	*************************************************
	*                                               *
	*          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/178-663-0009/intel/TeaLeaf/run/oneview_runs/multicore/aocc_2/oneview_results_1786642649 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: -> OPEN THE MAIN APPLICATION BINARY ...
[MAQAO] Info: ---> ALL LOOPS HAVE BEEN ANALYZED
[MAQAO] Info: ---> ALL FUNCTIONS HAVE BEEN ANALYZED
[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/178-663-0009/intel/TeaLeaf/run/oneview_runs/multicore/aocc_2/oneview_results_1786642649


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


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

  Application:			/beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/run/binaries/aocc_2/exec
  Timestamp:			2026-08-13 19:37:29
  Universal Timestamp:		1786642649
  Experiment Type:		MPI; Throughput; 
  Machine:			isix06.benchmarkcenter.megware.com
  Architecture:			x86_64
  Micro Architecture:		GRANITE_RAPIDS
  Model Name:			Intel(R) Xeon(R) 6972P
  Cache Size:			491520 KB
  Number of Cores:		96
  OS Version:			Linux 5.14.0-687.31.1.el9_8.x86_64 #1 SMP PREEMPT_DYNAMIC Sat Aug 1 05:38:01 EDT 2026
  Compilation Options:		
		exec: AMD clang version 17.0.6 (CLANG: AOCC_5.1.0-Build#1994 2025_12_23) /cluster/comp/aocc/5.1.0/bin/clang-17 --driver-mode=g++ -D USE_OMP -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/aocc_2/generated -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/driver -O3 -march=graniterapids -mprefer-vector-width=512 -ffast-math -g -fno-omit-frame-pointer -fcf-protection=none -nopie -grecord-command-line -D NDEBUG -std=c++17 -O3 -Wall -fopenmp=libomp -MD -MT CMakeFiles/tealeaf.dir/src/omp/cg.cpp.o -MF CMakeFiles/tealeaf.dir/src/omp/cg.cpp.o.d -o CMakeFiles/tealeaf.dir/src/omp/cg.cpp.o -c /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp -I /cluster/hpcx/2.23/ompi5-aocc-mt/include -I /cluster/hpcx/2.23/ompi5-aocc-mt/include/openmpi 
  Number of processes observed:	6
  Number of threads observed:	6
  MAQAO version:		2026.1.0
  MAQAO build:			6d1be1d51c1e63266254997eb301734a7264775d::20260810-150026




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

  Total Time:				320.74 s
  Max (Thread Active Time):		320.12 s
  Average Active Time:			320.07 s
  Activity Ratio:			99.8 %
  Average number of active threads:	5.987
  Affinity Stability:			99.8 %
  Time spent in analyzed loops:		96.1 %
  Time spent in analyzed innermost loops: 95.6 %
  Time spent in user code:		96.2 %
  Compilation Options Score:		100
  Array Access Efficiency:		99.7 %

   Potential Speedups
  ----------------------------------------------------
  Perfect Flow Complexity:		1.00
  Perfect OpenMP/MPI/Pthread/TBB:	1.00
  Perfect OpenMP/MPI/Pthread/TBB + Load Distribution:	1.03
  If No Scalar Integer:
      Potential Speedup:		1.00
      Nb Loops to get 80%:		2
  If FP Vectorized:
      Potential Speedup:		1.00
      Nb Loops to get 80%:		2
  If Fully Vectorized:
      Potential Speedup:		1.01
      Nb Loops to get 80%:		10
  If Only FP Arithmetic:
      Potential Speedup:		1.09
      Nb Loops to get 80%:		2




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

  If No Scalar Integer:
      Number of loops   | 1      | 7      | 15     | 22     | 30     | 
      Cumulated Speedup | 1.0014 | 1.0031 | 1.0031 | 1.0031 | 1.0031 | 
  Top 5 loops:
    exec - 30:	1.0014
    exec - 25:	1.0028
    exec - 34:	1.0031
    exec - 124:	1.0031
    exec - 119:	1.0031

  If FP Vectorized:
      Number of loops   | 1      | 7      | 15     | 22     | 30     | 
      Cumulated Speedup | 1.0006 | 1.0010 | 1.0010 | 1.0010 | 1.0010 | 
  Top 5 loops:
    exec - 33:	1.0006
    exec - 38:	1.0009
    exec - 25:	1.001
    exec - 30:	1.001
    exec - 3:	1.001

  If Fully Vectorized:
      Number of loops   | 1      | 7      | 15     | 22     | 30     | 
      Cumulated Speedup | 1.0012 | 1.0038 | 1.0058 | 1.0060 | 1.0060 | 
  Top 5 loops:
    exec - 33:	1.0012
    exec - 38:	1.0017
    exec - 30:	1.0022
    exec - 133:	1.0027
    exec - 34:	1.0031

  If Only FP Arithmetic:
      Number of loops   | 1      | 7      | 15     | 22     | 30     | 
      Cumulated Speedup | 1.0664 | 1.0936 | 1.0945 | 1.0945 | 1.0945 | 
  Top 5 loops:
    exec - 37:	1.0664
    exec - 32:	1.0891
    exec - 30:	1.091
    exec - 25:	1.0927
    exec - 34:	1.093



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


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

  [4 / 4] Application profile is long enough (320.12 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.

  [2.9991146824097 / 3] Most of time spent in analyzed modules (99.97%) comes from functions compiled with architecture specialization option
-march=graniterapids


  [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 0.25 % 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 (96.14%)
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 99.79% of observed threads are actually active 

  [4 / 4] CPU activity is good
CPU cores are active 99.79% of time

  [4 / 4] Loop profile is not flat
At least one loop coverage is greater than 4% (39.02%), representing an hotspot for the application

  [4 / 4] Enough time of the experiment time spent in analyzed innermost loops (95.61%)
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 (99.82%)
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 (0.00%)

  [3 / 3] Cumulative Outermost/In between loops coverage (0.54%) lower than cumulative innermost loop coverage (95.61%)
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 - 27  :
     analysis: Execution Time: 39 % - Vectorization Ratio: 96.77 % - Vector Length Use: 97.18 %
     Data Access Issues: 1
        [1] [SA] Presence of special instructions executing on a single port (BROADCAST) - Simplify data access and
            try to get stride 1 access. There are 1 issues (= instructions) costing 1 point each.
     Inefficient Vectorization: 1
        [1] [SA] Presence of special instructions executing on a single port (BROADCAST) - Simplify data access and
            try to get stride 1 access. There are 1 issues (= instructions) costing 1 point each.

   + exec - 32  :
     analysis: Execution Time: 37 % - Vectorization Ratio: 100.00 % - Vector Length Use: 100.00 %

   + exec - 37  :
     analysis: Execution Time: 18 % - Vectorization Ratio: 100.00 % - Vector Length Use: 100.00 %

   + exec - 33  :
     analysis: Execution Time: 0 % - Vectorization Ratio: 100.00 % - Vector Length Use: 50.00 %
     Data Access Issues: 0
        [0] [SA] Inefficient vectorization: more than 10% of the vector loads instructions are unaligned - When
            allocating arrays, don’t forget to align them. There are 0 issues ( = arrays) costing 2 points each

   + exec - 30  :
     analysis: Execution Time: 0 % - Vectorization Ratio: 22.95 % - Vector Length Use: 19.98 %
     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: 53
        [51] [SA] Too many paths (47 paths) - Simplify control structure. There are 47 issues ( = paths) costing 1
            point each with a malus of 4 points.
        [2] [SA] Non innermost loop (Outermost) - Collapse loop with innermost ones. This issue costs 2 points.
     Data Access Issues: 9
        [7] [SA] Presence of special instructions executing on a single port (INSERT/EXTRACT, SHUFFLE/PERM, BROADCAST)
            - Simplify data access and try to get stride 1 access. There are 7 issues (= instructions) costing 1
            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: 53
        [51] [SA] Too many paths (47 paths) - Simplify control structure. There are 47 issues ( = paths) costing 1
            point each with a malus of 4 points.
        [2] [SA] Non innermost loop (Outermost) - Collapse loop with innermost ones. This issue costs 2 points.
     Inefficient Vectorization: 7
        [7] [SA] Presence of special instructions executing on a single port (INSERT/EXTRACT, SHUFFLE/PERM, BROADCAST)
            - Simplify data access and try to get stride 1 access. There are 7 issues (= instructions) costing 1
            point each.



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


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

   Category | IO     | Exe    | System  | Others  | Memory | String | MPI   | TBB   | OMP   | Pthread | Math  |
  ----------+--------+--------+---------+---------+--------+--------+-------+-------+-------+---------+-------+
   Time (%) | 0.00   | 96.21  | 0.12    | 0.25    | 0.01   | 0.14   | 3.18  | 0.00  | 0.07  | 0.02    | 0.00  |




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

   Buckets                    | Nb Functions              | Coverage                  | Cumulated Coverage        |
  ----------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                       | 3                         | 95.65                     | 95.65                     |
   4% to 8%                   | 0                         | 0.00                      | 95.65                     |
   2% to 4%                   | 0                         | 0.00                      | 95.65                     |
   1% to 2%                   | 1                         | 1.59                      | 97.25                     |
   0.5% to 1%                 | 1                         | 0.90                      | 98.14                     |
   0.25% to 0.5%              | 1                         | 0.48                      | 98.62                     |
   0.125% to 0.25%            | 1                         | 0.14                      | 98.77                     |
   < 0.125%                   | 38                        | 1.03                      | 99.80                     |




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

   Buckets                    | Nb Loops                  | Coverage                  | Cumulated Coverage        |
  ----------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                       | 3                         | 94.86                     | 94.86                     |
   4% to 8%                   | 0                         | 0.00                      | 94.86                     |
   2% to 4%                   | 0                         | 0.00                      | 94.86                     |
   1% to 2%                   | 0                         | 0.00                      | 94.86                     |
   0.5% to 1%                 | 0                         | 0.00                      | 94.86                     |
   0.25% to 0.5%              | 0                         | 0.00                      | 94.86                     |
   0.125% to 0.25%            | 1                         | 0.25                      | 95.11                     |
   < 0.125%                   | 17                        | 0.50                      | 95.61                     |


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


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

   Function                                               | Module              | Coverage (%)   | Time (s)       |
  --------------------------------------------------------+---------------------+----------------+----------------+
   cg_calc_w(int, int, int, double*, double const*, do... | exec                | 39.20          | 125.48         |
   cg_calc_ur(int, int, int, double, double*, double*,... | exec                | 37.63          | 120.44         |
   cg_calc_p(int, int, int, double, double*, double co... | exec                | 18.82          | 60.24          |
   hmca_bcol_basesmuma_bcast_k_nomial_knownroot_progress  | hmca_bcol_basesm... | 1.59           | 7.64           |
   hmca_bcol_basesmuma_reduce_intra_fanin_progress        | hmca_bcol_basesm... | 0.90           | 8.62           |
   hmca_bcol_basesmuma_allreduce_intra_fanin_fanout_pr... | hmca_bcol_basesm... | 0.48           | 4.61           |
   __GI___strcasecmp_l_sse2                               | libc.so.6           | 0.14           | 0.46           |
   unknown_kernel_region                                  | kernel              | 0.11           | 0.36           |
   mca_part_persist_progress                              | libmpi.so.40.40.7   | 0.11           | 0.34           |
   pack_right(int, int, int, int, double const*, doubl... | exec                | 0.09           | 0.55           |


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


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

   Loop Id        | Module              | Source Location                                        | Coverage (%)   |
  ----------------+---------------------+--------------------------------------------------------+----------------+
   27             | exec                | cg.cpp:88-90                                           | 39.02          |
   32             | exec                | cg.cpp:111-113                                         | 37.17          |
   37             | exec                | cg.cpp:131-131                                         | 18.67          |
   33             | exec                | cg.cpp:111-113                                         | 0.25           |
   30             | exec                | cg.cpp:107-113                                         | 0.21           |
   25             | exec                | cg.cpp:85-90                                           | 0.18           |
   38             | exec                | cg.cpp:131-131                                         | 0.10           |
   133            | exec                | pack_halos.cpp:33-35                                   | 0.05           |
   34             | exec                | cg.cpp:127-128,cg.cpp:131-131                          | 0.04           |
   114            | exec                | local_halos.cpp:28-30                                  | 0.04           |





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


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





      6.1.1  -  Loop 27 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:88-90.

It is main loop of related source loop which is unrolled by 8 (including vectorization).

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

75% of peak computational performance is used (24.00 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.1.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is highly vectorized.
97% of vector register length is used (average across all SSE/AVX instructions).


Details
96% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 95% of SSE/AVX loads are used in vector version.
 - 0% of SSE/AVX instructions that are not load, store, addition, subtraction nor multiply instructions are used in vector version.



      6.1.1.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by execution of FP multiply or FMA (fused multiply-add) operations (the FP multiply/FMA unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 10.00 to 7.50 cycles (1.33x speedup).


Workaround
Reduce the number of FP multiply/FMA instructions




      6.1.1.1.3  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 80 FMA (fused multiply-add) operations.
Presence of both ADD/SUB and MUL operations.

Workaround
Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
Estimated speedup by perfect pairing: 1.11x.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).




      6.1.1.1.4  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

20 AVX-512 instructions are processing arithmetic or math operations on double precision FP elements in vector mode (eight at a time).



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

The binary loop is composed of 240 FP arithmetical operations:
 - 144: addition or subtraction (80 inside FMA instructions)
 - 96: multiply (80 inside FMA instructions)
The binary loop is loading 1288 bytes (161 double precision FP elements).
The binary loop is storing 128 bytes (16 double precision FP elements).


      6.1.1.1.6  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.17 FP operations per loaded or stored byte.







      6.1.2  -  Loop 32 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:111-113.

It is main loop of related source loop which is unrolled by 16 (including vectorization).

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

94% of peak computational performance is used (30.32 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.2.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is fully vectorized, using full register length.


Details
All SSE/AVX instructions are used in vector version (process two or more data elements in vector registers).



      6.1.2.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

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




      6.1.2.1.3  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 96 FMA (fused multiply-add) operations.




      6.1.2.1.4  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

12 AVX-512 instructions are processing arithmetic or math operations on double precision FP elements in vector mode (eight at a time).



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

The binary loop is composed of 192 FP arithmetical operations:
 - 96: addition or subtraction (all inside FMA instructions)
 - 96: multiply (all inside FMA instructions)
The binary loop is loading 1024 bytes (128 double precision FP elements).
The binary loop is storing 512 bytes (64 double precision FP elements).


      6.1.2.1.6  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.12 FP operations per loaded or stored byte.







      6.1.3  -  Loop 37 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:131.

It is main loop of related source loop which is unrolled by 32 (including vectorization).

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

66% of peak computational performance is used (21.33 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.3.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is fully vectorized, using full register length.


Details
All SSE/AVX instructions are used in vector version (process two or more data elements in vector registers).



      6.1.3.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

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




      6.1.3.1.3  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 32 FMA (fused multiply-add) operations.




      6.1.3.1.4  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

4 AVX-512 instructions are processing arithmetic or math operations on double precision FP elements in vector mode (eight at a time).



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

The binary loop is composed of 64 FP arithmetical operations:
 - 32: addition or subtraction (all inside FMA instructions)
 - 32: multiply (all inside FMA instructions)
The binary loop is loading 512 bytes (64 double precision FP elements).
The binary loop is storing 256 bytes (32 double precision FP elements).


      6.1.3.1.6  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

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







      6.1.4  -  Loop 33 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:111-113.

It is intermediate loop of related source loop which is unrolled by 16 (including vectorization).

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

18% of peak computational performance is used (6.00 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.4.1.1  -  Unrolling/vectorization cost
  ----------------------------------------------------------------------------------------------------------

This loop is peel/tail of a unrolled/vectorized loop. If its cost is not negligible compared to the main (unrolled/vectorized) loop, unrolling/vectorization is counterproductive due to low trip count.

Details
The more iterations the main loop is processing, the higher the trip count must be to amortize peel/tail overhead.

Workaround
 - recompile with -fprofile-instr-generate, execute ,merge raw profiles with 'llvm-profdata merge -o default.profdata default.profraw' and recompile with -fprofile-instr-use (profile-guided optimization)
 - hardcode most frequent values of loop bounds by adding specialized paths.:
  *  For instance, replace for (i=0; i<n; i++) foo(i) with:
switch (n) {
  case (4): for (i=0; i<4; i++) foo(i); break;
  case (6): for (i=0; i<6; i++) foo(i); break;
  default : for (i=0; i<n; i++) foo(i); break;
}



      6.1.4.1.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is vectorized, but using only 256 out of 512 bits (AVX/AVX2 instructions on AVX-512 processors).
<<image_4x64_512>>

Details
All SSE/AVX instructions are used in vector version (process two or more data elements in vector registers).



      6.1.4.1.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

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




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

Detected 12 FMA (fused multiply-add) operations.




      6.1.4.1.5  -  Vector unaligned load/store instructions
  ----------------------------------------------------------------------------------------------------------

Detected 4 optimal vector unaligned load/store instructions.


Details
 - VMOVUPD: 4 occurrences<<list_path_1_vec_align_1>>


Workaround
Use vector aligned instructions:
 1) align your arrays on 64 bytes boundaries: replace { void *p = malloc (size); } with { void *p; posix_memalign (&p, 64, size); }.
 2) inform your compiler that your arrays are vector aligned: if array 'foo' is 64 bytes-aligned, define a pointer 'p_foo' as __builtin_assume_aligned (foo, 64) and use it instead of 'foo' in the loop.
<<image_vec_align>>


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

3 AVX instructions are processing arithmetic or math operations on double precision FP elements in vector mode (four 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 24 FP arithmetical operations:
 - 12: addition or subtraction (all inside FMA instructions)
 - 12: multiply (all inside FMA instructions)
The binary loop is loading 128 bytes (16 double precision FP elements).
The binary loop is storing 64 bytes (8 double precision FP elements).


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

Arithmetic intensity is 0.12 FP operations per loaded or stored byte.







      6.1.5  -  Loop 30 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:107-113.

Warnings:
 - Non-innermost loop: analyzing only self part (ignoring child loops).
 - Ignoring paths for analysis
 - Too many paths. If you really need to analyze all of the 47 paths individually, rerun with max-paths=47
 - RecMII not computed since number of paths is unknown or > max_paths
 - Streams not analyzed since number of paths is unknown or > max_paths

Try to simplify control and/or increase the maximum number of paths per function/loop through the 'max-paths-nb' option.

This loop has 47 execution paths.

The presence of multiple execution paths is typically the main/first bottleneck.
Try to simplify control inside loop: ideally, try to remove all conditional expressions, for example by (if applicable):
 - hoisting them (moving them outside the loop)
 - turning them into conditional moves, MIN or MAX


Ex: if (x<0) x=0 => x = (x<0 ? 0 : x) (or MAX(0,x) after defining the corresponding macro)


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

3% of peak computational performance is used (1.22 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.5.1.1  -  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 36.17 to 12.00 cycles (3.01x speedup).

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



      6.1.5.1.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is poorly vectorized.
Only 19% of vector register length is used (average across all SSE/AVX instructions).
By fully vectorizing your loop, you can lower the cost of an iteration from 36.17 to 28.30 cycles (1.28x speedup).

Details
22% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 0% of SSE/AVX loads are used in vector version.
 - 0% of SSE/AVX stores are used in vector version.
 - 66% of SSE/AVX addition or subtraction instructions are used in vector version.
 - 0% of SSE/AVX fused multiply-add instructions are used in vector version.
 - 25% of SSE/AVX instructions that are not load, store, addition, subtraction nor multiply instructions are used in vector version.
Since your execution units are vector units, only a fully vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one
 - 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)



      6.1.5.1.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

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




      6.1.5.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 3 FMA (fused multiply-add) operations.




      6.1.5.1.5  -  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.
 - AND: 2 occurrences<<list_path_1_complex_1>>
 - SETA: 2 occurrences<<list_path_1_complex_2>>
 - SETB: 4 occurrences<<list_path_1_complex_3>>



      6.1.5.1.6  -  Vector unaligned load/store instructions
  ----------------------------------------------------------------------------------------------------------

Detected 2 suboptimal vector unaligned load/store instructions.


Details
 - VEXTRACTF128: 2 occurrences<<list_path_1_vec_align_1>>


Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * use march=native
 - Use vector aligned instructions:
  1) align your arrays on 64 bytes boundaries: replace { void *p = malloc (size); } with { void *p; posix_memalign (&p, 64, size); }.
  2) inform your compiler that your arrays are vector aligned: if array 'foo' is 64 bytes-aligned, define a pointer 'p_foo' as __builtin_assume_aligned (foo, 64) and use it instead of 'foo' in the loop.
<<image_vec_align>>


      6.1.5.1.7  -  Conversion instructions
  ----------------------------------------------------------------------------------------------------------

Detected expensive conversion instructions.

Details
 - CLTQ: 2 occurrences<<list_path_1_cvt_1>>
 - MOVZX: 2 occurrences<<list_path_1_cvt_2>>


Workaround
Avoid mixing data with different types. In particular, check if the type of constants is the same as array elements.


      6.1.5.1.8  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

5 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).
2 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in vector mode (two at a time).
4 AVX-512 instructions are processing arithmetic or math operations on double precision FP elements in vector mode (eight at a time).



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

The binary loop is composed of 44 FP arithmetical operations:
 - 41: addition or subtraction (3 inside FMA instructions)
 - 3: multiply (all inside FMA instructions)
The binary loop is loading 493 bytes (61 double precision FP elements).
The binary loop is storing 51 bytes (6 double precision FP elements).


      6.1.5.1.10  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

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







      6.1.6  -  Loop 25 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:85-90.

Warnings:
 - Non-innermost loop: analyzing only self part (ignoring child loops).
 - Ignoring paths for analysis
 - Too many paths. If you really need to analyze all of the 43 paths individually, rerun with max-paths=43
 - RecMII not computed since number of paths is unknown or > max_paths
 - Streams not analyzed since number of paths is unknown or > max_paths

Try to simplify control and/or increase the maximum number of paths per function/loop through the 'max-paths-nb' option.

This loop has 43 execution paths.

The presence of multiple execution paths is typically the main/first bottleneck.
Try to simplify control inside loop: ideally, try to remove all conditional expressions, for example by (if applicable):
 - hoisting them (moving them outside the loop)
 - turning them into conditional moves, MIN or MAX


Ex: if (x<0) x=0 => x = (x<0 ? 0 : x) (or MAX(0,x) after defining the corresponding macro)


      6.1.6.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

2% of peak computational performance is used (0.84 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.6.1.1  -  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 44.00 to 11.00 cycles (4.00x speedup).

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



      6.1.6.1.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
Only 17% of vector register length is used (average across all SSE/AVX instructions).
By fully vectorizing your loop, you can lower the cost of an iteration from 44.00 to 36.90 cycles (1.19x speedup).

Details
18% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 0% of SSE/AVX loads are used in vector version.
 - 0% of SSE/AVX stores are used in vector version.
 - 40% of SSE/AVX addition or subtraction instructions are used in vector version.
 - 0% of SSE/AVX multiply instructions are used in vector version.
 - 0% of SSE/AVX fused multiply-add instructions are used in vector version.
 - 22% of SSE/AVX instructions that are not load, store, addition, subtraction nor multiply instructions are used in vector version.
Since your execution units are vector units, only a fully vectorized loop can use their full power.


Workaround
 - Try another compiler or update/tune your current one
 - 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)



      6.1.6.1.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

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




      6.1.6.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 5 FMA (fused multiply-add) operations.
Presence of both ADD/SUB and MUL operations.

Workaround
Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible.
Estimated speedup by perfect pairing: 1.00x.
For instance a + b*c is a valid FMA (MUL then ADD).
However (a+b)* c cannot be translated into an FMA (ADD then MUL).




      6.1.6.1.5  -  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.
 - AND: 1 occurrences<<list_path_1_complex_1>>
 - SETB: 3 occurrences<<list_path_1_complex_2>>



      6.1.6.1.6  -  Vector unaligned load/store instructions
  ----------------------------------------------------------------------------------------------------------

Detected 2 suboptimal vector unaligned load/store instructions.


Details
 - VEXTRACTF128: 2 occurrences<<list_path_1_vec_align_1>>


Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * use march=native
 - Use vector aligned instructions:
  1) align your arrays on 64 bytes boundaries: replace { void *p = malloc (size); } with { void *p; posix_memalign (&p, 64, size); }.
  2) inform your compiler that your arrays are vector aligned: if array 'foo' is 64 bytes-aligned, define a pointer 'p_foo' as __builtin_assume_aligned (foo, 64) and use it instead of 'foo' in the loop.
<<image_vec_align>>


      6.1.6.1.7  -  Conversion instructions
  ----------------------------------------------------------------------------------------------------------

Detected expensive conversion instructions.

Details
 - CLTQ: 1 occurrences<<list_path_1_cvt_1>>
 - MOVZX: 1 occurrences<<list_path_1_cvt_2>>


Workaround
Avoid mixing data with different types. In particular, check if the type of constants is the same as array elements.


      6.1.6.1.8  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

12 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).
2 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in vector mode (two at a time).
2 AVX-512 instructions are processing arithmetic or math operations on double precision FP elements in vector mode (eight at a time).



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

The binary loop is composed of 37 FP arithmetical operations:
 - 31: addition or subtraction (5 inside FMA instructions)
 - 6: multiply (5 inside FMA instructions)
The binary loop is loading 643 bytes (80 double precision FP elements).
The binary loop is storing 40 bytes (5 double precision FP elements).


      6.1.6.1.10  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.05 FP operations per loaded or stored byte.







      6.1.7  -  Loop 38 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:131.

It is intermediate loop of related source loop which is unrolled by 32 (including vectorization).

      6.1.7.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

25% of peak computational performance is used (8.00 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.7.1.1  -  Unrolling/vectorization cost
  ----------------------------------------------------------------------------------------------------------

This loop is peel/tail of a unrolled/vectorized loop. If its cost is not negligible compared to the main (unrolled/vectorized) loop, unrolling/vectorization is counterproductive due to low trip count.

Details
The more iterations the main loop is processing, the higher the trip count must be to amortize peel/tail overhead.

Workaround
 - recompile with -fprofile-instr-generate, execute ,merge raw profiles with 'llvm-profdata merge -o default.profdata default.profraw' and recompile with -fprofile-instr-use (profile-guided optimization)
 - hardcode most frequent values of loop bounds by adding specialized paths.:
  *  For instance, replace for (i=0; i<n; i++) foo(i) with:
switch (n) {
  case (4): for (i=0; i<4; i++) foo(i); break;
  case (6): for (i=0; i<6; i++) foo(i); break;
  default : for (i=0; i<n; i++) foo(i); break;
}



      6.1.7.1.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is vectorized, but using only 256 out of 512 bits (AVX/AVX2 instructions on AVX-512 processors).
<<image_4x64_512>>

Details
All SSE/AVX instructions are used in vector version (process two or more data elements in vector registers).



      6.1.7.1.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by execution of FP multiply or FMA (fused multiply-add) operations (the FP multiply/FMA unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 1.00 to 0.67 cycles (1.50x speedup).


Workaround
Reduce the number of FP multiply/FMA instructions




      6.1.7.1.4  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 4 FMA (fused multiply-add) operations.




      6.1.7.1.5  -  Vector unaligned load/store instructions
  ----------------------------------------------------------------------------------------------------------

Detected 2 optimal vector unaligned load/store instructions.


Details
 - VMOVUPD: 2 occurrences<<list_path_1_vec_align_1>>


Workaround
Use vector aligned instructions:
 1) align your arrays on 64 bytes boundaries: replace { void *p = malloc (size); } with { void *p; posix_memalign (&p, 64, size); }.
 2) inform your compiler that your arrays are vector aligned: if array 'foo' is 64 bytes-aligned, define a pointer 'p_foo' as __builtin_assume_aligned (foo, 64) and use it instead of 'foo' in the loop.
<<image_vec_align>>


      6.1.7.1.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

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



      6.1.7.1.7  -  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).


      6.1.7.1.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

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







      6.1.8  -  Loop 133 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp/pack_halos.cpp:33-35.

It is peel/tail loop of related source loop which is unrolled by 32 (including vectorization).

      6.1.8.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

0% of peak computational performance is used (0.00 out of 64.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.8.1.1  -  Unrolling/vectorization cost
  ----------------------------------------------------------------------------------------------------------

This loop is peel/tail of a unrolled/vectorized loop. If its cost is not negligible compared to the main (unrolled/vectorized) loop, unrolling/vectorization is counterproductive due to low trip count.

Details
The more iterations the main loop is processing, the higher the trip count must be to amortize peel/tail overhead.

Workaround
 - recompile with -fprofile-instr-generate, execute ,merge raw profiles with 'llvm-profdata merge -o default.profdata default.profraw' and recompile with -fprofile-instr-use (profile-guided optimization)
 - hardcode most frequent values of loop bounds by adding specialized paths.:
  *  For instance, replace for (i=0; i<n; i++) foo(i) with:
switch (n) {
  case (4): for (i=0; i<4; i++) foo(i); break;
  case (6): for (i=0; i<6; i++) foo(i); break;
  default : for (i=0; i<n; i++) foo(i); break;
}



      6.1.8.1.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
8 data elements could be processed at once in vector registers.


Details
All SSE/AVX instructions are used in scalar version (process only one data element in vector registers).



      6.1.8.1.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

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



No data for this section



      6.1.8.1.4  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

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

Details
 - Irregular (variable stride) or indirect: 1 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.


      6.1.8.1.5  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

No instructions are processing arithmetic or math operations on FP elements. This loop is probably writing/copying data or processing integer elements.


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

The binary loop does not contain any FP arithmetical operations.
The binary loop is loading 8 bytes.
The binary loop is storing 8 bytes.







      6.1.9  -  Loop 34 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:127-131.

Analyzed code is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:127-128,131.

Warnings:
 - Non-innermost loop: analyzing only self part (ignoring child loops).
 - Ignoring paths for analysis
 - Too many paths. Rerun with max-paths=27
 - RecMII not computed since number of paths is unknown or > max_paths
 - Streams not analyzed since number of paths is unknown or > max_paths

Try to simplify control and/or increase the maximum number of paths per function/loop through the 'max-paths-nb' option.

This loop has 27 execution paths.

The presence of multiple execution paths is typically the main/first bottleneck.
Try to simplify control inside loop: ideally, try to remove all conditional expressions, for example by (if applicable):
 - hoisting them (moving them outside the loop)
 - turning them into conditional moves, MIN or MAX


Ex: if (x<0) x=0 => x = (x<0 ? 0 : x) (or MAX(0,x) after defining the corresponding macro)


      6.1.9.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

0% of peak computational performance is used (0.00 out of 64.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.9.1.1  -  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 17.17 to 5.00 cycles (3.43x speedup).

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



      6.1.9.1.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
Only 10% 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 17.17 to 1.20 cycles (14.32x 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
 - 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)



      6.1.9.1.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

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



No data for this section



      6.1.9.1.4  -  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.
 - SETA: 1 occurrences<<list_path_1_complex_1>>



      6.1.9.1.5  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

No instructions are processing arithmetic or math operations on FP elements. This loop is probably writing/copying data or processing integer elements.


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

The binary loop does not contain any FP arithmetical operations.
The binary loop is loading 228 bytes.
The binary loop is storing 16 bytes.







      6.1.10  -  Loop 114 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-0009/intel/TeaLeaf/build/TeaLeaf/src/omp/local_halos.cpp:28-30.

It is peel/tail loop of related source loop which is unrolled by 4 (including vectorization).

      6.1.10.1  -  Path 1
  ----------------------------------------------------------------------------------------------------------

0% of peak computational performance is used (0.00 out of 64.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.10.1.1  -  Unrolling/vectorization cost
  ----------------------------------------------------------------------------------------------------------

This loop is peel/tail of a unrolled/vectorized loop. If its cost is not negligible compared to the main (unrolled/vectorized) loop, unrolling/vectorization is counterproductive due to low trip count.

Details
The more iterations the main loop is processing, the higher the trip count must be to amortize peel/tail overhead.

Workaround
 - recompile with -fprofile-instr-generate, execute ,merge raw profiles with 'llvm-profdata merge -o default.profdata default.profraw' and recompile with -fprofile-instr-use (profile-guided optimization)
 - hardcode most frequent values of loop bounds by adding specialized paths.:
  *  For instance, replace for (i=0; i<n; i++) foo(i) with:
switch (n) {
  case (4): for (i=0; i<4; i++) foo(i); break;
  case (6): for (i=0; i<6; i++) foo(i); break;
  default : for (i=0; i<n; i++) foo(i); break;
}



      6.1.10.1.2  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
8 data elements could be processed at once in vector registers.


Details
All SSE/AVX instructions are used in scalar version (process only one data element in vector registers).



      6.1.10.1.3  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

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



No data for this section



      6.1.10.1.4  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

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

Details
 - Irregular (variable stride) or indirect: 1 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.


      6.1.10.1.5  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

No instructions are processing arithmetic or math operations on FP elements. This loop is probably writing/copying data or processing integer elements.


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

The binary loop does not contain any FP arithmetical operations.
The binary loop is loading 8 bytes.
The binary loop is storing 8 bytes.





[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/178-663-0009/intel/TeaLeaf/run/oneview_runs/multicore/aocc_2/oneview_run_1786642649"
[MAQAO] Info: 
