	*************************************************
	*                                               *
	*          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/178-663-3091/intel/CoMD/run/oneview_runs/multicore/gcc_8/oneview_results_1786635418 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/178-663-3091/intel/CoMD/run/oneview_runs/multicore/gcc_8/oneview_results_1786635418


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


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

  Application:			/beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/run/binaries/gcc_8/exec
  Timestamp:			2026-08-13 17:36:58
  Universal Timestamp:		1786635418
  Experiment Type:		MPI; OpenMP; Throughput; 
  Machine:			gmz17.benchmarkcenter.megware.com
  Architecture:			x86_64
  Micro Architecture:		ZEN_V5
  Model Name:			AMD EPYC 9655 96-Core Processor
  Cache Size:			1024 KB
  Number of Cores:		96
  OS Version:			Linux 5.14.0-687.29.1.el9_8.x86_64 #1 SMP PREEMPT_DYNAMIC Thu Jul 23 16:18:48 EDT 2026
  Compilation Options:		
		exec: GNU C23 15.1.0 -march=znver5 -mprefer-vector-width=256 -g -O2 -ffast-math -fno-omit-frame-pointer -fcf-protection=none -fopenmp -funroll-loops 
  Number of processes observed:	1
  Number of threads observed:	8
  MAQAO version:		2026.1.0
  MAQAO build:			6d1be1d51c1e63266254997eb301734a7264775d::20260810-150026




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

  Total Time:				58.00 s
  Max (Thread Active Time):		57.34 s
  Average Active Time:			51.90 s
  Activity Ratio:			89.7 %
  Average number of active threads:	7.158
  Affinity Stability:			99.6 %
  Time spent in analyzed loops:		95.4 %
  Time spent in analyzed innermost loops: 80.7 %
  Time spent in user code:		97.0 %
  Compilation Options Score:		87.5
  Array Access Efficiency:		85.4 %

   Potential Speedups
  ----------------------------------------------------
  Perfect Flow Complexity:		2.93
  Perfect OpenMP/MPI/Pthread/TBB:	1.01
  Perfect OpenMP/MPI/Pthread/TBB + Load Distribution:	1.13
  If No Scalar Integer:
      Potential Speedup:		1.01
      Nb Loops to get 80%:		3
  If FP Vectorized:
      Potential Speedup:		2.48
      Nb Loops to get 80%:		2
  If Fully Vectorized:
      Potential Speedup:		5.55
      Nb Loops to get 80%:		3
  If Only FP Arithmetic:
      Potential Speedup:		1.03
      Nb Loops to get 80%:		5




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

  If No Scalar Integer:
      Number of loops   | 1      | 4      | 9      | 13     | 18     | 
      Cumulated Speedup | 1.0037 | 1.0067 | 1.0072 | 1.0072 | 1.0072 | 
  Top 5 loops:
    exec - 58:	1.0037
    exec - 85:	1.0056
    exec - 59:	1.0063
    exec - 89:	1.0067
    exec - 101:	1.007

  If FP Vectorized:
      Number of loops   | 1      | 4      | 9      | 13     | 18     | 
      Cumulated Speedup | 2.0199 | 2.4812 | 2.4848 | 2.4849 | 2.4849 | 
  Top 5 loops:
    exec - 88:	2.0199
    exec - 87:	2.4661
    exec - 100:	2.4791
    exec - 101:	2.4812
    exec - 99:	2.4833

  If Fully Vectorized:
      Number of loops   | 1      | 4      | 9      | 13     | 18     | 
      Cumulated Speedup | 2.9268 | 4.8976 | 5.4406 | 5.5371 | 5.5526 | 
  Top 5 loops:
    exec - 88:	2.9268
    exec - 87:	4.4542
    exec - 58:	4.733
    exec - 102:	4.8976
    exec - 101:	5.0445

  If Only FP Arithmetic:
      Number of loops   | 1      | 4      | 9      | 13     | 18     | 
      Cumulated Speedup | 1.0132 | 1.0228 | 1.0290 | 1.0292 | 1.0292 | 
  Top 5 loops:
    exec - 58:	1.0132
    exec - 83:	1.0174
    exec - 102:	1.0202
    exec - 101:	1.0228
    exec - 59:	1.0254



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


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

  [4 / 4] Application profile is long enough (57.34 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.

  [2.9994786365744 / 3] Most of time spent in analyzed modules (99.98%) comes from functions compiled with architecture specialization option
-march=znver5


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

  [3 / 3] Optimization level option is correctly used


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


  [2 / 2] Application is correctly profiled ("Others" category represents 0.38 % 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 (95.37%)
If the time spent in analyzed loops is less than 30%, standard loop optimizations will have a limited impact on
application performances.

  [3 / 4] A significant amount of threads are idle (10.52%)
On average, more than 10% of observed threads are idle. Such threads are probably IO/sync waiting. Some hints: use
faster filesystems to read/write data, improve parallel load balancing and/or scheduling.

  [3 / 4] CPU activity is below 90% (89.73%)
CPU cores are idle more than 10% of time. Threads supposed to run on these cores are probably IO/sync waiting. Some
hints: use faster filesystems to read/write data, improve parallel load balancing and/or scheduling.

  [4 / 4] Loop profile is not flat
At least one loop coverage is greater than 4% (76.70%), representing an hotspot for the application

  [4 / 4] Enough time of the experiment time spent in analyzed innermost loops (80.66%)
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 (99.60%)
Threads are not migrating to CPU cores: probably successfully pinned

  [3 / 3] Less than 10% (0.00%) is spend in BLAS1 operations
It could be more efficient to inline by hand BLAS1 operations

  [0 / 3] Too many functions do not use all threads
Functions running on a reduced number of threads (typically sequential code) cover at least 10% of application
walltime (11.81%). Check both "Max Inclusive Time Over Threads" and "Nb Threads" in Functions or Loops tabs and
consider parallelizing sequential regions or improving parallelization of regions running on a reduced number of
threads

  [3 / 3] Cumulative Outermost/In between loops coverage (14.70%) lower than cumulative innermost loop coverage (80.66%)
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% (0.02%) is spend in Libm/SVML (special functions)



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

  Top 5 loops:
   + exec - 88  :
     analysis: Execution Time: 76 % - Vectorization Ratio: 26.67 % - Vector Length Use: 15.83 %
     Loop Computation Issues: 8
        [8] [SA] Presence of expensive FP instructions - Perform hoisting, change algorithm, use SVML or proper
            numerical library or perform value profiling (count the number of distinct input values). There are 2
            issues (= instructions) costing 4 points each.
     Control Flow Issues: 13
        [13] [SA] Too many paths (9 paths) - Simplify control structure. There are 9 issues ( = paths) costing 1 point
            each with a malus of 4 points.
     Data Access Issues: 4
        [4] [SA] Presence of special instructions executing on a single port (BLEND/MERGE, BROADCAST) - Simplify data
            access and try to get stride 1 access. There are 4 issues (= instructions) costing 1 point each.
     Vectorization Roadblocks: 13
        [13] [SA] Too many paths (9 paths) - Simplify control structure. There are 9 issues ( = paths) costing 1 point
            each with a malus of 4 points.
     Inefficient Vectorization: 4
        [4] [SA] Presence of special instructions executing on a single port (BLEND/MERGE, BROADCAST) - Simplify data
            access and try to get stride 1 access. There are 4 issues (= instructions) costing 1 point each.

   + exec - 87  :
     analysis: Execution Time: 13 % - Vectorization Ratio: 25.40 % - Vector Length Use: 15.48 %
     Loop Computation Issues: 8
        [8] [SA] Presence of expensive FP instructions - Perform hoisting, change algorithm, use SVML or proper
            numerical library or perform value profiling (count the number of distinct input values). There are 2
            issues (= instructions) costing 4 points each.
     Control Flow Issues: 126
        [124] [SA] Too many paths (120 paths) - Simplify control structure. There are 120 issues ( = paths) costing 1
            point each with a malus of 4 points.
        [2] [SA] Non innermost loop (InBetween) - Collapse loop with innermost ones. This issue costs 2 points.
     Data Access Issues: 4
        [4] [SA] Presence of special instructions executing on a single port (BLEND/MERGE, BROADCAST) - Simplify data
            access and try to get stride 1 access. There are 4 issues (= instructions) costing 1 point each.
     Vectorization Roadblocks: 126
        [124] [SA] Too many paths (120 paths) - Simplify control structure. There are 120 issues ( = paths) costing 1
            point each with a malus of 4 points.
        [2] [SA] Non innermost loop (InBetween) - Collapse loop with innermost ones. This issue costs 2 points.
     Inefficient Vectorization: 4
        [4] [SA] Presence of special instructions executing on a single port (BLEND/MERGE, BROADCAST) - Simplify data
            access and try to get stride 1 access. There are 4 issues (= instructions) costing 1 point each.

   + exec - 58  :
     analysis: Execution Time: 1 % - Vectorization Ratio: 0.00 % - Vector Length Use: 11.25 %
     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.
     Data Access Issues: 8
        [8] [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 4 issues ( = data accesses) costing 2 point
            each.
     Vectorization Roadblocks: 8
        [8] [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 4 issues ( = data accesses) costing 2 point
            each.

   + exec - 102 :
     analysis: Execution Time: 0 % - Vectorization Ratio: 0.00 % - Vector Length Use: 12.50 %
     Data Access Issues: 6
        [6] [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 3 issues ( = data accesses) costing 2 point
            each.
     Vectorization Roadblocks: 6
        [6] [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 3 issues ( = data accesses) costing 2 point
            each.

   + exec - 101 :
     analysis: Execution Time: 0 % - Vectorization Ratio: 0.00 % - Vector Length Use: 12.41 %
     Control Flow Issues: 22
        [20] [SA] Too many paths (16 paths) - Simplify control structure. There are 16 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.
     Vectorization Roadblocks: 22
        [20] [SA] Too many paths (16 paths) - Simplify control structure. There are 16 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.



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


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

   Category | IO     | Exe    | System  | Others  | Memory | String | MPI   | TBB   | OMP   | Pthread | Math  |
  ----------+--------+--------+---------+---------+--------+--------+-------+-------+-------+---------+-------+
   Time (%) | 0.00   | 97.04  | 0.16    | 0.38    | 0.00   | 0.15   | 0.00  | 0.00  | 2.24  | 0.00    | 0.02  |




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

   Buckets                    | Nb Functions              | Coverage                  | Cumulated Coverage        |
  ----------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                       | 1                         | 90.72                     | 90.72                     |
   4% to 8%                   | 0                         | 0.00                      | 90.72                     |
   2% to 4%                   | 0                         | 0.00                      | 90.72                     |
   1% to 2%                   | 4                         | 5.91                      | 96.63                     |
   0.5% to 1%                 | 2                         | 1.44                      | 98.07                     |
   0.25% to 0.5%              | 2                         | 0.77                      | 98.84                     |
   0.125% to 0.25%            | 5                         | 0.72                      | 99.56                     |
   < 0.125%                   | 15                        | 0.39                      | 99.95                     |




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

   Buckets                    | Nb Loops                  | Coverage                  | Cumulated Coverage        |
  ----------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                       | 1                         | 76.70                     | 76.70                     |
   4% to 8%                   | 0                         | 0.00                      | 76.70                     |
   2% to 4%                   | 0                         | 0.00                      | 76.70                     |
   1% to 2%                   | 1                         | 1.48                      | 78.18                     |
   0.5% to 1%                 | 2                         | 1.39                      | 79.57                     |
   0.25% to 0.5%              | 2                         | 0.75                      | 80.32                     |
   0.125% to 0.25%            | 1                         | 0.13                      | 80.45                     |
   < 0.125%                   | 7                         | 0.21                      | 80.66                     |


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


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

   Function                                               | Module              | Coverage (%)   | Time (s)       |
  --------------------------------------------------------+---------------------+----------------+----------------+
   ljForce._omp_fn.1                                      | exec                | 90.72          | 47.08          |
   sortAtomsInCell                                        | exec                | 1.89           | 0.98           |
   advanceVelocity._omp_fn.0                              | exec                | 1.49           | 0.77           |
   gomp_team_barrier_wait_end                             | libgomp.so.1.0.0    | 1.46           | 0.76           |
   getBoxFromCoord                                        | exec                | 1.07           | 4.43           |
   gomp_barrier_wait_end                                  | libgomp.so.1.0.0    | 0.77           | 0.46           |
   advancePosition._omp_fn.0                              | exec                | 0.67           | 0.35           |
   ljForce._omp_fn.0                                      | exec                | 0.47           | 0.24           |
   msort_with_tmp.part.0                                  | libc.so.6           | 0.30           | 0.16           |
   unknown_kernel_region                                  | kernel              | 0.16           | 0.08           |


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


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

   Loop Id        | Module              | Source Location                                        | Coverage (%)   |
  ----------------+---------------------+--------------------------------------------------------+----------------+
   88             | exec                | ljForce.c:191-191,ljForce.c:197-216                    | 76.70          |
   87             | exec                | ljForce.c:187-187,ljForce.c:191-191,ljForce.c:197-1... | 13.65          |
   58             | exec                | haloExchange.c:621-630                                 | 1.48           |
   102            | exec                | timestep.c:74-78                                       | 0.81           |
   101            | exec                | timestep.c:74-78                                       | 0.68           |
   100            | exec                | timestep.c:88-94                                       | 0.58           |
   83             | exec                | mytype.h:22-24,ljForce.c:161-161                       | 0.47           |
   59             | exec                | haloExchange.c:633-642                                 | 0.29           |
   85             | exec                | ljForce.c:178-182,ljForce.c:187-187,ljForce.c:191-1... | 0.27           |
   37             | exec                | haloExchange.c:380-390                                 | 0.13           |





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


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





      6.1.1  -  Loop 88 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/ljForce.c:191,197-216.

The related source loop is not unrolled or unrolled with no peel/tail loop.
Warnings:
 - Ignoring paths for analysis
 - Too many paths. Rerun with max-paths=9
 - 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 9 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)


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

8% of peak computational performance is used (4.13 out of 48.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is poorly vectorized.
Only 15% 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 15.00 to 2.12 cycles (7.06x speedup).

Details
26% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 42% of SSE/AVX loads are used in vector version.
 - 33% of SSE/AVX stores are used in vector version.
 - 50% of SSE/AVX addition or subtraction instructions are used in vector version.
 - 14% of SSE/AVX multiply instructions are used in vector version.
 - 14% of SSE/AVX fused multiply-add instructions are used in vector version.
 - 0% of SSE/AVX divide and square root instructions are used in vector version.
 - 33% 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:
  * recompile with ftree-vectorize (included in O3) to enable loop vectorization (including 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  -  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 15.00 to 10.13 cycles (1.48x speedup).


Workaround
Reduce the number of FP multiply/FMA instructions




      6.1.1.1.3  -  Expensive FP math instructions/calls
  ----------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 15.00 to 14.00 cycles (1.07x speedup).


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

Detected 16 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).




      6.1.1.1.5  -  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.
 - VCOMISD: 4 occurrences<<list_path_1_complex_1>>



      6.1.1.1.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

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



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

The binary loop is composed of 62 FP arithmetical operations:
 - 28: addition or subtraction (16 inside FMA instructions)
 - 32: multiply (16 inside FMA instructions)
 - 2: divide
The binary loop is loading 192 bytes (24 double precision FP elements).
The binary loop is storing 64 bytes (8 double precision FP elements).


      6.1.1.1.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.24 FP operations per loaded or stored byte.







      6.1.2  -  Loop 87 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/ljForce.c:187-191,197-216.

Analyzed code is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/ljForce.c:187,191,197-198,201,206-210,213,216.

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 120 paths individually, rerun with max-paths=120
 - 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 120 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)


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

8% of peak computational performance is used (4.13 out of 48.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is poorly vectorized.
Only 15% 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 15.00 to 2.12 cycles (7.06x speedup).

Details
25% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 42% of SSE/AVX loads are used in vector version.
 - 33% of SSE/AVX stores are used in vector version.
 - 44% of SSE/AVX addition or subtraction instructions are used in vector version.
 - 14% of SSE/AVX multiply instructions are used in vector version.
 - 14% of SSE/AVX fused multiply-add instructions are used in vector version.
 - 0% of SSE/AVX divide and square root instructions are used in vector version.
 - 28% 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:
  * recompile with ftree-vectorize (included in O3) to enable loop vectorization (including 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  -  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 15.00 to 12.25 cycles (1.22x speedup).


Workaround
Reduce the number of FP multiply/FMA instructions




      6.1.2.1.3  -  Expensive FP math instructions/calls
  ----------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 15.00 to 14.00 cycles (1.07x speedup).


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

Detected 16 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).




      6.1.2.1.5  -  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.
 - VCOMISD: 4 occurrences<<list_path_1_complex_1>>



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

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



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

The binary loop is composed of 62 FP arithmetical operations:
 - 28: addition or subtraction (16 inside FMA instructions)
 - 32: multiply (16 inside FMA instructions)
 - 2: divide
The binary loop is loading 200 bytes (25 double precision FP elements).
The binary loop is storing 64 bytes (8 double precision FP elements).


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

Arithmetic intensity is 0.23 FP operations per loaded or stored byte.







      6.1.3  -  Loop 58 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/haloExchange.c:621-630.

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

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

0% of peak computational performance is used (0.00 out of 96.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Detected a slowdown caused by scalar integer instructions (typically used for address computation).
By removing them, you can lower the cost of an iteration from 8.00 to 6.00 cycles (1.33x speedup).

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



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

Your loop is not vectorized.
Only 11% 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 8.00 to 0.87 cycles (9.14x speedup).

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


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ftree-vectorize (included in O3) to enable loop vectorization (including FP reductions).
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
C storage order is row-major: for(i) for(j) a[j][i] = b[j][i]; (slow, non stride 1) => for(i) for(j) a[i][j] = b[i][j]; (fast, stride 1)<<image_row_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
for(i) a[i].x = b[i].x; (slow, non stride 1) => for(i) a.x[i] = b.x[i]; (fast, stride 1)



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

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

By removing all these bottlenecks, you can lower the cost of an iteration from 8.00 to 6.00 cycles (1.33x speedup).


Workaround
 - Read less array elements
 - Write less array elements
 - Provide more information to your compiler:
  * hardcode the bounds of the corresponding 'for' loop
  * use the 'restrict' C99 keyword




No data for this section



      6.1.3.1.4  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

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

Details
 - Constant non-unit stride: 4 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)



      6.1.3.1.5  -  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.


      6.1.3.1.6  -  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 112 bytes.
The binary loop is storing 112 bytes.


      6.1.3.1.7  -  Unroll opportunity
  ----------------------------------------------------------------------------------------------------------

Loop is data access bound.

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







      6.1.4  -  Loop 102 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/timestep.c:74-78.

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

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

5% of peak computational performance is used (2.67 out of 48.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is not vectorized.
8 data elements could be processed at once in vector registers.
<<image_1x64_512>>By vectorizing your loop, you can lower the cost of an iteration from 18.00 to 2.25 cycles (8.00x speedup).

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


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ftree-vectorize (included in O3) to enable loop vectorization (including FP reductions).
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
C storage order is row-major: for(i) for(j) a[j][i] = b[j][i]; (slow, non stride 1) => for(i) for(j) a[i][j] = b[i][j]; (fast, stride 1)<<image_row_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
for(i) a[i].x = b[i].x; (slow, non stride 1) => for(i) a.x[i] = b.x[i]; (fast, stride 1)



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

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

By removing all these bottlenecks, you can lower the cost of an iteration from 18.00 to 12.00 cycles (1.50x speedup).


Workaround
 - Read less array elements
All SSE and/or AVX registers are used:
in that case, try to relax register pressure by reducing the unroll factor or splitting your loop
 - Write less array elements
 - Provide more information to your compiler:
  * hardcode the bounds of the corresponding 'for' loop
  * use the 'restrict' C99 keyword





      6.1.4.1.3  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 24 FMA (fused multiply-add) operations.




      6.1.4.1.4  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

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

Details
 - Constant non-unit stride: 3 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


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



      6.1.4.1.5  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

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



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

The binary loop is composed of 48 FP arithmetical operations:
 - 24: addition or subtraction (all inside FMA instructions)
 - 24: multiply (all inside FMA instructions)
The binary loop is loading 384 bytes (48 double precision FP elements).
The binary loop is storing 192 bytes (24 double precision FP elements).


      6.1.4.1.7  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

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


      6.1.4.1.8  -  Unroll opportunity
  ----------------------------------------------------------------------------------------------------------

Loop is data access bound.

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







      6.1.5  -  Loop 101 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/timestep.c:74-78.

Warnings:
 - Non-innermost loop: analyzing only self part (ignoring child loops).
 - Ignoring paths for analysis
 - Too many paths. Rerun with max-paths=16
 - 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 16 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
  ----------------------------------------------------------------------------------------------------------

5% of peak computational performance is used (2.51 out of 48.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is not vectorized.
Only 12% 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 16.75 to 2.03 cycles (8.25x speedup).

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


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ftree-vectorize (included in O3) to enable loop vectorization (including FP reductions).
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
C storage order is row-major: for(i) for(j) a[j][i] = b[j][i]; (slow, non stride 1) => for(i) for(j) a[i][j] = b[i][j]; (fast, stride 1)<<image_row_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
for(i) a[i].x = b[i].x; (slow, non stride 1) => for(i) a.x[i] = b.x[i]; (fast, stride 1)



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

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

By removing all these bottlenecks, you can lower the cost of an iteration from 16.75 to 13.88 cycles (1.21x speedup).


Workaround
 - Read less array elements
All SSE and/or AVX registers are used:
in that case, try to relax register pressure by reducing the unroll factor or splitting your loop
 - Write less array elements
 - Provide more information to your compiler:
  * hardcode the bounds of the corresponding 'for' loop
  * use the 'restrict' C99 keyword





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

Detected 21 FMA (fused multiply-add) operations.




      6.1.5.1.4  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

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



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

The binary loop is composed of 42 FP arithmetical operations:
 - 21: addition or subtraction (all inside FMA instructions)
 - 21: multiply (all inside FMA instructions)
The binary loop is loading 364 bytes (45 double precision FP elements).
The binary loop is storing 168 bytes (21 double precision FP elements).


      6.1.5.1.6  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

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







      6.1.6  -  Loop 100 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/timestep.c:88-94.

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

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

5% of peak computational performance is used (2.50 out of 48.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.6.1.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
8 data elements could be processed at once in vector registers.
<<image_1x64_512>>By vectorizing your loop, you can lower the cost of an iteration from 16.00 to 2.00 cycles (8.00x speedup).

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


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ftree-vectorize (included in O3) to enable loop vectorization (including 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)



      6.1.6.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

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

By removing all these bottlenecks, you can lower the cost of an iteration from 16.00 to 14.00 cycles (1.14x speedup).


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





      6.1.6.1.3  -  Expensive FP math instructions/calls
  ----------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 16.00 to 12.00 cycles (1.33x speedup).


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

Detected 12 FMA (fused multiply-add) operations.




      6.1.6.1.5  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

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

Details
 - Constant non-unit stride: 4 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)



      6.1.6.1.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

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



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

The binary loop is composed of 40 FP arithmetical operations:
 - 12: addition or subtraction (all inside FMA instructions)
 - 24: multiply (12 inside FMA instructions)
 - 4: divide
The binary loop is loading 240 bytes (30 double precision FP elements).
The binary loop is storing 96 bytes (12 double precision FP elements).


      6.1.6.1.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

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


      6.1.6.1.9  -  Unroll opportunity
  ----------------------------------------------------------------------------------------------------------

Loop is potentially data access bound.

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







      6.1.7  -  Loop 83 from exec
  ==========================================================================================================

The loop is defined in:
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/mytype.h:22-24
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/ljForce.c:161


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

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

0% of peak computational performance is used (0.00 out of 96.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is not vectorized.
16 data elements could be processed at once in vector registers.
By vectorizing your loop, you can lower the cost of an iteration from 8.00 to 0.50 cycles (16.00x speedup).

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


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ftree-vectorize (included in O3) to enable loop vectorization (including 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  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

Performance is limited by writing data to caches/RAM (the store unit is a bottleneck).

By removing all these bottlenecks, you can lower the cost of an iteration from 8.00 to 4.38 cycles (1.83x speedup).


Workaround
 - Write less array elements
 - Provide more information to your compiler:
  * hardcode the bounds of the corresponding 'for' loop
  * use the 'restrict' C99 keyword




No data for this section



      6.1.7.1.3  -  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.


      6.1.7.1.4  -  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 storing 256 bytes.


      6.1.7.1.5  -  Unroll opportunity
  ----------------------------------------------------------------------------------------------------------

Loop is data access bound.

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







      6.1.8  -  Loop 59 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/haloExchange.c:633-642.

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 96.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.8.1.1  -  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 8.00 to 6.00 cycles (1.33x speedup).

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



      6.1.8.1.2  -  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 8.00 to 0.87 cycles (9.14x speedup).

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


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ftree-vectorize (included in O3) to enable loop vectorization (including 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)



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

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

By removing all these bottlenecks, you can lower the cost of an iteration from 8.00 to 6.00 cycles (1.33x speedup).


Workaround
 - Read less array elements
 - Write less array elements
 - Provide more information to your compiler:
  * hardcode the bounds of the corresponding 'for' loop
  * use the 'restrict' C99 keyword




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
 - Constant non-unit stride: 4 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)



      6.1.8.1.5  -  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 112 bytes.
The binary loop is storing 112 bytes.


      6.1.8.1.7  -  Unroll opportunity
  ----------------------------------------------------------------------------------------------------------

Loop is data access bound.

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







      6.1.9  -  Loop 85 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/ljForce.c:178-191,197-216.

Analyzed code is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/ljForce.c:178-182,187,191,201.

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 4337 paths individually, rerun with max-paths=4337
 - 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 4337 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 96.00 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 3.50 to 1.13 cycles (3.11x 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 probably 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 3.50 to 0.26 cycles (13.26x speedup).

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


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ftree-vectorize (included in O3) to enable loop vectorization (including 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)



      6.1.9.1.3  -  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  -  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.


      6.1.9.1.5  -  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 44 bytes.
The binary loop is storing 24 bytes.







      6.1.10  -  Loop 37 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/build/CoMD/CoMD/src-openmp/haloExchange.c:380-390.

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

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

1% of peak computational performance is used (0.71 out of 48.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.10.1.1  -  Code clean check
  ----------------------------------------------------------------------------------------------------------

Detected a slowdown caused by scalar integer instructions (typically used for address computation).
By removing them, you can lower the cost of an iteration from 4.25 to 3.50 cycles (1.21x speedup).

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



      6.1.10.1.2  -  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 4.25 to 0.45 cycles (9.38x speedup).

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


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with ftree-vectorize (included in O3) to enable loop vectorization (including 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)



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

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

By removing all these bottlenecks, you can lower the cost of an iteration from 4.25 to 3.00 cycles (1.42x speedup).


Workaround
 - Read less array elements
 - Write less array elements
 - Provide more information to your compiler:
  * hardcode the bounds of the corresponding 'for' loop
  * use the 'restrict' C99 keyword




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
 - 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)



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

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



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

The binary loop is composed of 3 FP arithmetical operations:
 - 3: addition or subtraction
The binary loop is loading 60 bytes (7 double precision FP elements).
The binary loop is storing 56 bytes (7 double precision FP elements).


      6.1.10.1.7  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.03 FP operations per loaded or stored byte.


      6.1.10.1.8  -  Unroll opportunity
  ----------------------------------------------------------------------------------------------------------

Loop is data access bound.

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





[MAQAO] Info: STOP THE REPORT GENERATION
[MAQAO] Info: 
[MAQAO] Info: If your application produces files, they can be found in directory "/beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3091/intel/CoMD/run/oneview_runs/multicore/gcc_8/oneview_run_1786635418"
[MAQAO] Info: 
