	*************************************************
	*                                               *
	*          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/178-654-5529/intel/HACCmk/run/oneview_runs/compilers/icx_3/oneview_results_1786546870 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/178-654-5529/intel/HACCmk/run/oneview_runs/compilers/icx_3/oneview_results_1786546870


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


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

  Application:			/beegfs/hackathon/users/eoseret/qaas_runs_test/178-654-5529/intel/HACCmk/run/binaries/icx_3/exec
  Timestamp:			2026-08-12 17:01:10
  Universal Timestamp:		1786546870
  Experiment Type:		MPI; OpenMP; Throughput; 
  Machine:			isix06.benchmarkcenter.megware.com
  Architecture:			x86_64
  Micro Architecture:		GRANITE_RAPIDS
  Model Name:			Intel(R) Xeon(R) 6972P
  Cache Size:			491520 KB
  Number of Cores:		96
  OS Version:			Linux 5.14.0-687.31.1.el9_8.x86_64 #1 SMP PREEMPT_DYNAMIC Sat Aug 1 05:38:01 EDT 2026
  Compilation Options:		
		exec:  --intel -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-654-5529/intel/HACCmk/build/HACCmk/CoMD/src-openmp -I /beegfs/hackathon/users/eoseret/qaas_runs_test/178-654-5529/intel/HACCmk/build/icx_3 -O3 -fargument-noalias -qopt-report=3 -O3 -x GRANITERAPIDS -mprefer-vector-width=256 -g -fno-omit-frame-pointer -fcf-protection=none -no-pie -grecord-command-line -fiopenmp -MD -MT CMakeFiles/HACCmk.dir/src/Step10_orig.c.o -MF CMakeFiles/HACCmk.dir/src/Step10_orig.c.o.d -o CMakeFiles/HACCmk.dir/src/Step10_orig.c.o -c /beegfs/hackathon/users/eoseret/qaas_runs_test/178-654-5529/intel/HACCmk/build/HACCmk/src/Step10_orig.c -fveclib=SVML 
  Number of processes observed:	1
  Number of threads observed:	192
  MAQAO version:		2026.1.0
  MAQAO build:			6d1be1d51c1e63266254997eb301734a7264775d::20260810-150026




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

  Total Time:				8.81 s
  Max (Thread Active Time):		8.61 s
  Average Active Time:			8.49 s
  Activity Ratio:			99.5 %
  Average number of active threads:	185.033
  Affinity Stability:			98.9 %
  Time spent in analyzed loops:		79.2 %
  Time spent in analyzed innermost loops: 79.2 %
  Time spent in user code:		79.2 %
  Compilation Options Score:		100
  Array Access Efficiency:		100 %

   Potential Speedups
  ----------------------------------------------------
  Perfect Flow Complexity:		1.00
  Perfect OpenMP/MPI/Pthread/TBB:	1.03
  Perfect OpenMP/MPI/Pthread/TBB + Load Distribution:	1.27
  If No Scalar Integer:
      Potential Speedup:		1.00
      Nb Loops to get 80%:		1
  If FP Vectorized:
      Potential Speedup:		1.00
      Nb Loops to get 80%:		2
  If Fully Vectorized:
      Potential Speedup:		1.00
      Nb Loops to get 80%:		2
  If Only FP Arithmetic:
      Potential Speedup:		1.00
      Nb Loops to get 80%:		1




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

  If No Scalar Integer:
      Number of loops   | 1      | 1      | 3      | 3      | 5      | 
      Cumulated Speedup | 1.0004 | 1.0004 | 1.0004 | 1.0004 | 1.0004 | 
  Top 5 loops:
    exec - 10:	1.0004
    exec - 7:	1.0004
    exec - 6:	1.0004
    exec - 9:	1.0004
    exec - 11:	1.0004

  If FP Vectorized:
      Number of loops   | 1      | 1      | 3      | 3      | 5      | 
      Cumulated Speedup | 1.0004 | 1.0004 | 1.0006 | 1.0006 | 1.0006 | 
  Top 5 loops:
    exec - 10:	1.0004
    exec - 9:	1.0005
    exec - 6:	1.0006
    exec - 7:	1.0006
    exec - 11:	1.0006

  If Fully Vectorized:
      Number of loops   | 1      | 1      | 3      | 3      | 5      | 
      Cumulated Speedup | 1.0012 | 1.0012 | 1.0017 | 1.0017 | 1.0018 | 
  Top 5 loops:
    exec - 10:	1.0012
    exec - 9:	1.0015
    exec - 6:	1.0017
    exec - 7:	1.0018
    exec - 11:	1.0018

  If Only FP Arithmetic:
      Number of loops   | 1      | 1      | 3      | 3      | 5      | 
      Cumulated Speedup | 1.0009 | 1.0009 | 1.0010 | 1.0010 | 1.0010 | 
  Top 5 loops:
    exec - 10:	1.0009
    exec - 7:	1.001
    exec - 6:	1.001
    exec - 9:	1.001
    exec - 11:	1.001



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


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

  [0 / 4] Application profile is too short (8.61 s)
If the overall application profiling time is less than 10 seconds, many of the measurements at function or loop level
will very likely be under the measurement quality threshold (0,1 seconds).
 Rerun to increase runtime duration: for example use a larger dataset or include a repetition loop.

  [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.

  [2.9997677254867 / 3] Most of time spent in analyzed modules (99.99%) comes from functions compiled with architecture specialization option 
-x GRANITERAPIDS


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

  [3 / 3] Optimization level option is correctly used


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


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

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



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

  [4 / 4] Enough time of the experiment time spent in analyzed loops (79.21%)
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 96.37% of observed threads are actually active 

  [4 / 4] CPU activity is good
CPU cores are active 99.46% of time

  [4 / 4] Loop profile is not flat
At least one loop coverage is greater than 4% (79.01%), representing an hotspot for the application

  [4 / 4] Enough time of the experiment time spent in analyzed innermost loops (79.21%)
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 (98.92%)
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 (18.22%). 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 (0.00%) lower than cumulative innermost loop coverage (79.21%)
Having cumulative Outermost/In between loops coverage greater than cumulative innermost loop coverage will make loop
optimization more complex

  [2 / 2] Less than 10% (0.00%) is spend in BLAS2 operations
BLAS2 calls usually could make a poor cache usage and could benefit from inlining.

  [2 / 2] Less than 10% (0.00%) is spend in Libm/SVML (special functions)



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

  Top 5 loops:
   + exec - 11  :
     analysis: Execution Time: 79 % - Vectorization Ratio: 100.00 % - Vector Length Use: 45.63 %
     Loop Computation Issues: 16
        [16] [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 4
            issues (= instructions) costing 4 points each.
     Data Access Issues: 3
        [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
        [3] [SA] Presence of special instructions executing on a single port (INSERT/EXTRACT) - Simplify data access
            and try to get stride 1 access. There are 3 issues (= instructions) costing 1 point each.
     Inefficient Vectorization: 5
        [3] [SA] Presence of special instructions executing on a single port (INSERT/EXTRACT) - Simplify data access
            and try to get stride 1 access. There are 3 issues (= instructions) costing 1 point each.
        [2] [SA] Inefficient vectorization: use of masked instructions - Simplify control structure. The issue costs 2
            points.

   + exec - 10  :
     analysis: Execution Time: 0 % - Vectorization Ratio: 0.00 % - Vector Length Use: 6.82 %
     Loop Computation Issues: 2
        [2] [SA] Presence of a large number of scalar integer instructions - Simplify loop structure, perform loop
            splitting or perform unroll and jam. This issue costs 2 points.
     Control Flow Issues: 1
        [1] [SA] Presence of calls - Inline either by compiler or by hand and use SVML for libm calls. There are 1
            issues (= calls) costing 1 point each.
     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: 1
        [1] [SA] Presence of calls - Inline either by compiler or by hand and use SVML for libm calls. There are 1
            issues (= calls) costing 1 point each.

   + exec - 9   :
     analysis: Execution Time: 0 % - Vectorization Ratio: 0.00 % - Vector Length Use: 6.25 %
     Loop Computation Issues: 4
        [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 - 6   :
     analysis: Execution Time: 0 % - Vectorization Ratio: 33.33 % - Vector Length Use: 12.50 %
     Loop Computation Issues: 4
        [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.
     Data Access Issues: 8
        [8] [SA] Presence of special instructions executing on a single port (INSERT/EXTRACT) - Simplify data access
            and try to get stride 1 access. There are 8 issues (= instructions) costing 1 point each.
     Inefficient Vectorization: 8
        [8] [SA] Presence of special instructions executing on a single port (INSERT/EXTRACT) - Simplify data access
            and try to get stride 1 access. There are 8 issues (= instructions) costing 1 point each.

   + exec - 7   :
     analysis: Execution Time: 0 % - Vectorization Ratio: 80.00 % - Vector Length Use: 41.25 %
     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: 1
        [1] [SA] Presence of special instructions executing on a single port (BROADCAST) - Simplify data access and
            try to get stride 1 access. There are 1 issues (= instructions) costing 1 point each.
     Inefficient Vectorization: 1
        [1] [SA] Presence of special instructions executing on a single port (BROADCAST) - Simplify data access and
            try to get stride 1 access. There are 1 issues (= instructions) costing 1 point each.



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


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

   Category | IO     | Exe    | System  | Others  | Memory | String | MPI   | TBB   | OMP   | Pthread | Math  |
  ----------+--------+--------+---------+---------+--------+--------+-------+-------+-------+---------+-------+
   Time (%) | 0.00   | 79.23  | 0.74    | 0.00    | 0.01   | 0.00   | 0.00  | 0.00  | 20.03 | 0.00    | 0.00  |




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

   Buckets                    | Nb Functions              | Coverage                  | Cumulated Coverage        |
  ----------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                       | 2                         | 98.67                     | 98.67                     |
   4% to 8%                   | 0                         | 0.00                      | 98.67                     |
   2% to 4%                   | 0                         | 0.00                      | 98.67                     |
   1% to 2%                   | 0                         | 0.00                      | 98.67                     |
   0.5% to 1%                 | 1                         | 0.66                      | 99.33                     |
   0.25% to 0.5%              | 1                         | 0.28                      | 99.60                     |
   0.125% to 0.25%            | 1                         | 0.13                      | 99.73                     |
   < 0.125%                   | 25                        | 0.27                      | 100.00                    |




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

   Buckets                    | Nb Loops                  | Coverage                  | Cumulated Coverage        |
  ----------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                       | 1                         | 79.01                     | 79.01                     |
   4% to 8%                   | 0                         | 0.00                      | 79.01                     |
   2% to 4%                   | 0                         | 0.00                      | 79.01                     |
   1% to 2%                   | 0                         | 0.00                      | 79.01                     |
   0.5% to 1%                 | 0                         | 0.00                      | 79.01                     |
   0.25% to 0.5%              | 0                         | 0.00                      | 79.01                     |
   0.125% to 0.25%            | 1                         | 0.13                      | 79.14                     |
   < 0.125%                   | 3                         | 0.07                      | 79.21                     |


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


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

   Function                                               | Module              | Coverage (%)   | Time (s)       |
  --------------------------------------------------------+---------------------+----------------+----------------+
   Step10_orig                                            | exec                | 79.02          | 6.71           |
   kmp_flag_64<false, true>::wait(kmp_info*, int, void*)  | libiomp5.so         | 19.65          | 1.67           |
   unknown_kernel_region                                  | kernel              | 0.66           | 0.06           |
   kmp_flag_native<unsigned long long, (flag_type)1, t... | libiomp5.so         | 0.28           | 0.02           |
   main.extracted.9                                       | exec                | 0.13           | 0.01           |
   __GI___sched_yield                                     | libc.so.6           | 0.09           | 0.01           |
   main                                                   | exec                | 0.07           | 1.21           |
   __kmp_hyper_barrier_release(barrier_type, kmp_info*... | libiomp5.so         | 0.03           | 0.01           |
   __kmp_yield                                            | libiomp5.so         | 0.02           | 0.01           |
   __kmp_hyper_barrier_gather(barrier_type, kmp_info*,... | libiomp5.so         | 0.01           | 0.01           |


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


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

   Loop Id        | Module              | Source Location                                        | Coverage (%)   |
  ----------------+---------------------+--------------------------------------------------------+----------------+
   11             | exec                | Step10_orig.c:19-35                                    | 79.01          |
   10             | exec                | main.c:143-150                                         | 0.13           |
   9              | exec                | main.c:114-116                                         | 0.03           |
   6              | exec                | main.c:114-118                                         | 0.02           |
   7              | exec                | main.c:114-119                                         | 0.02           |
   4              | exec                | main.c:77-77,main.c:112-114                            | 0.00           |





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


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





      6.1.1  -  Loop 11 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-654-5529/intel/HACCmk/build/HACCmk/src/Step10_orig.c:19-35.

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

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

11% of peak computational performance is used (7.29 out of 64.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is vectorized, but using 45% register length (average across all SSE/AVX instructions).


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



      6.1.1.1.2  -  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 34.00 to 15.00 cycles (2.27x 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





      6.1.1.1.3  -  512-bits vectorization
  ----------------------------------------------------------------------------------------------------------

On some x86 processors supporting 512-bits vectorization, compilers are often too conservative and limit vectorization to 256 bits. Performance can then be improved by enforcing 512-bits vectorization, especially with many vectorized and high trip count loops. 512-bits vectorization performance overhead (compared to 256-bits) is generally lower on newer processors.


Workaround
Recompile with -mprefer-vector-width=512


      6.1.1.1.4  -  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 34.00 to 15.00 cycles (2.27x speedup).


      6.1.1.1.5  -  Masked instructions
  ----------------------------------------------------------------------------------------------------------

Detected masked instructions.

Details
Vector registers are partially exploited, which is expected if your loop is irregular or mixes elements of different sizes.

Workaround
If your loop is irregular, try to remove or hoist conditional structures out of your loop. If it mixes elements of different sizes, try to uniformize them.


      6.1.1.1.6  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 88 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).




      6.1.1.1.7  -  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.
 - VCVTPD2PS: 2 occurrences<<list_path_1_complex_1>>
 - VCVTPS2PD: 4 occurrences<<list_path_1_complex_2>>



      6.1.1.1.8  -  Vector unaligned load/store instructions
  ----------------------------------------------------------------------------------------------------------

Detected 4 optimal vector unaligned load/store instructions.


Details
 - VMOVUPS: 4 occurrences<<list_path_1_vec_align_1>>


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


      6.1.1.1.9  -  Conversion instructions
  ----------------------------------------------------------------------------------------------------------

Detected expensive conversion instructions.

Details
 - VCVTPD2PS (FP64 to FP32, SIMD): 2 occurrences<<list_path_1_cvt_1>>
 - VCVTPS2PD (FP32 to FP64, SIMD): 4 occurrences<<list_path_1_cvt_2>>


Workaround
Avoid mixing data with different types. In particular, check if the type of constants is the same as array elements. Use double instead of single precision only when/where needed by numerical stability and avoid mixing precision. In C/C++, FP constants are double precision by default and must be suffixed by 'f' to make them single precision.


      6.1.1.1.10  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

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



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

The binary loop is composed of 248 FP arithmetical operations:
 - 120: addition or subtraction (88 inside FMA instructions)
 - 112: multiply (88 inside FMA instructions)
 - 8: divide
 - 8: square root
The binary loop is loading 128 bytes (32 single precision FP elements).


      6.1.1.1.12  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 1.94 FP operations per loaded or stored byte.







      6.1.2  -  Loop 10 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-654-5529/intel/HACCmk/build/HACCmk/src/main.c:143-150.

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

      6.1.2.1  -  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.
1% of peak computational performance is used (1.09 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 5.50 to 4.00 cycles (1.37x speedup).

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



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

Your loop is not vectorized.
Only 6% 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 5.50 to 0.36 cycles (15.48x 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
  ----------------------------------------------------------------------------------------------------------

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




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

Detected 3 FMA (fused multiply-add) operations.




      6.1.2.1.5  -  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.
 - Step10_orig: 1 occurrences<<list_path_1_call_1>>



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

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



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

The binary loop is composed of 6 FP arithmetical operations:
 - 3: addition or subtraction (all inside FMA instructions)
 - 3: multiply (all inside FMA instructions)
The binary loop is loading 48 bytes (12 single precision FP elements).
The binary loop is storing 12 bytes (3 single precision FP elements).


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

Arithmetic intensity is 0.10 FP operations per loaded or stored byte.


      6.1.2.1.9  -  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 9 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-654-5529/intel/HACCmk/build/HACCmk/src/main.c:114-116.

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

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

0% of peak computational performance is used (0.33 out of 64.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is not vectorized.
16 data elements could be processed at once in vector registers.
<<image_1x32_512>>By vectorizing your loop, you can lower the cost of an iteration from 24.00 to 1.50 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 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
  ----------------------------------------------------------------------------------------------------------

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



No data for this section



      6.1.3.1.3  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

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



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

The binary loop is composed of 8 FP arithmetical operations:
 - 8: addition or subtraction
The binary loop is storing 64 bytes (16 single precision FP elements).


      6.1.3.1.5  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

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







      6.1.4  -  Loop 6 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-654-5529/intel/HACCmk/build/HACCmk/src/main.c:114-118.

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

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

2% of peak computational performance is used (1.33 out of 64.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.4.1.1  -  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 24.00 to 2.74 cycles (8.75x speedup).

Details
33% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 0% 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)



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

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



No data for this section



      6.1.4.1.3  -  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.
 - VEXTRACTPS: 8 occurrences<<list_path_1_complex_1>>



      6.1.4.1.4  -  Type of elements and instruction set
  ----------------------------------------------------------------------------------------------------------

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



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

The binary loop is composed of 32 FP arithmetical operations:
 - 32: addition or subtraction
The binary loop is storing 64 bytes (16 single precision FP elements).


      6.1.4.1.6  -  Arithmetic intensity
  ----------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.50 FP operations per loaded or stored byte.







      6.1.5  -  Loop 7 from exec
  ==========================================================================================================

The loop is defined in /beegfs/hackathon/users/eoseret/qaas_runs_test/178-654-5529/intel/HACCmk/build/HACCmk/src/main.c:114-119.

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

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

16% of peak computational performance is used (10.67 out of 64.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.5.1.1  -  Code clean check
  ----------------------------------------------------------------------------------------------------------

Detected a slowdown caused by scalar integer instructions (typically used for address computation).
By removing them, you can lower the cost of an iteration from 1.50 to 1.33 cycles (1.12x speedup).

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



      6.1.5.1.2  -  Unrolling/vectorization cost
  ----------------------------------------------------------------------------------------------------------

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

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

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



      6.1.5.1.3  -  Vectorization
  ----------------------------------------------------------------------------------------------------------

Your loop is partially vectorized.
Only 41% of vector register length is used (average across all SSE/AVX instructions).


Details
80% of SSE/AVX instructions are used in vector version (process two or more data elements in vector registers):
 - 50% of SSE/AVX instructions that are not load, store, addition, subtraction nor multiply instructions are used in vector version.



      6.1.5.1.4  -  Execution units bottlenecks
  ----------------------------------------------------------------------------------------------------------

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




      6.1.5.1.5  -  FMA
  ----------------------------------------------------------------------------------------------------------

Detected 8 FMA (fused multiply-add) operations.




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

Detected 1 optimal vector unaligned load/store instructions.


Details
 - VMOVUPS: 1 occurrences<<list_path_1_vec_align_1>>


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


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

Detected expensive conversion instructions.

Details
 - VCVTDQ2PS (INT32 to FP32, SIMD): 1 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.


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

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



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

The binary loop is composed of 16 FP arithmetical operations:
 - 8: addition or subtraction (all inside FMA instructions)
 - 8: multiply (all inside FMA instructions)
The binary loop is loading 32 bytes (8 single precision FP elements).
The binary loop is storing 32 bytes (8 single precision FP elements).


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

Arithmetic intensity is 0.25 FP operations per loaded or stored byte.





[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/178-654-5529/intel/HACCmk/run/oneview_runs/compilers/icx_3/oneview_run_1786546870"
[MAQAO] Info: 
