 814:	1.3255
    exec - 948:	1.4299
    exec - 1355:	1.4299
    exec - 1089:	1.4299



+====================================================================================================================+
+                                                   2  -  SUMMARY                                                    +
+====================================================================================================================+


+--------------------------------------------------------------------------------------------------------------------+
+                                             2.1  -  EXPERIMENT QUALITY                                             +
+--------------------------------------------------------------------------------------------------------------------+

  [4 / 4] Application profile is long enough (51.33 s)
To have good quality measurements, it is advised that the application profiling time is greater than 10 seconds.

  [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.

  [2.9994508342217 / 3] Most of time spent in analyzed modules (99.98%) comes from functions compiled with architecture specialization option
-march=znver5


  [3 / 3] Most of time spent in analyzed modules comes from functions with compilation options informations and
-fno-omit-frame-pointer is present
-fno-omit-frame-pointer improves the accuracy of callchains found during the application profiling.

  [3 / 3] Optimization level option is correctly used


  [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.



+--------------------------------------------------------------------------------------------------------------------+
+                                                2.2  -  CODE QUALITY                                                +
+--------------------------------------------------------------------------------------------------------------------+

  [4 / 4] Enough time of the experiment time spent in analyzed loops (55.88%)
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 98.75% of observed threads are actually active 

  [4 / 4] CPU activity is good
CPU cores are active 99.48% of time

  [4 / 4] Loop profile is not flat
At least one loop coverage is greater than 4% (19.99%), representing an hotspot for the application

  [4 / 4] Enough time of the experiment time spent in analyzed innermost loops (35.89%)
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 (99.81%)
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

  [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 (3.18%)

  [3 / 3] Cumulative Outermost/In between loops coverage (20.00%) lower than cumulative innermost loop coverage (35.89%)
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)



+--------------------------------------------------------------------------------------------------------------------+
+                                               2.3  -  LOOPS OVERVIEW                                               +
+--------------------------------------------------------------------------------------------------------------------+

  Top 5 loops:
   + exec - 1174:
     analysis: Execution Time: 19 % - Vectorization Ratio: 14.29 % - Vector Length Use: 14.29 %
     Loop Computation Issues: 2
        [2] [SA] Presence of a large number of scalar integer instructions - Simplify loop structure, perform loop
            splitting or perform unroll and jam. This issue costs 2 points.
     Control Flow Issues: 2
        [2] [SA] Non innermost loop (Outermost) - Collapse loop with innermost ones. This issue costs 2 points.
     Data Access Issues: 2
        [2] [SA] More than 20% of the loads are accessing the stack - Perform loop splitting to decrease pressure on
            registers. This issue costs 2 points.
     Vectorization Roadblocks: 1002
        [1000] [SA] Too many paths (at least 1000 paths) - Simplify control structure. There are at least 1000 issues ( =
            paths) costing 1 point.
        [2] [SA] Non innermost loop (Outermost) - Collapse loop with innermost ones. This issue costs 2 points.

   + exec - 814 :
     analysis: Execution Time: 19 % - Vectorization Ratio: 0.00 % - Vector Length Use: 12.50 %

   + exec - 948 :
     analysis: Execution Time: 14 % - Vectorization Ratio: 0.00 % - Vector Length Use: 12.50 %

   + exec - 1355:
     analysis: Execution Time: 1 % - Vectorization Ratio: 0.00 % - Vector Length Use: 12.50 %
     Loop Computation Issues: 32
        [32] [SA] Presence of expensive FP instructions - Perform hoisting, change algorithm, use SVML or proper
            numerical library or perform value profiling (count the number of distinct input values). There are 8
            issues (= instructions) costing 4 points each.
     Data Access Issues: 46
        [32] [SA] Presence of constant non unit stride data access - Use array restructuring, perform loop interchange
            or use gather instructions to lower a bit the cost. There are 16 issues ( = data accesses) costing 2
            point each.
        [12] [SA] Presence of indirect accesses - Use array restructuring or gather instructions to lower the cost.
            There are 3 issues ( = indirect data accesses) costing 4 point each.
        [2] [SA] More than 20% of the loads are accessing the stack - Perform loop splitting to decrease pressure on
            registers. This issue costs 2 points.
     Vectorization Roadblocks: 44
        [32] [SA] Presence of constant non unit stride data access - Use array restructuring, perform loop interchange
            or use gather instructions to lower a bit the cost. There are 16 issues ( = data accesses) costing 2
            point each.
        [12] [SA] Presence of indirect accesses - Use array restructuring or gather instructions to lower the cost.
            There are 3 issues ( = indirect data accesses) costing 4 point each.

   + exec - 1089:
     analysis: Execution Time: 0 % - Vectorization Ratio: 0.00 % - Vector Length Use: 12.50 %



+====================================================================================================================+
+                                                 3  -  APPLICATION                                                  +
+====================================================================================================================+


+--------------------------------------------------------------------------------------------------------------------+
+                                               3.1  -  Categorization                                               +
+--------------------------------------------------------------------------------------------------------------------+

   Category | IO     | Exe    | System  | Others  | Memory | String | MPI   | TBB   | OMP   | Pthread | Math  |
  ----------+--------+--------+---------+---------+--------+--------+-------+-------+-------+---------+-------+
   Time (%) | 0.00   | 77.81  | 0.87    | 0.00    | 0.01   | 0.00   | 0.00  | 0.00  | 21.30 | 0.00    | 0.00  |




+--------------------------------------------------------------------------------------------------------------------+
+                                          3.2  -  Function Based Profiling                                          +
+--------------------------------------------------------------------------------------------------------------------+

   Buckets                    | Nb Functions              | Coverage                  | Cumulated Coverage        |
  ----------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                       | 4                         | 96.12                     | 96.12                     |
   4% to 8%                   | 0                         | 0.00                      | 96.12                     |
   2% to 4%                   | 0                         | 0.00                      | 96.12                     |
   1% to 2%                   | 1                         | 1.89                      | 98.01                     |
   0.5% to 1%                 | 3                         | 1.85                      | 99.86                     |
   0.25% to 0.5%              | 0                         | 0.00                      | 99.86                     |
   0.125% to 0.25%            | 0                         | 0.00                      | 99.86                     |
   < 0.125%                   | 9                         | 0.13                      | 99.99                     |




+--------------------------------------------------------------------------------------------------------------------+
+                                            3.3  -  Loop Based Profiling                                            +
+--------------------------------------------------------------------------------------------------------------------+

   Buckets                    | Nb Loops                  | Coverage                  | Cumulated Coverage        |
  ----------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                       | 2                         | 33.49                     | 33.49                     |
   4% to 8%                   | 0                         | 0.00                      | 33.49                     |
   2% to 4%                   | 0                         | 0.00                      | 33.49                     |
   1% to 2%                   | 1                         | 1.88                      | 35.36                     |
   0.5% to 1%                 | 1                         | 0.52                      | 35.88                     |
   0.25% to 0.5%              | 0                         | 0.00                      | 35.88                     |
   0.125% to 0.25%            | 0                         | 0.00                      | 35.88                     |
   < 0.125%                   | 2                         | 0.00                      | 35.89                     |


+====================================================================================================================+
+                                                  4  -  FUNCTIONS                                                   +
+====================================================================================================================+


+--------------------------------------------------------------------------------------------------------------------+
+                                              4.1  -  Top 10 Functions                                              +
+--------------------------------------------------------------------------------------------------------------------+

   Function                                               | Module              | Coverage (%)   | Time (s)       |
  --------------------------------------------------------+---------------------+----------------+----------------+
   void Kripke::DispatchHelper<Kripke::ArchT_OpenMP>::... | exec                | 41.89          | 21.40          |
   kmp_flag_64<false, true>::wait(kmp_info*, int, void*)  | libiomp5.so         | 20.72          | 10.59          |
   void LPlusTimesSdom::operator()<Kripke::ArchLayoutT... | exec                | 19.18          | 9.80           |
   void Kripke::DispatchHelper<Kripke::ArchT_OpenMP>::... | exec                | 14.33          | 7.32           |
   void Kripke::DispatchHelper<Kripke::ArchT_OpenMP>::... | exec                | 1.89           | 0.96           |
   unknown_kernel_region                                  | kernel              | 0.79           | 0.40           |
   kmp_flag_native<unsigned long long, (flag_type)1, t... | libiomp5.so         | 0.54           | 0.27           |
   void PopulationSdom::operator()<Kripke::ArchLayoutT... | exec                | 0.52           | 0.27           |
   __GI___sched_yield                                     | libc.so.6           | 0.09           | 0.05           |
   __kmp_yield                                            | libiomp5.so         | 0.02           | 0.02           |


+====================================================================================================================+
+                                                    5  -  LOOPS                                                     +
+====================================================================================================================+


+--------------------------------------------------------------------------------------------------------------------+
+                                                5.1  -  Top 10 Loops                                                +
+--------------------------------------------------------------------------------------------------------------------+

   Loop Id        | Module              | Source Location                                        | Coverage (%)   |
  ----------------+---------------------+--------------------------------------------------------+----------------+
   1174           | exec                | For.hpp:142-142,Layout.hpp:191-191,Scattering.cpp:8... | 19.99          |
   814            | exec                | For.hpp:142-142,LPlusTimes.cpp:57-57                   | 19.17          |
   948            | exec                | For.hpp:142-142,LTimes.cpp:62-62                       | 14.32          |
   1355           | exec                | For.hpp:142-142,SweepSubdomain.cpp:88-90,SweepSubdo... | 1.88           |
   1089           | exec                | For.hpp:142-142,Operators.hpp:369-369,Population.cp... | 0.52           |
   947            | exec                | For.hpp:142-142,LTimes.cpp:62-62                       | 0.00           |
   1353           | exec                | For.hpp:142-142,Operators.hpp:369-369,Layout.hpp:19... | 0.00           |
   813            | exec                | For.hpp:142-142,LPlusTimes.cpp:57-57                   | 0.00           |
   810            | exec                | For.hpp:142-142,LPlusTimes.cpp:57-57,Collapse.hpp:1... | 0.00           |
   1173           | exec                | For.hpp:142-142,Collapse.hpp:131-131,Collapse.hpp:1... | 0.00           |





+====================================================================================================================+
+                                                     6  -  CQA                                                      +
+====================================================================================================================+


+--------------------------------------------------------------------------------------------------------------------+
+                                                   6.1  -  Loops                                                    +
+--------------------------------------------------------------------------------------------------------------------+





      6.1.1  -  Loop 1174 from exec
  ==========================================================================================================

The loop is defined in:
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/tpl/raja/include/RAJA/pattern/kernel/For.hpp:142
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/tpl/raja/include/RAJA/policy/openmp/kernel/Collapse.hpp:131,140
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/tpl/raja/include/RAJA/util/Layout.hpp:191
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/tpl/raja/include/RAJA/internal/Iterators.hpp:291
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/src/Kripke/Kernel/Scattering.cpp:82-97


Warnings:
 - Non-innermost loop: analyzing only self part (ignoring child loops).
 - Ignoring paths for analysis
 - Failed to get the number of paths
 - RecMII not computed since number of paths is unknown or > max_paths
 - Streams not analyzed since number of paths is unknown or > max_paths


      6.1.1.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

0% of peak computational performance is used (0.42 out of 48.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.1.1.1  -  Code clean check
  ----------------------------------------------------------------------------------------------------------

Detected a slowdown caused by scalar integer instructions (typically used for address computation).
By removing them, you can lower the cost of an iteration from 4.75 to 1.00 cycles (4.75x speedup).

Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)



      6.1.1.1.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is probably not vectorized.
Only 14% of vector register length is used (average across all SSE/AVX instructions).
By vectorizing your loop, you can lower the cost of an iteration from 4.75 to 0.59 cycles (8.00x speedup).

Details
Store and arithmetical SSE/AVX instructions are used in scalar version (process only one data element in vector registers).
Since your execution units are vector units, only a vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ffast-math (included in Ofast) to extend loop vectorization to FP reductions.
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
C storage order is row-major: for(i) for(j) a[j][i] = b[j][i]; (slow, non stride 1) => for(i) for(j) a[i][j] = b[i][j]; (fast, stride 1)<<image_row_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
for(i) a[i].x = b[i].x; (slow, non stride 1) => for(i) a.x[i] = b.x[i]; (fast, stride 1)



      6.1.1.1.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by:
 - reading data from caches/RAM (load units are a bottleneck)
 - writing data to caches/RAM (the store unit is a bottleneck)

By removing all these bottlenecks, you can lower the cost of an iteration from 4.75 to 4.13 cycles (1.15x speedup).


Workaround
 - Read less array elements
 - Write less array elements
 - Provide more information to your compiler:
  * hardcode the bounds of the corresponding 'for' loop





      6.1.1.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 1 FMA (fused multiply-add) operations.




      6.1.1.1.5  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

1 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).



      6.1.1.1.6  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 2 FP arithmetical operations:
 - 1: addition or subtraction (all inside FMA instructions)
 - 1: multiply (all inside FMA instructions)
The binary loop is loading 132 bytes (16 double precision FP elements).
The binary loop is storing 16 bytes (2 double precision FP elements).


      6.1.1.1.7  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.01 FP operations per loaded or stored byte.







      6.1.2  -  Loop 814 from exec
  ==========================================================================================================

The loop is defined in:
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/tpl/raja/include/RAJA/pattern/kernel/For.hpp:142
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/src/Kripke/Kernel/LPlusTimes.cpp:57


It is main loop of related source loop which is unrolled by 4 (including vectorization).

      6.1.2.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

5% of peak computational performance is used (2.46 out of 48.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.2.1.1  -  Code clean check
  ----------------------------------------------------------------------------------------------------------

Detected a slowdown caused by scalar integer instructions (typically used for address computation).
By removing them, you can lower the cost of an iteration from 3.25 to 3.00 cycles (1.08x speedup).

Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)



      6.1.2.1.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
8 data elements could be processed at once in vector registers.
<<image_1x64_512>>By vectorizing your loop, you can lower the cost of an iteration from 3.25 to 0.41 cycles (8.00x speedup).

Details
All SSE/AVX instructions are used in scalar version (process only one data element in vector registers).
Since your execution units are vector units, only a vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ffast-math (included in Ofast) to extend loop vectorization to FP reductions.
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
C storage order is row-major: for(i) for(j) a[j][i] = b[j][i]; (slow, non stride 1) => for(i) for(j) a[i][j] = b[i][j]; (fast, stride 1)<<image_row_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
for(i) a[i].x = b[i].x; (slow, non stride 1) => for(i) a.x[i] = b.x[i]; (fast, stride 1)



      6.1.2.1.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by:
 - reading data from caches/RAM (load units are a bottleneck)
 - writing data to caches/RAM (the store unit is a bottleneck)

By removing all these bottlenecks, you can lower the cost of an iteration from 3.25 to 2.13 cycles (1.53x speedup).


Workaround
 - Read less array elements
 - Write less array elements
 - Provide more information to your compiler:
  * hardcode the bounds of the corresponding 'for' loop





      6.1.2.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 4 FMA (fused multiply-add) operations.




      6.1.2.1.5  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - LEA: 1 occurrences<<list_path_1_complex_1>>



      6.1.2.1.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

4 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).



      6.1.2.1.7  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 8 FP arithmetical operations:
 - 4: addition or subtraction (all inside FMA instructions)
 - 4: multiply (all inside FMA instructions)
The binary loop is loading 64 bytes (8 double precision FP elements).
The binary loop is storing 32 bytes (4 double precision FP elements).


      6.1.2.1.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.08 FP operations per loaded or stored byte.







      6.1.3  -  Loop 948 from exec
  ==========================================================================================================

The loop is defined in:
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/tpl/raja/include/RAJA/pattern/kernel/For.hpp:142
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/src/Kripke/Kernel/LTimes.cpp:62


It is main loop of related source loop which is unrolled by 4 (including vectorization).

      6.1.3.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

5% of peak computational performance is used (2.46 out of 48.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.3.1.1  -  Code clean check
  ----------------------------------------------------------------------------------------------------------

Detected a slowdown caused by scalar integer instructions (typically used for address computation).
By removing them, you can lower the cost of an iteration from 3.25 to 3.00 cycles (1.08x speedup).

Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)



      6.1.3.1.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
8 data elements could be processed at once in vector registers.
<<image_1x64_512>>By vectorizing your loop, you can lower the cost of an iteration from 3.25 to 0.41 cycles (8.00x speedup).

Details
All SSE/AVX instructions are used in scalar version (process only one data element in vector registers).
Since your execution units are vector units, only a vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ffast-math (included in Ofast) to extend loop vectorization to FP reductions.
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
C storage order is row-major: for(i) for(j) a[j][i] = b[j][i]; (slow, non stride 1) => for(i) for(j) a[i][j] = b[i][j]; (fast, stride 1)<<image_row_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
for(i) a[i].x = b[i].x; (slow, non stride 1) => for(i) a.x[i] = b.x[i]; (fast, stride 1)



      6.1.3.1.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by:
 - reading data from caches/RAM (load units are a bottleneck)
 - writing data to caches/RAM (the store unit is a bottleneck)

By removing all these bottlenecks, you can lower the cost of an iteration from 3.25 to 2.13 cycles (1.53x speedup).


Workaround
 - Read less array elements
 - Write less array elements
 - Provide more information to your compiler:
  * hardcode the bounds of the corresponding 'for' loop





      6.1.3.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 4 FMA (fused multiply-add) operations.




      6.1.3.1.5  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - LEA: 1 occurrences<<list_path_1_complex_1>>



      6.1.3.1.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

4 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).



      6.1.3.1.7  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 8 FP arithmetical operations:
 - 4: addition or subtraction (all inside FMA instructions)
 - 4: multiply (all inside FMA instructions)
The binary loop is loading 64 bytes (8 double precision FP elements).
The binary loop is storing 32 bytes (4 double precision FP elements).


      6.1.3.1.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.08 FP operations per loaded or stored byte.







      6.1.4  -  Loop 1355 from exec
  ==========================================================================================================

The loop is defined in:
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/tpl/raja/include/RAJA/pattern/kernel/For.hpp:142
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/src/Kripke/Kernel/SweepSubdomain.cpp:88-90,96-106


The related source loop is not unrolled or unrolled with no peel/tail loop.

      6.1.4.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

2% of peak computational performance is used (1.25 out of 48.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.4.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
8 data elements could be processed at once in vector registers.
<<image_1x64_512>>By vectorizing your loop, you can lower the cost of an iteration from 32.00 to 4.00 cycles (8.00x speedup).

Details
All SSE/AVX instructions are used in scalar version (process only one data element in vector registers).
Since your execution units are vector units, only a vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ffast-math (included in Ofast) to extend loop vectorization to FP reductions.
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
C storage order is row-major: for(i) for(j) a[j][i] = b[j][i]; (slow, non stride 1) => for(i) for(j) a[i][j] = b[i][j]; (fast, stride 1)<<image_row_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
for(i) a[i].x = b[i].x; (slow, non stride 1) => for(i) a.x[i] = b.x[i]; (fast, stride 1)



      6.1.4.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by execution of divide and square root operations (the divide/square root unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 32.00 to 15.25 cycles (2.10x speedup).


Workaround
 - Reduce the number of division or square root instructions:
  * If denominator is constant over iterations, use reciprocal (replace x/y with x*(1/y)). Check precision impact. This will be done by your compiler with ffast-math or Ofast
 - Check whether you really need double precision. If not, switch to single precision to speedup execution





      6.1.4.1.3  -  Expensive FP math instructions/calls
  ----------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 32.00 to 14.00 cycles (2.29x speedup).


      6.1.4.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 6 FMA (fused multiply-add) operations.




      6.1.4.1.5  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

Detected data structures (typically arrays) that cannot be efficiently read/written

Details
 - Constant unknown stride: 16 occurrence(s)
 - Irregular (variable stride) or indirect: 3 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)
 - Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.



      6.1.4.1.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

34 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).



      6.1.4.1.7  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 40 FP arithmetical operations:
 - 26: addition or subtraction (6 inside FMA instructions)
 - 6: multiply (all inside FMA instructions)
 - 8: divide
The binary loop is loading 416 bytes (52 double precision FP elements).
The binary loop is storing 72 bytes (9 double precision FP elements).


      6.1.4.1.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.08 FP operations per loaded or stored byte.







      6.1.5  -  Loop 1089 from exec
  ==========================================================================================================

The loop is defined in:
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/tpl/raja/include/RAJA/pattern/kernel/For.hpp:142
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/tpl/raja/include/RAJA/util/Operators.hpp:369
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/build/Kripke/src/Kripke/Kernel/Population.cpp:58


The related source loop is not unrolled or unrolled with no peel/tail loop.

      6.1.5.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

1% of peak computational performance is used (0.75 out of 48.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.5.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
8 data elements could be processed at once in vector registers.
<<image_1x64_512>>By vectorizing your loop, you can lower the cost of an iteration from 32.00 to 4.00 cycles (8.00x speedup).

Details
All SSE/AVX instructions are used in scalar version (process only one data element in vector registers).
Since your execution units are vector units, only a vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ffast-math (included in Ofast) to extend loop vectorization to FP reductions.
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
C storage order is row-major: for(i) for(j) a[j][i] = b[j][i]; (slow, non stride 1) => for(i) for(j) a[i][j] = b[i][j]; (fast, stride 1)<<image_row_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
for(i) a[i].x = b[i].x; (slow, non stride 1) => for(i) a.x[i] = b.x[i]; (fast, stride 1)



      6.1.5.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Found no such bottlenecks but see expert reports for more complex bottlenecks.




      6.1.5.1.3  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 8 FMA (fused multiply-add) operations.




      6.1.5.1.4  -  Complex instructions
  ----------------------------------------------------------------------------------------------------------

Detected COMPLEX INSTRUCTIONS.


Details
These instructions generate more than one micro-operation and only one of them can be decoded during a cycle and the extra micro-operations increase pressure on execution units.
 - LEA: 2 occurrences<<list_path_1_complex_1>>



      6.1.5.1.5  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

16 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).



      6.1.5.1.6  -  Matching between your loop (in the source code) and the binary loop
  ----------------------------------------------------------------------------------------------------------

The binary loop is composed of 24 FP arithmetical operations:
 - 8: addition or subtraction (all inside FMA instructions)
 - 16: multiply (8 inside FMA instructions)
The binary loop is loading 128 bytes (16 double precision FP elements).


      6.1.5.1.7  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.19 FP operations per loaded or stored byte.


      6.1.5.1.8  -  Unroll opportunity
  ----------------------------------------------------------------------------------------------------------

Loop is potentially data access bound.

Workaround
Unroll your loop if trip count is significantly higher than target unroll factor and if some data references are common to consecutive iterations. This can be done manually. Or with the unroll (resp. unroll_and_jam) directive on top of the inner (resp. surrounding) loop. You can enforce an unroll factor: #pragma unroll_and_jam N, unroll_and_jam(N), unroll N or unroll(N)





[MAQAO] Info: STOP THE REPORT GENERATION
[MAQAO] Info: 
[MAQAO] Info: If your application produces files, they can be found in directory "/beegfs/hackathon/users/eoseret/qaas_runs_test/178-668-9338/intel/Kripke/run/oneview_runs/compilers/icx_5/oneview_run_1786695111"
[MAQAO] Info: 
