	*************************************************
	*                                               *
	*          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/178-663-3083/intel/CoMD/run/oneview_runs/defaults/aocc/oneview_results_1786633260 already exists and is reused.
           It can be replaced using --replace in the command line.
[MAQAO] Info: 
[MAQAO] Info: START THE APPLICATION PROFILING
[MAQAO] Info: -> RUNNING THE PROFILER...
[MAQAO] Info:   LPROF has already been run
[MAQAO] Info: STOP THE APPLICATION PROFILING
[MAQAO] Info: 
[MAQAO] Info: START FUNCTIONS AND LOOPS ANALYSIS ...
[MAQAO] Info: STOP FUNCTIONS AND LOOPS ANALYSIS ...
[MAQAO] Info: 
[MAQAO] Info: START THE REPORT GENERATION
[MAQAO] Info: -> ONE-VIEW EXPERIMENT DIRECTORY: /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/intel/CoMD/run/oneview_runs/defaults/aocc/oneview_results_1786633260


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


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

  Application:			/beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/intel/CoMD/run/base_runs/defaults/aocc/exec
  Timestamp:			2026-08-13 17:01:00
  Universal Timestamp:		1786633260
  Experiment Type:		MPI; OpenMP; Throughput; 
  Machine:			isix07.benchmarkcenter.megware.com
  Architecture:			x86_64
  Micro Architecture:		GRANITE_RAPIDS
  Model Name:			Intel(R) Xeon(R) 6972P
  Cache Size:			491520 KB
  Number of Cores:		96
  OS Version:			Linux 5.14.0-687.31.1.el9_8.x86_64 #1 SMP PREEMPT_DYNAMIC Sat Aug 1 05:38:01 EDT 2026
  Compilation Options:		
		exec: AMD clang version 17.0.6 (CLANG: AOCC_5.1.0-Build#1994 2025_12_23) /cluster/comp/aocc/5.1.0/bin/clang-17 -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/intel/CoMD/build/CoMD/CoMD/src-openmp -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/intel/CoMD/build/aocc -O3 -D DO_MPI -g -fno-omit-frame-pointer -fcf-protection=none -nopie -grecord-command-line -fopenmp=libomp -MD -MT CMakeFiles/CoMD-openmp-mpi.dir/CoMD/src-openmp/ljForce.c.o -MF CMakeFiles/CoMD-openmp-mpi.dir/CoMD/src-openmp/ljForce.c.o.d -o CMakeFiles/CoMD-openmp-mpi.dir/CoMD/src-openmp/ljForce.c.o -c /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/intel/CoMD/build/CoMD/CoMD/src-openmp/ljForce.c -I /cluster/hpcx/2.23/ompi5-aocc-mt/include -I /cluster/hpcx/2.23/ompi5-aocc-mt/include/openmpi 
  Number of processes observed:	1
  Number of threads observed:	191
  MAQAO version:		2026.1.0
  MAQAO build:			6d1be1d51c1e63266254997eb301734a7264775d::20260810-150026




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

  Total Time:				15.08 s
  Max (Thread Active Time):		14.59 s
  Average Active Time:			14.18 s
  Activity Ratio:			97.6 %
  Average number of active threads:	179.518
  Affinity Stability:			99.4 %
  Time spent in analyzed loops:		23.7 %
  Time spent in analyzed innermost loops: 23.4 %
  Time spent in user code:		23.9 %
  Compilation Options Score:		66.67
  Array Access Efficiency:		96.4 %

   Potential Speedups
  ----------------------------------------------------
  Perfect Flow Complexity:		1.22
  Perfect OpenMP/MPI/Pthread/TBB:	1.09
  Perfect OpenMP/MPI/Pthread/TBB + Load Distribution:	3.88
  If No Scalar Integer:
      Potential Speedup:		1.01
      Nb Loops to get 80%:		3
  If FP Vectorized:
      Potential Speedup:		1.17
      Nb Loops to get 80%:		1
  If Fully Vectorized:
      Potential Speedup:		1.25
      Nb Loops to get 80%:		1
  If Only FP Arithmetic:
      Potential Speedup:		1.01
      Nb Loops to get 80%:		3




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

  If No Scalar Integer:
      Number of loops   | 1      | 2      | 5      | 7      | 10     | 
      Cumulated Speedup | 1.0025 | 1.0039 | 1.0051 | 1.0051 | 1.0051 | 
  Top 5 loops:
    exec - 59:	1.0025
    exec - 87:	1.0039
    exec - 91:	1.0049
    exec - 60:	1.0051
    exec - 45:	1.0051

  If FP Vectorized:
      Number of loops   | 1      | 2      | 5      | 7      | 10     | 
      Cumulated Speedup | 1.1640 | 1.1658 | 1.1675 | 1.1675 | 1.1675 | 
  Top 5 loops:
    exec - 97:	1.164
    exec - 87:	1.1658
    exec - 106:	1.1674
    exec - 45:	1.1675
    exec - 60:	1.1675

  If Fully Vectorized:
      Number of loops   | 1      | 2      | 5      | 7      | 10     | 
      Cumulated Speedup | 1.2189 | 1.2322 | 1.2437 | 1.2497 | 1.2527 | 
  Top 5 loops:
    exec - 97:	1.2189
    exec - 59:	1.2322
    exec - 91:	1.2365
    exec - 106:	1.2406
    exec - 87:	1.2437

  If Only FP Arithmetic:
      Number of loops   | 1      | 2      | 5      | 7      | 10     | 
      Cumulated Speedup | 1.0075 | 1.0090 | 1.0120 | 1.0125 | 1.0125 | 
  Top 5 loops:
    exec - 59:	1.0075
    exec - 87:	1.009
    exec - 104:	1.0105
    exec - 91:	1.0115
    exec - 60:	1.012



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


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

  [4 / 4] Application profile is long enough (14.59 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.


  [0 / 3] Compilation of some functions is not optimized for the target processor
Architecture specific options are needed to produce efficient code for a specific processor ( -march=(target) ).

  [3 / 3] Optimization level option is correctly used


  [2 / 2] Application is correctly profiled ("Others" category represents 0.09 % 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                                                +
+--------------------------------------------------------------------------------------------------------------------+

  [0 / 4] Too little time of the experiment time spent in analyzed loops (23.73%)
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 93.99% of observed threads are actually active 

  [4 / 4] CPU activity is good
CPU cores are active 97.64% of time

  [4 / 4] Loop profile is not flat
At least one loop coverage is greater than 4% (21.01%), representing an hotspot for the application

  [4 / 4] Enough time of the experiment time spent in analyzed innermost loops (23.36%)
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 (99.37%)
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 (133.09%). Check both "Max Inclusive Time Over Threads" and "Nb Threads" in Functions or Loops tabs and
consider parallelizing sequential regions or improving parallelization of regions running on a reduced number of
threads

  [3 / 3] Cumulative Outermost/In between loops coverage (0.37%) lower than cumulative innermost loop coverage (23.36%)
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% (0.01%) is spend in Libm/SVML (special functions)



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

  Top 5 loops:
   + exec - 97  :
     analysis: Execution Time: 21 % - Vectorization Ratio: 32.45 % - Vector Length Use: 16.56 %
     Loop Computation Issues: 8
        [4] [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 1
            issues (= instructions) costing 4 points each.
        [4] [SA] Less than 10% of the FP ADD/SUB/MUL arithmetic operations are performed using FMA - Reorganize
            arithmetic expressions to exhibit potential for FMA. This issue costs 4 points.
     Control Flow Issues: 3
        [3] [SA] Several paths (3 paths) - Simplify control structure or force the compiler to use masked
            instructions. There are 3 issues ( = paths) costing 1 point each.
     Data Access Issues: 4
        [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.
        [0] [SA] Inefficient vectorization: more than 10% of the vector loads instructions are unaligned - When
            allocating arrays, don’t forget to align them. There are 0 issues ( = arrays) costing 2 points each
        [2] [SA] Presence of special instructions executing on a single port (BLEND/MERGE) - Simplify data access and
            try to get stride 1 access. There are 2 issues (= instructions) costing 1 point each.
     Vectorization Roadblocks: 5
        [3] [SA] Several paths (3 paths) - Simplify control structure or force the compiler to use masked
            instructions. There are 3 issues ( = paths) costing 1 point each.
        [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.
     Inefficient Vectorization: 2
        [2] [SA] Presence of special instructions executing on a single port (BLEND/MERGE) - Simplify data access and
            try to get stride 1 access. There are 2 issues (= instructions) costing 1 point each.

   + exec - 59  :
     analysis: Execution Time: 0 % - Vectorization Ratio: 0.00 % - Vector Length Use: 10.94 %
     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.

   + exec - 106 :
     analysis: Execution Time: 0 % - Vectorization Ratio: 5.88 % - Vector Length Use: 13.24 %
     Loop Computation Issues: 8
        [4] [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 1
            issues (= instructions) costing 4 points each.
        [4] [SA] Less than 10% of the FP ADD/SUB/MUL arithmetic operations are performed using FMA - Reorganize
            arithmetic expressions to exhibit potential for FMA. This issue costs 4 points.

   + exec - 91  :
     analysis: Execution Time: 0 % - Vectorization Ratio: 33.33 % - Vector Length Use: 12.50 %
     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.

   + exec - 96  :
     analysis: Execution Time: 0 % - Vectorization Ratio: 0.00 % - Vector Length Use: 12.50 %
     Control Flow Issues: 5
        [3] [SA] Several paths (3 paths) - Simplify control structure or force the compiler to use masked
            instructions. There are 3 issues ( = paths) costing 1 point each.
        [2] [SA] Non innermost loop (InBetween) - Collapse loop with innermost ones. This issue costs 2 points.
     Data Access Issues: 2
        [2] [SA] More than 20% of the loads are accessing the stack - Perform loop splitting to decrease pressure on
            registers. This issue costs 2 points.
     Vectorization Roadblocks: 5
        [3] [SA] Several paths (3 paths) - Simplify control structure or force the compiler to use masked
            instructions. There are 3 issues ( = paths) costing 1 point each.
        [2] [SA] Non innermost loop (InBetween) - 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   | 23.87  | 2.50    | 0.09    | 0.00   | 0.04   | 0.00  | 0.00  | 73.48 | 0.00    | 0.01  |




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

   Buckets                    | Nb Functions              | Coverage                  | Cumulated Coverage        |
  ----------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                       | 3                         | 93.96                     | 93.96                     |
   4% to 8%                   | 0                         | 0.00                      | 93.96                     |
   2% to 4%                   | 1                         | 2.14                      | 96.10                     |
   1% to 2%                   | 1                         | 1.08                      | 97.18                     |
   0.5% to 1%                 | 1                         | 0.85                      | 98.03                     |
   0.25% to 0.5%              | 3                         | 1.19                      | 99.22                     |
   0.125% to 0.25%            | 2                         | 0.44                      | 99.66                     |
   < 0.125%                   | 50                        | 0.34                      | 100.00                    |




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

   Buckets                    | Nb Loops                  | Coverage                  | Cumulated Coverage        |
  ----------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                       | 1                         | 21.01                     | 21.01                     |
   4% to 8%                   | 0                         | 0.00                      | 21.01                     |
   2% to 4%                   | 0                         | 0.00                      | 21.01                     |
   1% to 2%                   | 0                         | 0.00                      | 21.01                     |
   0.5% to 1%                 | 1                         | 1.00                      | 22.01                     |
   0.25% to 0.5%              | 2                         | 0.80                      | 22.81                     |
   0.125% to 0.25%            | 2                         | 0.43                      | 23.25                     |
   < 0.125%                   | 13                        | 0.11                      | 23.36                     |


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


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

   Function                                               | Module              | Coverage (%)   | Time (s)       |
  --------------------------------------------------------+---------------------+----------------+----------------+
   __kmp_hyper_barrier_release(barrier_type, kmp_info*... | libomp.so           | 59.35          | 8.46           |
   ljForce.omp_outlined.5                                 | exec                | 21.37          | 3.03           |
   __kmp_hardware_timestamp                               | libomp.so           | 13.24          | 1.88           |
   unknown_kernel_region                                  | kernel              | 2.14           | 0.30           |
   sortAtomsInCell                                        | exec                | 1.08           | 0.15           |
   __kmp_hyper_barrier_gather(barrier_type, kmp_info*,... | libomp.so           | 0.85           | 0.25           |
   advancePosition.omp_outlined                           | exec                | 0.49           | 0.07           |
   __GI___sched_yield                                     | libc.so.6           | 0.39           | 0.05           |
   ljForce.omp_outlined                                   | exec                | 0.32           | 0.04           |
   updateLinkCells                                        | exec                | 0.22           | 6.06           |


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


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

   Loop Id        | Module              | Source Location                                        | Coverage (%)   |
  ----------------+---------------------+--------------------------------------------------------+----------------+
   97             | exec                | ljForce.c:191-191,ljForce.c:197-216                    | 21.01          |
   59             | exec                | haloExchange.c:621-630                                 | 1.00           |
   106            | exec                | timestep.c:88-94                                       | 0.49           |
   91             | exec                | mytype.h:23-23,ljForce.c:158-161                       | 0.32           |
   96             | exec                | ljForce.c:187-187                                      | 0.23           |
   87             | exec                | linkCells.c:211-247,linkCells.c:258-269,linkCells.c... | 0.22           |
   104            | exec                | timestep.c:74-78                                       | 0.21           |
   95             | exec                | ljForce.c:178-184,ljForce.c:187-187                    | 0.12           |
   60             | exec                | haloExchange.c:633-642                                 | 0.06           |
   45             | exec                | haloExchange.c:380-389                                 | 0.02           |





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


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





      6.1.1  -  Loop 97 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/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.
This loop has 3 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
  ----------------------------------------------------------------------------------------------------------

7% of peak computational performance is used (2.25 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is poorly vectorized.
Only 17% of vector register length is used (average across all SSE/AVX instructions).
By fully vectorizing your loop, you can lower the cost of an iteration from 4.00 to 0.56 cycles (7.11x speedup).

Details
37% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 50% of SSE/AVX loads are used in vector version.
 - 20% of SSE/AVX addition or subtraction instructions are used in vector version.
 - 0% of SSE/AVX multiply instructions are used in vector version.
 - 60% 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 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  -  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 4.00 to 3.50 cycles (1.14x speedup).


Workaround
Reduce the number of FP multiply/FMA instructions




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

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * use march=native
 - 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).





      6.1.1.1.4  -  Vector unaligned load/store instructions
  ----------------------------------------------------------------------------------------------------------

Detected 2 suboptimal vector unaligned load/store instructions.


Details
 - MOVUPD: 2 occurrences<<list_path_1_vec_align_1>>


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


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

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



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

The binary loop is composed of 9 FP arithmetical operations:
 - 6: addition or subtraction
 - 3: multiply
The binary loop is loading 48 bytes (6 double precision FP elements).


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

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




      6.1.1.2  -  Path 2
  ----------------------------------------------------------------------------------------------------------

7% of peak computational performance is used (2.30 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.1.2.1  -  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 13.50 to 2.00 cycles (6.75x speedup).

Details
26% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 27% of SSE/AVX loads are used in vector version.
 - 33% of SSE/AVX stores are used in vector version.
 - 16% of SSE/AVX addition or subtraction instructions are used in vector version.
 - 6% of SSE/AVX multiply instructions are used in vector version.
 - 0% of SSE/AVX divide and square root instructions are used in vector version.
 - 60% 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 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)



      6.1.1.2.2  -  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 13.50 to 10.50 cycles (1.29x speedup).


Workaround
Reduce the number of FP multiply/FMA instructions




      6.1.1.2.3  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * use march=native
 - 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).





      6.1.1.2.4  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown 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)



      6.1.1.2.5  -  Vector unaligned load/store instructions
  ----------------------------------------------------------------------------------------------------------

Detected 4 suboptimal vector unaligned load/store instructions.


Details
 - MOVUPD: 4 occurrences<<list_path_2_vec_align_1>>


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


      6.1.1.2.6  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

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



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

The binary loop is composed of 31 FP arithmetical operations:
 - 14: addition or subtraction
 - 16: multiply
 - 1: divide
The binary loop is loading 128 bytes (16 double precision FP elements).
The binary loop is storing 32 bytes (4 double precision FP elements).


      6.1.1.2.8  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

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




      6.1.1.3  -  Path 3
  ----------------------------------------------------------------------------------------------------------

7% of peak computational performance is used (2.25 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.1.3.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is poorly vectorized.
Only 16% 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 4.00 to 0.56 cycles (7.11x speedup).

Details
33% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 40% of SSE/AVX loads are used in vector version.
 - 20% of SSE/AVX addition or subtraction instructions are used in vector version.
 - 0% of SSE/AVX multiply instructions are used in vector version.
 - 50% 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 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)



      6.1.1.3.2  -  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 4.00 to 3.50 cycles (1.14x speedup).


Workaround
Reduce the number of FP multiply/FMA instructions




      6.1.1.3.3  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * use march=native
 - 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).





      6.1.1.3.4  -  Vector unaligned load/store instructions
  ----------------------------------------------------------------------------------------------------------

Detected 2 suboptimal vector unaligned load/store instructions.


Details
 - MOVUPD: 2 occurrences<<list_path_3_vec_align_1>>


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


      6.1.1.3.5  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

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



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

The binary loop is composed of 9 FP arithmetical operations:
 - 6: addition or subtraction
 - 3: multiply
The binary loop is loading 56 bytes (7 double precision FP elements).


      6.1.1.3.7  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.16 FP operations per loaded or stored byte.







      6.1.2  -  Loop 59 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/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.

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

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

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

Detected a slowdown caused by scalar integer instructions (typically used for address computation).
By removing them, you can lower the cost of an iteration from 4.00 to 3.00 cycles (1.33x speedup).

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



      6.1.2.1.2  -  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 4.00 to 0.44 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 ffast-math (included in Ofast) to extend loop vectorization to FP reductions.
 - Remove inter-iterations dependences from your loop and make it unit-stride:
  * If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly:
C storage order is row-major: for(i) for(j) a[j][i] = b[j][i]; (slow, non stride 1) => for(i) for(j) a[i][j] = b[i][j]; (fast, stride 1)<<image_row_maj>>
  * If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA):
for(i) a[i].x = b[i].x; (slow, non stride 1) => for(i) a.x[i] = b.x[i]; (fast, stride 1)



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

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


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



      6.1.2.1.4  -  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.


      6.1.2.1.5  -  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 56 bytes.
The binary loop is storing 56 bytes.


      6.1.2.1.6  -  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 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)







      6.1.3  -  Loop 106 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/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.

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

6% of peak computational performance is used (2.22 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is probably not vectorized.
Only 13% 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 4.50 to 2.00 cycles (2.25x speedup).

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


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



      6.1.3.1.2  -  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).


Workaround
Reduce the number of FP multiply/FMA instructions




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

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * use march=native
 - 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).





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

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



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

The binary loop is composed of 10 FP arithmetical operations:
 - 3: addition or subtraction
 - 6: multiply
 - 1: divide
The binary loop is loading 84 bytes (10 double precision FP elements).
The binary loop is storing 24 bytes (3 double precision FP elements).


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

Arithmetic intensity is 0.09 FP operations per loaded or stored byte.


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

Loop is potentially data access bound.

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







      6.1.4  -  Loop 91 from exec
  ==========================================================================================================

The loop is defined in:
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/intel/CoMD/build/CoMD/CoMD/src-openmp/mytype.h:23
 - /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/intel/CoMD/build/CoMD/CoMD/src-openmp/ljForce.c:158-161


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

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

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

      6.1.4.1.1  -  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 1.50 to 1.00 cycles (1.50x speedup).

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



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

Your loop is poorly vectorized.
Only 12% 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 1.50 to 0.19 cycles (8.00x speedup).

Details
33% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 33% of SSE/AVX stores 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)



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

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


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



      6.1.4.1.4  -  Vector unaligned load/store instructions
  ----------------------------------------------------------------------------------------------------------

Detected 1 suboptimal vector unaligned load/store instructions.


Details
 - MOVUPS: 1 occurrences<<list_path_1_vec_align_1>>


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


      6.1.4.1.5  -  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.


      6.1.4.1.6  -  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 storing 32 bytes.


      6.1.4.1.7  -  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)







      6.1.5  -  Loop 96 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/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/178-663-3083/intel/CoMD/build/CoMD/CoMD/src-openmp/ljForce.c:187.

Warnings:
Non-innermost loop: analyzing only self part (ignoring child loops).
This loop has 3 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
  ----------------------------------------------------------------------------------------------------------

Warnings:
This path is accessible from 3 CFG paths (including child blocks)

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

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

Your loop is not vectorized.
8 data elements could be processed at once in vector registers.
By vectorizing your loop, you can lower the cost of an iteration from 3.00 to 0.37 cycles (8.00x speedup).

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


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



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

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



No data for this section



      6.1.5.1.3  -  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.


      6.1.5.1.4  -  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 loading 16 bytes.







      6.1.6  -  Loop 87 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/intel/CoMD/build/CoMD/CoMD/src-openmp/linkCells.c:211-247,258-269,295-301,327-334,352-365,371-373.

The related source loop is not unrolled or unrolled with no peel/tail loop.
Warnings:
 - Ignoring paths for analysis
 - Too many paths. If you really need to analyze all of the 390 paths individually, rerun with max-paths=390
 - 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 390 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
  ----------------------------------------------------------------------------------------------------------

Warnings:
Detected a function call instruction: ignoring called function instructions.
Rerun with --follow-calls=append to include them to analysis  or with --follow-calls=inline to simulate inlining.
0% of peak computational performance is used (0.15 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 40.00 to 15.50 cycles (2.58x 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 12% 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 40.00 to 3.32 cycles (12.04x speedup).

Details
17% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 22% of SSE/AVX loads are used in vector version.
 - 20% of SSE/AVX stores are used in vector version.
 - 0% of SSE/AVX addition or subtraction instructions are used in vector version.
 - 0% of SSE/AVX multiply instructions are used in vector version.
 - 6% 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 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)



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

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




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

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * use march=native
 - 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).





      6.1.6.1.5  -  CALL instructions
  ----------------------------------------------------------------------------------------------------------

Detected function call instructions.


Details
Calling (and then returning from) a function prevents many compiler optimizations (like vectorization), breaks control flow (which reduces pipeline performance) and executes extra instructions to save/restore the registers used inside it, which is very expensive (dozens of cycles). Consider to inline small functions.
 - unknown: 3 occurrences<<list_path_1_call_1>>



      6.1.6.1.6  -  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.
 - CMOVBE: 1 occurrences<<list_path_1_complex_1>>
 - CVTTSD2SI: 3 occurrences<<list_path_1_complex_2>>
 - DEC: 1 occurrences<<list_path_1_complex_3>>
 - UCOMISD: 1 occurrences<<list_path_1_complex_4>>


Workaround
Pass to your compiler a micro-architecture specialization option:
 - use march=native



      6.1.6.1.7  -  Vector unaligned load/store instructions
  ----------------------------------------------------------------------------------------------------------

Detected 12 suboptimal vector unaligned load/store instructions.


Details
 - MOVUPD: 4 occurrences<<list_path_1_vec_align_1>>
 - MOVUPS: 8 occurrences<<list_path_1_vec_align_2>>


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


      6.1.6.1.8  -  Conversion instructions
  ----------------------------------------------------------------------------------------------------------

Detected expensive conversion instructions.

Details
 - CVTTSD2SI (FP64 to INT32/64, scalar): 3 occurrences<<list_path_1_cvt_1>>


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


      6.1.6.1.9  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

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



      6.1.6.1.10  -  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
 - 3: multiply
The binary loop is loading 572 bytes (71 double precision FP elements).
The binary loop is storing 248 bytes (31 double precision FP elements).


      6.1.6.1.11  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

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


      6.1.6.1.12  -  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 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)







      6.1.7  -  Loop 104 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/intel/CoMD/build/CoMD/CoMD/src-openmp/timestep.c:74-78.

It is main loop of related source loop which is unrolled by 2 (including vectorization).

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

4% of peak computational performance is used (1.33 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is poorly vectorized.
Only 17% of vector register length is used (average across all SSE/AVX instructions).
By fully vectorizing your loop, you can lower the cost of an iteration from 9.00 to 1.12 cycles (8.00x speedup).

Details
40% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 0% of SSE/AVX loads are used in vector version.
 - 40% 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 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  -  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 9.00 to 4.83 cycles (1.86x speedup).


Workaround
Reduce the number of FP multiply/FMA instructions




      6.1.7.1.3  -  FMA
  ----------------------------------------------------------------------------------------------------------

Presence of both ADD/SUB and MUL operations.

Workaround
 - Pass to your compiler a micro-architecture specialization option:
  * use march=native
 - 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).





      6.1.7.1.4  -  Vector unaligned load/store instructions
  ----------------------------------------------------------------------------------------------------------

Detected 9 suboptimal vector unaligned load/store instructions.


Details
 - MOVHPD: 6 occurrences<<list_path_1_vec_align_1>>
 - MOVUPD: 3 occurrences<<list_path_1_vec_align_2>>


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


      6.1.7.1.5  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

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



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

The binary loop is composed of 12 FP arithmetical operations:
 - 6: addition or subtraction
 - 6: multiply
The binary loop is loading 104 bytes (13 double precision FP elements).
The binary loop is storing 48 bytes (6 double precision FP elements).


      6.1.7.1.7  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

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







      6.1.8  -  Loop 95 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/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/178-663-3083/intel/CoMD/build/CoMD/CoMD/src-openmp/ljForce.c:178-184,187.

Warnings:
Non-innermost loop: analyzing only self part (ignoring child loops).
This loop has 4 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)


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

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

      6.1.8.1.1  -  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 3.00 to 0.22 cycles (13.87x speedup).

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


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



      6.1.8.1.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

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



No data for this section



      6.1.8.1.3  -  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.


      6.1.8.1.4  -  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 loading 40 bytes.
The binary loop is storing 8 bytes.




      6.1.8.2  -  Path 2
  ----------------------------------------------------------------------------------------------------------

Warnings:
This path is accessible from 3 CFG paths (including child blocks)

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

      6.1.8.2.1  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
Only 9% 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 4.33 to 0.31 cycles (13.87x speedup).

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


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



      6.1.8.2.2  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

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



No data for this section



      6.1.8.2.3  -  Slow data structures access
  ----------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown 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)



      6.1.8.2.4  -  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.


      6.1.8.2.5  -  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 72 bytes.
The binary loop is storing 16 bytes.







      6.1.9  -  Loop 60 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/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.

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

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

      6.1.9.1.1  -  Code clean check
  ----------------------------------------------------------------------------------------------------------

Detected a slowdown caused by scalar integer instructions (typically used for address computation).
By removing them, you can lower the cost of an iteration from 4.00 to 3.00 cycles (1.33x speedup).

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



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

Your loop is not vectorized.
Only 10% of vector register length is used (average across all SSE/AVX instructions).
By vectorizing your loop, you can lower the cost of an iteration from 4.00 to 0.44 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 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)



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

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


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



      6.1.9.1.4  -  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 56 bytes.
The binary loop is storing 56 bytes.


      6.1.9.1.6  -  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 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)







      6.1.10  -  Loop 45 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/intel/CoMD/build/CoMD/CoMD/src-openmp/haloExchange.c:380-389.

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

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

2% of peak computational performance is used (0.69 out of 32.00 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 4.33 to 3.17 cycles (1.37x 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 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 4.33 to 0.44 cycles (9.90x speedup).

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


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



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

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



No data for this section



      6.1.10.1.4  -  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).



      6.1.10.1.5  -  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).


      6.1.10.1.6  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

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


      6.1.10.1.7  -  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 with the unroll (resp. unroll_and_jam) directive on top of the inner (resp. surrounding) loop. You can enforce an unroll factor: #pragma unroll_and_jam N, unroll_and_jam(N), unroll N or unroll(N)





[MAQAO] Info: STOP THE REPORT GENERATION
[MAQAO] Info: 
[MAQAO] Info: If your application produces files, they can be found in directory "/beegfs/hackathon/users/eoseret/qaas_runs_test/178-663-3083/intel/CoMD/run/oneview_runs/defaults/aocc/oneview_run_1786633260"
[MAQAO] Info: 
