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

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

* [MAQAO] Warning: Experiment directory /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/run/oneview_runs/defaults/armclang/oneview_results_1782375901 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: /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/run/oneview_runs/defaults/armclang/oneview_results_1782375901


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


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

  Application:			/home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/run/oneview_runs/defaults/orig/exec
  Timestamp:			2026-06-25 08:25:01
  Universal Timestamp:		1782375901
  Experiment Type:		MPI; OpenMP; Throughput; 
  Machine:			ip-172-31-38-240.ec2.internal
  Architecture:			aarch64
  Micro Architecture:		ARM_NEOVERSE_V1
  OS Version:			Linux 6.1.170-213.321.amzn2023.aarch64 #1 SMP Thu May 14 12:18:13 UTC 2026
  Compilation Options:		
		exec: Arm Toolchain for Linux 22.1.0 clang version 22.1.0 (https://github.com/arm/arm-toolchain.git c95792353373404441df364b5a762338e5642230) /opt/arm/arm-toolchain-for-linux/bin/clang-22 -frtlib-add-rpath -fveclib=ArmPL -mllvm -gvn-add-phi-translation=1 -mllvm -store-to-load-forwarding-conflict-detection=0 --driver-mode=g++ -D USE_OMP -I /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/TeaLeaf/src/omp -I /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/build/generated -I /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/TeaLeaf/driver -g -fno-omit-frame-pointer -fcf-protection=none -no-pie -grecord-command-line -D NDEBUG -std=c++17 -O3 -Wall -march=native -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 /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp -I /home/eoseret/tools/mpi/openmpi-armclang-22.1/include 
  Number of processes observed:	1
  Number of threads observed:	64
  MAQAO version:		2026.0.0
  MAQAO build:			Build information not available




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

  Total Time:				265.76 s
  Max (Thread Active Time):		265.57 s
  Average Active Time:			265.55 s
  Activity Ratio:			99.9 %
  Average number of active threads:	63.949
  Affinity Stability:			100.0 %
  Time spent in analyzed loops:		96.7 %
  Time spent in analyzed innermost loops: 96.7 %
  Time spent in user code:		96.8 %
  Compilation Options Score:		100
  Array Access Efficiency:		85.1 %

   Potential Speedups
  ----------------------------------------------------
  Perfect Flow Complexity:		1.00
  Perfect OpenMP/MPI/Pthread/TBB:	1.02
  Perfect OpenMP/MPI/Pthread/TBB + Load Distribution:	1.03
  If No Scalar Integer:
      Potential Speedup:		1.00
      Nb Loops to get 80%:		1
  If FP Vectorized:
      Potential Speedup:		1.11
      Nb Loops to get 80%:		1
  If Fully Vectorized:
      Potential Speedup:		2.29
      Nb Loops to get 80%:		2
  If Only FP Arithmetic:
      Potential Speedup:		1.37
      Nb Loops to get 80%:		2




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

  If No Scalar Integer:
      Number of loops   | 1      | 2      | 4      | 5      | 8      | 
      Cumulated Speedup | 1.0002 | 1.0002 | 1.0002 | 1.0002 | 1.0002 | 
  Top 5 loops:
    exec - 22:	1.0002
    exec - 70:	1.0002
    exec - 19:	1.0002
    exec - 13:	1.0002
    exec - 17:	1.0002

  If FP Vectorized:
      Number of loops   | 1      | 2      | 4      | 5      | 8      | 
      Cumulated Speedup | 1.1051 | 1.1051 | 1.1051 | 1.1051 | 1.1051 | 
  Top 5 loops:
    exec - 23:	1.1051
    exec - 19:	1.1051
    exec - 22:	1.1051
    exec - 70:	1.1051
    exec - 17:	1.1051

  If Fully Vectorized:
      Number of loops   | 1      | 2      | 4      | 5      | 8      | 
      Cumulated Speedup | 1.4805 | 2.2892 | 2.2922 | 2.2929 | 2.2940 | 
  Top 5 loops:
    exec - 19:	1.4805
    exec - 16:	2.2892
    exec - 17:	2.2909
    exec - 70:	2.2922
    exec - 22:	2.2929

  If Only FP Arithmetic:
      Number of loops   | 1      | 2      | 4      | 5      | 8      | 
      Cumulated Speedup | 1.1897 | 1.3183 | 1.3717 | 1.3721 | 1.3722 | 
  Top 5 loops:
    exec - 19:	1.1897
    exec - 16:	1.3183
    exec - 23:	1.3712
    exec - 17:	1.3717
    exec - 22:	1.3721



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


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

  [4 / 4] Application profile is long enough (265.57 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.

  [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] Host configuration allows retrieval of all necessary metrics.


  [2.9994127241522 / 3] Architecture specific option -march=native is used


  [3 / 3] Optimization level option is correctly used


  [2 / 2] Application is correctly profiled ("Others" category represents 0.00 % of the execution time)
To have a representative profiling, it is advised that the category "Others" represents less than 20% of the
execution time in order to analyze as much as possible of the user code

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


  [0 / 0] Fastmath not used
Consider to add ffast-math to compilation flags (or replace -O3 with -Ofast) to unlock potential extra speedup by
relaxing floating-point computation consistency. Warning: floating-point accuracy may be reduced and the compliance
to IEEE/ISO rules/specifications for math functions will be relaxed, typically 'errno' will no longer be set after
calling some math functions.


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

  [4 / 4] Enough time of the experiment time spent in analyzed loops (96.72%)
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 99.92% of observed threads are actually active 

  [4 / 4] CPU activity is good
CPU cores are active 99.93% of time

  [4 / 4] Loop profile is not flat
At least one loop coverage is greater than 4% (43.27%), representing an hotspot for the application

  [4 / 4] Enough time of the experiment time spent in analyzed innermost loops (96.65%)
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 (99.99%)
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 (1.41%)

  [3 / 3] Cumulative Outermost/In between loops coverage (0.07%) lower than cumulative innermost loop coverage (96.65%)
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 - 19  :
     analysis: Execution Time: 43 % - Vectorization Ratio: 0.00 % - Vector Length Use: 25.00 %

   + exec - 16  :
     analysis: Execution Time: 32 % - Vectorization Ratio: 4.35 % - Vector Length Use: 26.09 %
     Data Access Issues: 14
        [14] [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 7 issues ( = data accesses) costing 2
            point each.
     Vectorization Roadblocks: 14
        [14] [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 7 issues ( = data accesses) costing 2
            point each.

   + exec - 23  :
     analysis: Execution Time: 20 % - Vectorization Ratio: 83.33 % - Vector Length Use: 70.83 %
     Data Access Issues: 4
        [4] [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 2 issues ( = data accesses) costing 2
            point each.
     Vectorization Roadblocks: 4
        [4] [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 2 issues ( = data accesses) costing 2
            point each.

   + exec - 17  :
     analysis: Execution Time: 0 % - Vectorization Ratio: 0.00 % - Vector Length Use: 31.94 %
     Control Flow Issues: 2
        [2] [SA] Non innermost loop (Outermost) - Collapse loop with innermost ones. This issue costs 2 points.
     Data Access Issues: 14
        [14] [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 7 issues ( = data accesses) costing 2
            point each.
     Vectorization Roadblocks: 16
        [14] [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 7 issues ( = data accesses) costing 2
            point each.
        [2] [SA] Non innermost loop (Outermost) - Collapse loop with innermost ones. This issue costs 2 points.

   + exec - 70  :
     analysis: Execution Time: 0 % - Vectorization Ratio: 0.00 % - Vector Length Use: 25.00 %
     Data Access Issues: 2
        [2] [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 1 issues ( = data accesses) costing 2
            point each.
     Vectorization Roadblocks: 2
        [2] [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 1 issues ( = data accesses) costing 2
            point each.



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


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

   Category | IO     | Exe    | Others  | TBB   | String | Pthread | MPI   | OMP   | System | Memory | Math  |
  ----------+--------+--------+---------+-------+--------+---------+-------+-------+--------+--------+-------+
   Time (%) | 0.00   | 96.84  | 0.00    | 0.00  | 0.00   | 0.00    | 0.00  | 3.12  | 0.04   | 0.00   | 0.00  |




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

   Buckets                   | Nb Functions              | Coverage                  | Cumulated Coverage        |
  ---------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                      | 3                         | 96.68                     | 96.68                     |
   4% to 8%                  | 0                         | 0.00                      | 96.68                     |
   2% to 4%                  | 0                         | 0.00                      | 96.68                     |
   1% to 2%                  | 1                         | 1.88                      | 98.56                     |
   0.5% to 1%                | 0                         | 0.00                      | 98.56                     |
   0.25% to 0.5%             | 1                         | 0.41                      | 98.97                     |
   0.125% to 0.25%           | 2                         | 0.37                      | 99.34                     |
   < 0.125%                  | 48                        | 0.53                      | 99.87                     |




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

   Buckets                   | Nb Loops                  | Coverage                  | Cumulated Coverage        |
  ---------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                      | 3                         | 96.57                     | 96.57                     |
   4% to 8%                  | 0                         | 0.00                      | 96.57                     |
   2% to 4%                  | 0                         | 0.00                      | 96.57                     |
   1% to 2%                  | 0                         | 0.00                      | 96.57                     |
   0.5% to 1%                | 0                         | 0.00                      | 96.57                     |
   0.25% to 0.5%             | 0                         | 0.00                      | 96.57                     |
   0.125% to 0.25%           | 0                         | 0.00                      | 96.57                     |
   < 0.125%                  | 11                        | 0.08                      | 96.65                     |


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


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

   Function                                               | Module               | Coverage (%)  | Time (s)      |
  --------------------------------------------------------+----------------------+---------------+---------------+
   cg_calc_ur(int, int, int, double, double*, double*,... | exec                 | 43.29         | 114.95        |
   cg_calc_w(int, int, int, double*, double const*, do... | exec                 | 32.87         | 87.29         |
   cg_calc_p(int, int, int, double, double*, double co... | exec                 | 20.52         | 54.48         |
   kmp_flag_64<false, true>::wait(kmp_info*, int, void*)  | libomp.so            | 1.88          | 5.01          |
   arch_local_irq_enable                                  | kernel               | 0.41          | 1.09          |
   kmp_flag_native<unsigned long long, (flag_type)1, t... | libomp.so            | 0.21          | 0.57          |
   el0_svc_common.constprop.0                             | kernel               | 0.16          | 0.42          |
   __kmp_hyper_barrier_release(barrier_type, kmp_info*... | libomp.so            | 0.08          | 0.21          |
   __GI___sched_yield                                     | libc.so.6            | 0.06          | 0.15          |
   update_left(int, int, int, int, double*, bool) [clo... | exec                 | 0.04          | 0.10          |


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


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

   Loop Id        | Module               | Source Location                                       | Coverage (%)  |
  ----------------+----------------------+-------------------------------------------------------+---------------+
   19             | exec                 | cg.cpp:108-113                                        | 43.27         |
   16             | exec                 | cg.cpp:86-90                                          | 32.81         |
   23             | exec                 | cg.cpp:128-131                                        | 20.49         |
   17             | exec                 | cg.cpp:85-85                                          | 0.05          |
   70             | exec                 | local_halos.cpp:13-15                                 | 0.03          |
   22             | exec                 | cg.cpp:127-128,cg.cpp:131-131                         | 0.02          |
   13             | exec                 | cg.cpp:62-68                                          | 0.02          |
   72             | exec                 | local_halos.cpp:28-30                                 | 0.01          |
   0              | exec                 | cg.cpp:21-25                                          | 0.01          |
   125            | exec                 | solver_methods.cpp:69-72                              | 0.00          |





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


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





      6.1.1  -  Loop 19 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:108-113.

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

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

5% of peak computational performance is used (1.89 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is not vectorized.
4 data elements could be processed at once in vector registers.
By vectorizing your loop, you can lower the cost of an iteration from 3.17 to 0.79 cycles (4.00x speedup).

Details
All VPU 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)





      6.1.1.1.2  -  FMA
  ---------------------------------------------------------------------------------------------------------

Detected 3 FMA (fused multiply-add) operations.




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

The binary loop is composed of 6 FP arithmetical operations:
 - 3: addition or subtraction (all inside FMA instructions)
 - 3: multiply (all inside FMA instructions)
The binary loop does not load or store any data.







      6.1.2  -  Loop 16 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:86-90.

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

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

12% of peak computational performance is used (4.09 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is not vectorized.
Only 26% of vector register length is used (average across all VPU instructions).
By fully vectorizing your loop, you can lower the cost of an iteration from 3.67 to 1.00 cycles (3.67x speedup).

Details
4% of VPU instructions are used in vector version (process two or more data elements in vector registers):
 - 11% of VPU loads are used in vector version.
 - 0% of VPU stores are used in vector version.
 - 0% of VPU addition or subtraction instructions are used in vector version.
 - 0% of VPU multiply instructions are used in vector version.
 - 0% of VPU fused multiply-add 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:
  * 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)





      6.1.2.1.2  -  FMA
  ---------------------------------------------------------------------------------------------------------

Detected 4 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.
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).




      6.1.2.1.3  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 2 occurrence(s)
 - Constant non-unit stride: 5 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)



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

The binary loop is composed of 15 FP arithmetical operations:
 - 9: addition or subtraction (4 inside FMA instructions)
 - 6: multiply (4 inside FMA instructions)
The binary loop does not load or store any data.







      6.1.3  -  Loop 23 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:128-131.

The related source loop is unrolled by 4 (including vectorization).

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

21% of peak computational performance is used (6.86 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is partially vectorized.
Only 70% of vector register length is used (average across all VPU instructions).


Details
83% of VPU instructions are used in vector version (process two or more data elements in vector registers):
 - 0% of VPU addition or subtraction instructions are used in vector version.





      6.1.3.1.2  -  FMA
  ---------------------------------------------------------------------------------------------------------

Detected 4 FMA (fused multiply-add) operations.




      6.1.3.1.3  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant non-unit stride: 2 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)



      6.1.3.1.4  -  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 does not load or store any data.







      6.1.4  -  Loop 17 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:85-90.

Analyzed code is defined in /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:85.

Warnings:
Non-innermost loop: analyzing only self part (ignoring child loops).

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

0% of peak computational performance is used (0.00 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is not vectorized.
Only 31% of vector register length is used (average across all VPU instructions).
By vectorizing your loop, you can lower the cost of an iteration from 4.75 to 1.69 cycles (2.81x speedup).

Details
All VPU 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)




No data for this section



      6.1.4.1.2  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 7 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)



      6.1.4.1.3  -  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 does not load or store any data.







      6.1.5  -  Loop 70 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/TeaLeaf/src/omp/local_halos.cpp:13-15.

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

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

0% of peak computational performance is used (0.00 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is not vectorized.
4 data elements could be processed at once in vector registers.
By vectorizing your loop, you can lower the cost of an iteration from 1.00 to 0.25 cycles (4.00x speedup).

Details
All VPU 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)




No data for this section



      6.1.5.1.2  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant non-unit stride: 1 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)



      6.1.5.1.3  -  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 does not load or store any data.


      6.1.5.1.4  -  Unroll opportunity
  ---------------------------------------------------------------------------------------------------------

Loop body is too small to efficiently use resources.

Workaround
Unroll your loop if trip count is significantly higher than target unroll factor. 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)







      6.1.6  -  Loop 22 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:127-131.

Analyzed code is defined in /home/eoseret/qaas/qaas_runs/178-237-4322/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=5
 - 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 5 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
  ---------------------------------------------------------------------------------------------------------

0% of peak computational performance is used (0.00 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 9.25 to 1.75 cycles (5.29x 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 27% of vector register length is used (average across all VPU instructions).
By vectorizing your loop, you can lower the cost of an iteration from 9.25 to 3.78 cycles (2.45x speedup).

Details
All VPU 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)




No data for this section



      6.1.6.1.3  -  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 does not load or store any data.







      6.1.7  -  Loop 13 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/TeaLeaf/src/omp/cg.cpp:62-68.

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

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

10% of peak computational performance is used (3.43 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.7.1.1  -  Vectorization
  ---------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
Only 25% of vector register length is used (average across all VPU instructions).
By fully vectorizing your loop, you can lower the cost of an iteration from 4.67 to 1.25 cycles (3.73x speedup).

Details
3% of VPU instructions are used in vector version (process two or more data elements in vector registers):
 - 10% of VPU loads are used in vector version.
 - 0% of VPU stores are used in vector version.
 - 0% of VPU addition or subtraction instructions are used in vector version.
 - 0% of VPU multiply instructions are used in vector version.
 - 0% of VPU fused multiply-add 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:
  * 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)





      6.1.7.1.2  -  FMA
  ---------------------------------------------------------------------------------------------------------

Detected 4 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.
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).




      6.1.7.1.3  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 2 occurrence(s)
 - Constant non-unit stride: 7 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)



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

The binary loop is composed of 16 FP arithmetical operations:
 - 10: addition or subtraction (4 inside FMA instructions)
 - 6: multiply (4 inside FMA instructions)
The binary loop does not load or store any data.







      6.1.8  -  Loop 72 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/build/TeaLeaf/src/omp/local_halos.cpp:28-30.

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

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

0% of peak computational performance is used (0.00 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.8.1.1  -  Vectorization
  ---------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
4 data elements could be processed at once in vector registers.
By vectorizing your loop, you can lower the cost of an iteration from 1.00 to 0.25 cycles (4.00x speedup).

Details
All VPU 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)




No data for this section



      6.1.8.1.2  -  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 does not load or store any data.


      6.1.8.1.3  -  Unroll opportunity
  ---------------------------------------------------------------------------------------------------------

Loop body is too small to efficiently use resources.

Workaround
Unroll your loop if trip count is significantly higher than target unroll factor. 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 "/home/eoseret/qaas/qaas_runs/178-237-4322/intel/TeaLeaf/run/oneview_runs/defaults/orig/oneview_run_1782375901"
[MAQAO] Info: 
