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

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

* [MAQAO] Warning: Experiment directory /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/run/oneview_runs/defaults/gcc/oneview_results_1782198998 already exists and is reused.
           It can be replaced using --replace in the command line.
[MAQAO] Info: 
[MAQAO] Info: START THE APPLICATION PROFILING
[MAQAO] Info: -> RUNNING THE PROFILER...
[MAQAO] Info:   LPROF has already been run
[MAQAO] Info: STOP THE APPLICATION PROFILING
[MAQAO] Info: 
[MAQAO] Info: START FUNCTIONS AND LOOPS ANALYSIS ...
[MAQAO] Info: STOP FUNCTIONS AND LOOPS ANALYSIS ...
[MAQAO] Info: 
[MAQAO] Info: START THE REPORT GENERATION
[MAQAO] Info: -> ONE-VIEW EXPERIMENT DIRECTORY: /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/run/oneview_runs/defaults/gcc/oneview_results_1782198998


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


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

  Application:			/home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/run/base_runs/defaults/gcc/exec
  Timestamp:			2026-06-23 07:16:38
  Universal Timestamp:		1782198998
  Experiment Type:		MPI; OpenMP; Throughput; 
  Machine:			ip-172-31-38-240.ec2.internal
  Architecture:			aarch64
  Micro Architecture:		ARM_NEOVERSE_V1
  OS Version:			Linux 6.1.170-213.321.amzn2023.aarch64 #1 SMP Thu May 14 12:18:13 UTC 2026
  Compilation Options:		
		exec: GNU C++17 14.2.1 20250110 (Red Hat 14.2.1-7) -mlittle-endian -mabi=lp64 -mcpu=neoverse-v1+sm4+crc+aes+sha3+nossbs -g -O3 -O3 -std=c++17 -fno-omit-frame-pointer -fcf-protection=none -fopenmp 
  Number of processes observed:	1
  Number of threads observed:	64
  MAQAO version:		2026.0.0
  MAQAO build:			Build information not available




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

  Total Time:				136.88 s
  Max (Thread Active Time):		136.45 s
  Average Active Time:			132.76 s
  Activity Ratio:			97.0 %
  Average number of active threads:	62.073
  Affinity Stability:			98.1 %
  Time spent in analyzed loops:		97.9 %
  Time spent in analyzed innermost loops: 97.7 %
  Time spent in user code:		98.0 %
  Compilation Options Score:		75
  Array Access Efficiency:		46.6 %

   Potential Speedups
  ----------------------------------------------------
  Perfect Flow Complexity:		1.08
  Perfect OpenMP/MPI/Pthread/TBB:	1.00
  Perfect OpenMP/MPI/Pthread/TBB + Load Distribution:	1.03
  If No Scalar Integer:
      Potential Speedup:		1.14
      Nb Loops to get 80%:		4
  If FP Vectorized:
      Potential Speedup:		1.09
      Nb Loops to get 80%:		4
  If Fully Vectorized:
      Potential Speedup:		1.11
      Nb Loops to get 80%:		4
  If Only FP Arithmetic:
      Potential Speedup:		1.21
      Nb Loops to get 80%:		13




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

  If No Scalar Integer:
      Number of loops   | 1      | 9      | 19     | 28     | 38     | 
      Cumulated Speedup | 1.0328 | 1.1411 | 1.1411 | 1.1411 | 1.1411 | 
  Top 5 loops:
    exec - 259:	1.0328
    exec - 232:	1.0643
    exec - 178:	1.0907
    exec - 720:	1.115
    exec - 176:	1.138

  If FP Vectorized:
      Number of loops   | 1      | 9      | 19     | 28     | 38     | 
      Cumulated Speedup | 1.0209 | 1.0902 | 1.0902 | 1.0902 | 1.0902 | 
  Top 5 loops:
    exec - 232:	1.0209
    exec - 259:	1.0395
    exec - 178:	1.058
    exec - 176:	1.0733
    exec - 720:	1.0874

  If Fully Vectorized:
      Number of loops   | 1      | 9      | 19     | 28     | 38     | 
      Cumulated Speedup | 1.0242 | 1.1068 | 1.1070 | 1.1070 | 1.1070 | 
  Top 5 loops:
    exec - 259:	1.0242
    exec - 232:	1.0486
    exec - 178:	1.0721
    exec - 176:	1.0917
    exec - 720:	1.1064

  If Only FP Arithmetic:
      Number of loops   | 1      | 9      | 19     | 28     | 38     | 
      Cumulated Speedup | 1.0219 | 1.1291 | 1.2027 | 1.2106 | 1.2107 | 
  Top 5 loops:
    exec - 153:	1.0219
    exec - 200:	1.0356
    exec - 185:	1.0494
    exec - 188:	1.0632
    exec - 191:	1.0766



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


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

  [4 / 4] Application profile is long enough (136.45 s)
To have good quality measurements, it is advised that the application profiling time is greater than 10 seconds.

  [3 / 3] Most of time spent in analyzed modules comes from functions with source/debug info
-g option gives access to debugging informations, such are source locations.

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

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


  [2.9998035511194 / 3] Architecture specific option -mcpu is used


  [3 / 3] Optimization level option is correctly used


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

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


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


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

  [4 / 4] Enough time of the experiment time spent in analyzed loops (97.87%)
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 96.99% of observed threads are actually active 

  [4 / 4] CPU activity is good
CPU cores are active 97.00% of time

  [4 / 4] Loop profile is not flat
At least one loop coverage is greater than 4% (8.11%), representing an hotspot for the application

  [4 / 4] Enough time of the experiment time spent in analyzed innermost loops (97.74%)
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 (98.09%)
Threads are not migrating to CPU cores: probably successfully pinned

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

  [3 / 3] Functions mostly use all threads
Functions running on a reduced number of threads (typically sequential code) cover less than 10% of application
walltime (2.92%)

  [3 / 3] Cumulative Outermost/In between loops coverage (0.13%) lower than cumulative innermost loop coverage (97.74%)
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 - 311 :
     analysis: Execution Time: 8 % - Vectorization Ratio: 92.54 % - Vector Length Use: 100.00 %
     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.

   + exec - 153 :
     analysis: Execution Time: 7 % - Vectorization Ratio: 98.51 % - Vector Length Use: 100.00 %
     Loop Computation Issues: 4
        [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.
     Data Access Issues: 48
        [48] [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 24 issues ( = data accesses) costing 2
            point each.
     Vectorization Roadblocks: 48
        [48] [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 24 issues ( = data accesses) costing 2
            point each.

   + exec - 308 :
     analysis: Execution Time: 6 % - Vectorization Ratio: 84.06 % - Vector Length Use: 100.00 %
     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: 38
        [38] [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 19 issues ( = data accesses) costing 2
            point each.
     Vectorization Roadblocks: 38
        [38] [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 19 issues ( = data accesses) costing 2
            point each.

   + exec - 245 :
     analysis: Execution Time: 4 % - Vectorization Ratio: 91.67 % - Vector Length Use: 100.00 %
     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: 24
        [24] [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 12 issues ( = data accesses) costing 2
            point each.
     Vectorization Roadblocks: 24
        [24] [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 12 issues ( = data accesses) costing 2
            point each.

   + exec - 206 :
     analysis: Execution Time: 4 % - Vectorization Ratio: 100.00 % - Vector Length Use: 100.00 %
     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.
     Data Access Issues: 10
        [10] [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 5 issues ( = data accesses) costing 2
            point each.
     Vectorization Roadblocks: 10
        [10] [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 5 issues ( = data accesses) costing 2
            point each.



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


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

   Category | IO     | Exe    | Others  | TBB   | String | Pthread | MPI   | OMP   | System | Memory | Math  |
  ----------+--------+--------+---------+-------+--------+---------+-------+-------+--------+--------+-------+
   Time (%) | 0.00   | 97.99  | 0.00    | 0.00  | 0.00   | 0.00    | 0.00  | 0.34  | 1.65   | 0.01   | 0.00  |




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

   Buckets                   | Nb Functions              | Coverage                  | Cumulated Coverage        |
  ---------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                      | 1                         | 8.13                      | 8.13                      |
   4% to 8%                  | 8                         | 40.64                     | 48.77                     |
   2% to 4%                  | 15                        | 41.89                     | 90.66                     |
   1% to 2%                  | 5                         | 5.88                      | 96.54                     |
   0.5% to 1%                | 2                         | 1.88                      | 98.41                     |
   0.25% to 0.5%             | 3                         | 1.05                      | 99.47                     |
   0.125% to 0.25%           | 1                         | 0.24                      | 99.71                     |
   < 0.125%                  | 52                        | 0.19                      | 99.89                     |




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

   Buckets                   | Nb Loops                  | Coverage                  | Cumulated Coverage        |
  ---------------------------+---------------------------+---------------------------+---------------------------+
   > 8%                      | 1                         | 8.11                      | 8.11                      |
   4% to 8%                  | 8                         | 40.57                     | 48.68                     |
   2% to 4%                  | 15                        | 41.78                     | 90.46                     |
   1% to 2%                  | 5                         | 5.86                      | 96.33                     |
   0.5% to 1%                | 1                         | 0.99                      | 97.32                     |
   0.25% to 0.5%             | 1                         | 0.34                      | 97.66                     |
   0.125% to 0.25%           | 0                         | 0.00                      | 97.66                     |
   < 0.125%                  | 11                        | 0.08                      | 97.74                     |


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


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

   Function                                               | Module               | Coverage (%)  | Time (s)      |
  --------------------------------------------------------+----------------------+---------------+---------------+
   PdV_kernel(bool, int, int, int, int, double, clover... | exec                 | 8.13          | 10.79         |
   accelerate_kernel(int, int, int, int, double, clove... | exec                 | 7.26          | 9.64          |
   PdV_kernel(bool, int, int, int, int, double, clover... | exec                 | 6.41          | 8.50          |
   flux_calc_kernel(int, int, int, int, double, clover... | exec                 | 4.66          | 6.19          |
   advec_mom_kernel(int, int, int, int, clover::Buffer... | exec                 | 4.66          | 6.19          |
   advec_mom_kernel(int, int, int, int, clover::Buffer... | exec                 | 4.54          | 6.02          |
   ideal_gas_kernel(int, int, int, int, clover::Buffer... | exec                 | 4.53          | 6.01          |
   calc_dt_kernel(int, int, int, int, double, double, ... | exec                 | 4.52          | 6.00          |
   advec_cell_kernel(int, int, int, int, int, int, clo... | exec                 | 4.07          | 5.41          |
   advec_mom_kernel(int, int, int, int, clover::Buffer... | exec                 | 3.91          | 5.19          |


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


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

   Loop Id        | Module               | Source Location                                       | Coverage (%)  |
  ----------------+----------------------+-------------------------------------------------------+---------------+
   311            | exec                 | PdV.cpp:72-83                                         | 8.11          |
   153            | exec                 | context.h:69-69,accelerate.cpp:43-53                  | 7.25          |
   308            | exec                 | PdV.cpp:51-63                                         | 6.39          |
   245            | exec                 | flux_calc.cpp:39-40                                   | 4.65          |
   206            | exec                 | advec_mom.cpp:221-221                                 | 4.65          |
   197            | exec                 | advec_mom.cpp:149-149                                 | 4.53          |
   232            | exec                 | calc_dt.cpp:51-75,context.h:46-46,context.h:69-69     | 4.52          |
   259            | exec                 | ideal_gas.cpp:40-45,context.h:69-69                   | 4.52          |
   172            | exec                 | advec_cell.cpp:211-216                                | 4.06          |
   212            | exec                 | context.h:69-69,advec_mom.cpp:186-186,advec_mom.cp... | 3.90          |





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


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





      6.1.1  -  Loop 311 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/PdV.cpp:72-83.

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

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

8% of peak computational performance is used (2.63 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is highly vectorized.
100% of vector register length is used (average across all VPU instructions).


Details
92% of VPU instructions are used in vector version (process two or more data elements in vector registers):
 - 0% of VPU instructions that are not load, store, addition, subtraction nor multiply instructions are used in vector version.





      6.1.1.1.2  -  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 56.34 to 17.50 cycles (3.22x speedup).


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

Detected 12 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).




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

The binary loop is composed of 148 FP arithmetical operations:
 - 72: addition or subtraction (12 inside FMA instructions)
 - 60: multiply (12 inside FMA instructions)
 - 16: divide
The binary loop is loading 832 bytes.
The binary loop is storing 64 bytes.


      6.1.1.1.5  -  Arithmetic intensity
  ---------------------------------------------------------------------------------------------------------

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







      6.1.2  -  Loop 153 from exec
  =========================================================================================================

The loop is defined in:
 - /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/context.h:69
 - /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/accelerate.cpp:43-53


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

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

23% of peak computational performance is used (7.40 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is highly vectorized.
100% of vector register length is used (average across all VPU instructions).


Details
98% of VPU instructions are used in vector version (process two or more data elements in vector registers):
 - 0% of VPU instructions that are not load, store, addition, subtraction nor multiply instructions are used in vector version.





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

Detected 44 FMA (fused multiply-add) operations.
Presence of both ADD/SUB and MUL operations.

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




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

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

Details
 - Constant unknown stride: 24 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


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



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

The binary loop is composed of 148 FP arithmetical operations:
 - 76: addition or subtraction (44 inside FMA instructions)
 - 68: multiply (44 inside FMA instructions)
 - 4: divide
The binary loop is loading 1152 bytes.
The binary loop is storing 128 bytes.


      6.1.2.1.5  -  Arithmetic intensity
  ---------------------------------------------------------------------------------------------------------

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







      6.1.3  -  Loop 308 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/PdV.cpp:51-63.

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

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

9% of peak computational performance is used (2.91 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is partially vectorized.
100% of vector register length is used (average across all VPU instructions).


Details
84% of VPU instructions are used in vector version (process two or more data elements in vector registers):
 - 0% of VPU instructions that are not load, store, addition, subtraction nor multiply instructions are used in vector version.





      6.1.3.1.2  -  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 56.34 to 22.50 cycles (2.50x speedup).


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

Detected 12 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).




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

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

Details
 - Constant unknown stride: 19 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


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



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

The binary loop is composed of 164 FP arithmetical operations:
 - 72: addition or subtraction (12 inside FMA instructions)
 - 76: multiply (12 inside FMA instructions)
 - 16: divide
The binary loop is loading 576 bytes.
The binary loop is storing 64 bytes.


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

Arithmetic intensity is 0.26 FP operations per loaded or stored byte.







      6.1.4  -  Loop 245 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/flux_calc.cpp:39-40.

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

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

20% of peak computational performance is used (6.67 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is highly vectorized.
100% of vector register length is used (average across all VPU instructions).


Details
91% of VPU instructions are used in vector version (process two or more data elements in vector registers):
 - 0% of VPU instructions that are not load, store, addition, subtraction nor multiply instructions are used in vector version.





      6.1.4.1.2  -  FMA
  ---------------------------------------------------------------------------------------------------------

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).




      6.1.4.1.3  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 12 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


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



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

The binary loop is composed of 40 FP arithmetical operations:
 - 24: addition or subtraction
 - 16: multiply
The binary loop is loading 320 bytes.
The binary loop is storing 64 bytes.


      6.1.4.1.5  -  Arithmetic intensity
  ---------------------------------------------------------------------------------------------------------

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







      6.1.5  -  Loop 206 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/advec_mom.cpp:221.

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

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

3% of peak computational performance is used (1.14 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is fully vectorized, using full register length.


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





      6.1.5.1.2  -  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 14.08 to 3.00 cycles (4.69x speedup).


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

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).




      6.1.5.1.4  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 5 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


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



      6.1.5.1.5  -  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
 - 4: multiply
 - 4: divide
The binary loop is loading 160 bytes.
The binary loop is storing 32 bytes.


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

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







      6.1.6  -  Loop 197 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/advec_mom.cpp:149.

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

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

3% of peak computational performance is used (1.14 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

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

Your loop is fully vectorized, using full register length.


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





      6.1.6.1.2  -  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 14.08 to 3.00 cycles (4.69x speedup).


      6.1.6.1.3  -  FMA
  ---------------------------------------------------------------------------------------------------------

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).




      6.1.6.1.4  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 5 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


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



      6.1.6.1.5  -  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
 - 4: multiply
 - 4: divide
The binary loop is loading 160 bytes.
The binary loop is storing 32 bytes.


      6.1.6.1.6  -  Arithmetic intensity
  ---------------------------------------------------------------------------------------------------------

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







      6.1.7  -  Loop 232 from exec
  =========================================================================================================

The loop is defined in:
 - /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/calc_dt.cpp:51-75
 - /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/context.h:46,69


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


      6.1.7.1  -  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.
4% of peak computational performance is used (1.39 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.7.1.1  -  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 18.00 to 8.67 cycles (2.08x speedup).

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



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

Your loop is not vectorized.
Only 26% of vector register length is used (average across all VPU instructions).
By fully vectorizing your loop, you can lower the cost of an iteration from 18.00 to 8.74 cycles (2.06x speedup).

Details
5% of VPU instructions are used in vector version (process two or more data elements in vector registers):
 - 8% of VPU loads are used in vector version.
 - 33% of VPU stores are used in vector version.
 - 0% of VPU addition or subtraction instructions are used in vector version.
 - 0% of VPU multiply instructions are used in vector version.
 - 0% of VPU fused multiply-add instructions are used in vector version.
 - 0% of VPU divide and square root instructions are used in vector version.
 - 0% of VPU 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 fassociative-math (included in Ofast or ffast-math) 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)





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

Detected 1 FMA (fused multiply-add) operations.
Presence of both ADD/SUB and MUL operations.

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




      6.1.7.1.4  -  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: 1 occurrences<<list_path_1_call_1>>



      6.1.7.1.5  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 12 occurrence(s)
 - Irregular (variable stride) or indirect: 8 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)
 - Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.



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

The binary loop is composed of 25 FP arithmetical operations:
 - 11: addition or subtraction (1 inside FMA instructions)
 - 9: multiply (1 inside FMA instructions)
 - 5: divide
The binary loop is loading 3 bytes.
The binary loop is storing 3 bytes.


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

Arithmetic intensity is 4.17 FP operations per loaded or stored byte.




      6.1.7.2  -  Path 2
  ---------------------------------------------------------------------------------------------------------

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.
4% of peak computational performance is used (1.29 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.7.2.1  -  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 20.98 to 8.67 cycles (2.42x speedup).

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



      6.1.7.2.2  -  Vectorization
  ---------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
Only 26% of vector register length is used (average across all VPU instructions).
By fully vectorizing your loop, you can lower the cost of an iteration from 20.98 to 10.49 cycles (2.00x speedup).

Details
5% of VPU instructions are used in vector version (process two or more data elements in vector registers):
 - 8% of VPU loads are used in vector version.
 - 33% of VPU stores are used in vector version.
 - 0% of VPU addition or subtraction instructions are used in vector version.
 - 0% of VPU multiply instructions are used in vector version.
 - 0% of VPU fused multiply-add instructions are used in vector version.
 - 0% of VPU divide and square root instructions are used in vector version.
 - 0% of VPU 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 fassociative-math (included in Ofast or ffast-math) 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)





      6.1.7.2.3  -  Expensive FP math instructions/calls
  ---------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 20.98 to 18.00 cycles (1.17x speedup).


      6.1.7.2.4  -  FMA
  ---------------------------------------------------------------------------------------------------------

Detected 1 FMA (fused multiply-add) operations.
Presence of both ADD/SUB and MUL operations.

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




      6.1.7.2.5  -  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: 1 occurrences<<list_path_2_call_1>>



      6.1.7.2.6  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 12 occurrence(s)
 - Irregular (variable stride) or indirect: 8 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)
 - Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.



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

The binary loop is composed of 27 FP arithmetical operations:
 - 11: addition or subtraction (1 inside FMA instructions)
 - 10: multiply (1 inside FMA instructions)
 - 6: divide
The binary loop is loading 3 bytes.
The binary loop is storing 3 bytes.


      6.1.7.2.8  -  Arithmetic intensity
  ---------------------------------------------------------------------------------------------------------

Arithmetic intensity is 4.50 FP operations per loaded or stored byte.




      6.1.7.3  -  Path 3
  ---------------------------------------------------------------------------------------------------------

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

      6.1.7.3.1  -  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 21.23 to 6.83 cycles (3.11x speedup).

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



      6.1.7.3.2  -  Vectorization
  ---------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
4 data elements could be processed at once in vector registers.
By vectorizing your loop, you can lower the cost of an iteration from 21.23 to 10.61 cycles (2.00x speedup).

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


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with fassociative-math (included in Ofast or ffast-math) 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)





      6.1.7.3.3  -  Expensive FP math instructions/calls
  ---------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 21.23 to 14.83 cycles (1.43x speedup).


      6.1.7.3.4  -  FMA
  ---------------------------------------------------------------------------------------------------------

Detected 1 FMA (fused multiply-add) operations.
Presence of both ADD/SUB and MUL operations.

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




      6.1.7.3.5  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 13 occurrence(s)
 - Irregular (variable stride) or indirect: 7 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)
 - Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.



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

The binary loop is composed of 26 FP arithmetical operations:
 - 11: addition or subtraction (1 inside FMA instructions)
 - 9: multiply (1 inside FMA instructions)
 - 5: divide
 - 1: square root
The binary loop does not load or store any data.




      6.1.7.4  -  Path 4
  ---------------------------------------------------------------------------------------------------------

3% of peak computational performance is used (1.13 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.7.4.1  -  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 24.72 to 6.83 cycles (3.62x speedup).

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



      6.1.7.4.2  -  Vectorization
  ---------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
4 data elements could be processed at once in vector registers.
By vectorizing your loop, you can lower the cost of an iteration from 24.72 to 12.36 cycles (2.00x speedup).

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


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with fassociative-math (included in Ofast or ffast-math) 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)





      6.1.7.4.3  -  Expensive FP math instructions/calls
  ---------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 24.72 to 14.83 cycles (1.67x speedup).


      6.1.7.4.4  -  FMA
  ---------------------------------------------------------------------------------------------------------

Detected 1 FMA (fused multiply-add) operations.
Presence of both ADD/SUB and MUL operations.

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




      6.1.7.4.5  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 13 occurrence(s)
 - Irregular (variable stride) or indirect: 7 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)
 - Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.



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

The binary loop is composed of 28 FP arithmetical operations:
 - 11: addition or subtraction (1 inside FMA instructions)
 - 10: multiply (1 inside FMA instructions)
 - 6: divide
 - 1: square root
The binary loop does not load or store any data.







      6.1.8  -  Loop 259 from exec
  =========================================================================================================

The loop is defined in:
 - /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/ideal_gas.cpp:40-45
 - /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/context.h:69


The related source loop is not unrolled or unrolled with no peel/tail loop.
The structure of this loop is probably <if then [else] end>.

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
  ---------------------------------------------------------------------------------------------------------

Warnings:
 - The number of fused uops of the instruction [HINT	#0] is unknown
 - 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.

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

      6.1.8.1.1  -  Code clean check
  ---------------------------------------------------------------------------------------------------------

Detected a slowdown caused by scalar integer instructions (typically used for address computation).
By removing them, you can lower the cost of an iteration from 4.00 to 1.67 cycles (2.40x speedup).

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



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

Your loop is not vectorized.
Only 23% of vector register length is used (average across all VPU instructions).
By vectorizing your loop, you can lower the cost of an iteration from 4.00 to 1.75 cycles (2.29x speedup).

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


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with fassociative-math (included in Ofast or ffast-math) 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)





      6.1.8.1.3  -  FMA
  ---------------------------------------------------------------------------------------------------------

Detected 1 FMA (fused multiply-add) operations.




      6.1.8.1.4  -  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: 1 occurrences<<list_path_1_call_1>>



      6.1.8.1.5  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 1 occurrence(s)
 - Irregular (variable stride) or indirect: 4 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)
 - Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.



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

The binary loop is composed of 9 FP arithmetical operations:
 - 1: addition or subtraction (all inside FMA instructions)
 - 7: multiply (1 inside FMA instructions)
 - 1: divide
The binary loop is loading 3 bytes.
The binary loop is storing 3 bytes.


      6.1.8.1.7  -  Arithmetic intensity
  ---------------------------------------------------------------------------------------------------------

Arithmetic intensity is 1.50 FP operations per loaded or stored byte.




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

4% of peak computational performance is used (1.38 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.8.2.1  -  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 7.24 to 1.67 cycles (4.35x speedup).

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



      6.1.8.2.2  -  Vectorization
  ---------------------------------------------------------------------------------------------------------

Your loop is not vectorized.
4 data elements could be processed at once in vector registers.
By vectorizing your loop, you can lower the cost of an iteration from 7.24 to 3.62 cycles (2.00x speedup).

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


Workaround
 - Try another compiler or update/tune your current one:
  * recompile with fassociative-math (included in Ofast or ffast-math) 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)





      6.1.8.2.3  -  Expensive FP math instructions/calls
  ---------------------------------------------------------------------------------------------------------

Detected performance impact from expensive FP math instructions/calls.
By removing/reexpressing them, you can lower the cost of an iteration from 7.24 to 2.88 cycles (2.52x speedup).


      6.1.8.2.4  -  FMA
  ---------------------------------------------------------------------------------------------------------

Detected 1 FMA (fused multiply-add) operations.




      6.1.8.2.5  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 1 occurrence(s)
 - Irregular (variable stride) or indirect: 4 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)
 - Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.



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

The binary loop is composed of 10 FP arithmetical operations:
 - 1: addition or subtraction (all inside FMA instructions)
 - 7: multiply (1 inside FMA instructions)
 - 1: divide
 - 1: square root
The binary loop does not load or store any data.







      6.1.9  -  Loop 172 from exec
  =========================================================================================================

The loop is defined in /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/advec_cell.cpp:211-216.

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

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

4% of peak computational performance is used (1.42 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.9.1.1  -  Vectorization
  ---------------------------------------------------------------------------------------------------------

Your loop is highly vectorized.
100% of vector register length is used (average across all VPU instructions).


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





      6.1.9.1.2  -  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 28.17 to 5.50 cycles (5.12x speedup).


      6.1.9.1.3  -  FMA
  ---------------------------------------------------------------------------------------------------------

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).




      6.1.9.1.4  -  Slow data structures access
  ---------------------------------------------------------------------------------------------------------

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

Details
 - Constant unknown stride: 9 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)



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

The binary loop is composed of 40 FP arithmetical operations:
 - 24: addition or subtraction
 - 8: multiply
 - 8: divide
The binary loop is loading 288 bytes.
The binary loop is storing 64 bytes.


      6.1.9.1.6  -  Arithmetic intensity
  ---------------------------------------------------------------------------------------------------------

Arithmetic intensity is 0.11 FP operations per loaded or stored byte.







      6.1.10  -  Loop 212 from exec
  =========================================================================================================

The loop is defined in:
 - /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/context.h:69
 - /home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/build/CloverLeaf2.0-CXX/src/omp/advec_mom.cpp:186,197-211


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

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

3% of peak computational performance is used (1.28 out of 32.00 FLOP per cycle (GFLOPS @ 1GHz))

      6.1.10.1.1  -  Vectorization
  ---------------------------------------------------------------------------------------------------------

Your loop is partially vectorized.
95% of vector register length is used (average across all VPU instructions).


Details
72% of VPU instructions are used in vector version (process two or more data elements in vector registers):
 - 92% of VPU loads are used in vector version.
 - 46% of VPU instructions that are not load, store, addition, subtraction nor multiply instructions are used in vector version.





      6.1.10.1.2  -  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 112.68 to 46.00 cycles (2.45x speedup).


      6.1.10.1.3  -  FMA
  ---------------------------------------------------------------------------------------------------------

Detected 8 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).




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

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

Details
 - Constant unknown stride: 4 occurrence(s)
 - Irregular (variable stride) or indirect: 3 occurrence(s)
Non-unit stride (uncontiguous) accesses are not efficiently using data caches


Workaround
 - Try to reorganize arrays of structures to structures of arrays
 - Consider to permute loops (see vectorization gain report)
 - Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.



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

The binary loop is composed of 144 FP arithmetical operations:
 - 56: addition or subtraction (8 inside FMA instructions)
 - 56: multiply (8 inside FMA instructions)
 - 32: divide
The binary loop is loading 384 bytes.
The binary loop is storing 64 bytes.


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

Arithmetic intensity is 0.32 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 "/home/eoseret/qaas/qaas_runs/178-219-7589/intel/CloverLeaf2.0-CXX/run/oneview_runs/defaults/gcc/oneview_run_1782198998"
[MAQAO] Info: 
