OV - exec - Loop 105

0x42f2e0 MOV	(%R9),%R11    [2]

0x42f2e3 LOCK INCQ	(%RDI,%R11,8)    [1]

0x42f2e8 MOV	0x8(%R9),%R12    [2]

0x42f2ec LOCK INCQ	(%RDI,%R12,8)    [1]

0x42f2f1 MOV	0x10(%R9),%RBX    [2]

0x42f2f5 LOCK INCQ	(%RDI,%RBX,8)    [1]

0x42f2fa MOV	0x18(%R9),%RCX    [2]

0x42f2fe LOCK INCQ	(%RDI,%RCX,8)    [1]

0x42f303 MOV	0x20(%R9),%RSI    [2]

0x42f307 LOCK INCQ	(%RDI,%RSI,8)    [1]

0x42f30c MOV	0x28(%R9),%RAX    [2]

0x42f310 LOCK INCQ	(%RDI,%RAX,8)    [1]

0x42f315 MOV	0x30(%R9),%RDX    [2]

0x42f319 LOCK INCQ	(%RDI,%RDX,8)    [1]

0x42f31e MOV	0x38(%R9),%R8    [2]

0x42f322 LOCK INCQ	(%RDI,%R8,8)    [1]

0x42f327 ADD	$0x40,%R9

0x42f32b CMP	%R9,%R10

0x42f32e JNE	42f2e0

/home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c: 2135 - 2136

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

2135: #pragma omp atomic

2136:       measure_array_temp[S_diag_j[i]]++;

Coverage (%)	Name	Source Location	Module
○97.75	gomp_thread_start	team.c:130	libgomp.so.1.0.0
○2.25	GOMP_parallel	libgomp.h:982	libgomp.so.1.0.0

Path /

Metric	Value
CQA speedup if no scalar integer	1.00
CQA speedup if FP arith vectorized	1.00
CQA speedup if fully vectorized	8.00
CQA speedup if no inter-iteration dependency	NA
CQA speedup if next bottleneck killed	1.06
Bottlenecks	micro-operation queue,
Function	hypre_BoomerAMGCoarsenPMIS._omp_fn.2
Source	par_coarsen.c:2135-2136
Source loop unroll info	not unrolled or unrolled with no peel/tail loop
Source loop unroll confidence level	max
Unroll/vectorization loop type	NA
Unroll factor	NA
CQA cycles	5.67
CQA cycles if no scalar integer	5.67
CQA cycles if FP arith vectorized	5.67
CQA cycles if fully vectorized	0.71
Front-end cycles	5.67
P0 cycles	1.90
P1 cycles	1.80
P2 cycles	5.33
P3 cycles	5.33
P4 cycles	4.00
P5 cycles	1.80
P6 cycles	1.70
P7 cycles	4.00
P8 cycles	4.00
P9 cycles	4.00
P10 cycles	1.80
P11 cycles	5.33
DIV/SQRT cycles	0.00
Inter-iter dependencies cycles	1
FE+BE cycles (UFS)	NA
Stall cycles (UFS)	NA
Nb insns	19.00
Nb uops	34.00
Nb loads	16.00
Nb stores	8.00
Nb stack references	0.00
FLOP/cycle	0.00
Nb FLOP add-sub	0.00
Nb FLOP mul	0.00
Nb FLOP fma	0.00
Nb FLOP div	0.00
Nb FLOP rcp	0.00
Nb FLOP sqrt	0.00
Nb FLOP rsqrt	0.00
Bytes/cycle	33.88
Bytes prefetched	0.00
Bytes loaded	128.00
Bytes stored	64.00
Stride 0	0.00
Stride 1	1.00
Stride n	0.00
Stride unknown	0.00
Stride indirect	1.00
Vectorization ratio all	0.00
Vectorization ratio load	0.00
Vectorization ratio store	0.00
Vectorization ratio mul	NA
Vectorization ratio add_sub	NA
Vectorization ratio fma	NA
Vectorization ratio div_sqrt	NA
Vectorization ratio other	NA
Vector-efficiency ratio all	12.50
Vector-efficiency ratio load	12.50
Vector-efficiency ratio store	12.50
Vector-efficiency ratio mul	NA
Vector-efficiency ratio add_sub	NA
Vector-efficiency ratio fma	NA
Vector-efficiency ratio div_sqrt	NA
Vector-efficiency ratio other	NA

Metric	Value
CQA speedup if no scalar integer	1.00
CQA speedup if FP arith vectorized	1.00
CQA speedup if fully vectorized	8.00
CQA speedup if no inter-iteration dependency	NA
CQA speedup if next bottleneck killed	1.06
Bottlenecks	micro-operation queue,
Function	hypre_BoomerAMGCoarsenPMIS._omp_fn.2
Source	par_coarsen.c:2135-2136
Source loop unroll info	not unrolled or unrolled with no peel/tail loop
Source loop unroll confidence level	max
Unroll/vectorization loop type	NA
Unroll factor	NA
CQA cycles	5.67
CQA cycles if no scalar integer	5.67
CQA cycles if FP arith vectorized	5.67
CQA cycles if fully vectorized	0.71
Front-end cycles	5.67
P0 cycles	1.90
P1 cycles	1.80
P2 cycles	5.33
P3 cycles	5.33
P4 cycles	4.00
P5 cycles	1.80
P6 cycles	1.70
P7 cycles	4.00
P8 cycles	4.00
P9 cycles	4.00
P10 cycles	1.80
P11 cycles	5.33
DIV/SQRT cycles	0.00
Inter-iter dependencies cycles	1
FE+BE cycles (UFS)	NA
Stall cycles (UFS)	NA
Nb insns	19.00
Nb uops	34.00
Nb loads	16.00
Nb stores	8.00
Nb stack references	0.00
FLOP/cycle	0.00
Nb FLOP add-sub	0.00
Nb FLOP mul	0.00
Nb FLOP fma	0.00
Nb FLOP div	0.00
Nb FLOP rcp	0.00
Nb FLOP sqrt	0.00
Nb FLOP rsqrt	0.00
Bytes/cycle	33.88
Bytes prefetched	0.00
Bytes loaded	128.00
Bytes stored	64.00
Stride 0	0.00
Stride 1	1.00
Stride n	0.00
Stride unknown	0.00
Stride indirect	1.00
Vectorization ratio all	0.00
Vectorization ratio load	0.00
Vectorization ratio store	0.00
Vectorization ratio mul	NA
Vectorization ratio add_sub	NA
Vectorization ratio fma	NA
Vectorization ratio div_sqrt	NA
Vectorization ratio other	NA
Vector-efficiency ratio all	12.50
Vector-efficiency ratio load	12.50
Vector-efficiency ratio store	12.50
Vector-efficiency ratio mul	NA
Vector-efficiency ratio add_sub	NA
Vector-efficiency ratio fma	NA
Vector-efficiency ratio div_sqrt	NA
Vector-efficiency ratio other	NA

Path /

Average path: Display a virtual path defined by average values of all real paths

Function	hypre_BoomerAMGCoarsenPMIS._omp_fn.2
Source file and lines	par_coarsen.c:2135-2136
Module	exec

The loop is defined in /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135-2136.

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

gain
potential
hint
expert

Vectorization

Your loop is not vectorized. 8 data elements could be processed at once in vector registers. By vectorizing your loop, you can lower the cost of an iteration from 5.67 to 0.71 cycles (8.00x speedup).

Details

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

Workaround

Try another compiler or update/tune your current one
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)
- 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)

Execution units bottlenecks

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

No data for this section

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.

INC: 8 occurrences►
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135

Slow data structures access

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

Details

Irregular (variable stride) or indirect: 1 occurrence(s)

Non-unit stride (uncontiguous) accesses are not efficiently using data caches

Workaround

Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.

Type of elements and instruction set

No instructions are processing arithmetic or math operations on FP elements. This loop is probably writing/copying data or processing integer elements.

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

The binary loop does not contain any FP arithmetical operations. The binary loop is loading 128 bytes. The binary loop is storing 64 bytes.

Unroll opportunity

Loop is data access bound.

Workaround

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

General properties

nb instructions	19
nb uops	34
loop length	80
used x86 registers	11
used mmx registers	0
used xmm registers	0
used ymm registers	0
used zmm registers	0
nb stack references	0

Front-end

ASSUMED MACRO FUSION FIT IN UOP CACHE

micro-operation queue	5.67 cycles
front end	5.67 cycles

Back-end

	P0	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11
uops	1.90	1.80	5.33	5.33	4.00	1.80	1.70	4.00	4.00	4.00	1.80	5.33
cycles	1.90	1.80	5.33	5.33	4.00	1.80	1.70	4.00	4.00	4.00	1.80	5.33

Execution ports to units layout:

P0 (256 bits): VPU, BRU, ALU, DIV/SQRT
P1 (256 bits): ALU, VPU
P2 (512 bits): load
P3 (512 bits): load
P4 (512 bits): store data
P5 (512 bits): ALU, VPU
P6: ALU, BRU
P7: store address
P8: store address
P9 (512 bits): store data
P10: ALU
P11 (512 bits): load

Cycles executing div or sqrt instructions	NA
Longest recurrence chain latency (RecMII)	1.00

Cycles summary

Front-end	5.67
Dispatch	5.33
Data deps.	1.00
Overall L1	5.67

Vectorization ratios

all	0%
load	0%
store	0%
mul	NA (no mul vectorizable/vectorized instructions)
add-sub	NA (no add-sub vectorizable/vectorized instructions)
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	NA (no other vectorizable/vectorized instructions)

Vector efficiency ratios

all	12%
load	12%
store	12%
mul	NA (no mul vectorizable/vectorized instructions)
add-sub	NA (no add-sub vectorizable/vectorized instructions)
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	NA (no other vectorizable/vectorized instructions)

Cycles and memory resources usage

Assuming all data fit into the L1 cache, each iteration of the binary loop takes 5.67 cycles. At this rate:

11% of peak load performance is reached (22.59 out of 192.00 bytes loaded per cycle (GB/s @ 1GHz))
8% of peak store performance is reached (11.29 out of 128.00 bytes stored per cycle (GB/s @ 1GHz))

Front-end bottlenecks

Performance is limited by instruction throughput (loading/decoding program instructions to execution core) (front-end is a bottleneck). By removing all these bottlenecks, you can lower the cost of an iteration from 5.67 to 5.33 cycles (1.06x speedup).

ASM code

In the binary file, the address of the loop is: 42f2e0

Instruction	Nb FU	P0	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11	Latency	Recip. throughput	Vectorization
MOV (%R9),%R11	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%R11,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x8(%R9),%R12	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%R12,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x10(%R9),%RBX	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%RBX,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x18(%R9),%RCX	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%RCX,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x20(%R9),%RSI	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%RSI,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x28(%R9),%RAX	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%RAX,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x30(%R9),%RDX	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%RDX,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x38(%R9),%R8	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%R8,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
ADD $0x40,%R9	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
CMP %R9,%R10	1	0.20	0.20	0	0	0	0.20	0.20	0	0	0	0.20	0	1	0.20	N/A
JNE 42f2e0 <hypre_BoomerAMGCoarsenPMIS._omp_fn.2+0xf0>	1	0.50	0	0	0	0	0	0.50	0	0	0	0	0	0	0.50	N/A

Function	hypre_BoomerAMGCoarsenPMIS._omp_fn.2
Source file and lines	par_coarsen.c:2135-2136
Module	exec

The loop is defined in /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135-2136.

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

gain
potential
hint
expert

Vectorization

Your loop is not vectorized. 8 data elements could be processed at once in vector registers. By vectorizing your loop, you can lower the cost of an iteration from 5.67 to 0.71 cycles (8.00x speedup).

Details

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

Workaround

Try another compiler or update/tune your current one
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)
- 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)

Execution units bottlenecks

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

No data for this section

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.

INC: 8 occurrences►
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135
- /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_ls/par_coarsen.c:2135

Slow data structures access

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

Details

Irregular (variable stride) or indirect: 1 occurrence(s)

Non-unit stride (uncontiguous) accesses are not efficiently using data caches

Workaround

Try to remove indirect accesses. If applicable, precompute elements out of the innermost loop.

Type of elements and instruction set

No instructions are processing arithmetic or math operations on FP elements. This loop is probably writing/copying data or processing integer elements.

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

The binary loop does not contain any FP arithmetical operations. The binary loop is loading 128 bytes. The binary loop is storing 64 bytes.

Unroll opportunity

Loop is data access bound.

Workaround

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

General properties

nb instructions	19
nb uops	34
loop length	80
used x86 registers	11
used mmx registers	0
used xmm registers	0
used ymm registers	0
used zmm registers	0
nb stack references	0

Front-end

ASSUMED MACRO FUSION FIT IN UOP CACHE

micro-operation queue	5.67 cycles
front end	5.67 cycles

Back-end

	P0	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11
uops	1.90	1.80	5.33	5.33	4.00	1.80	1.70	4.00	4.00	4.00	1.80	5.33
cycles	1.90	1.80	5.33	5.33	4.00	1.80	1.70	4.00	4.00	4.00	1.80	5.33

Execution ports to units layout:

P0 (256 bits): VPU, BRU, ALU, DIV/SQRT
P1 (256 bits): ALU, VPU
P2 (512 bits): load
P3 (512 bits): load
P4 (512 bits): store data
P5 (512 bits): ALU, VPU
P6: ALU, BRU
P7: store address
P8: store address
P9 (512 bits): store data
P10: ALU
P11 (512 bits): load

Cycles executing div or sqrt instructions	NA
Longest recurrence chain latency (RecMII)	1.00

Cycles summary

Front-end	5.67
Dispatch	5.33
Data deps.	1.00
Overall L1	5.67

Vectorization ratios

all	0%
load	0%
store	0%
mul	NA (no mul vectorizable/vectorized instructions)
add-sub	NA (no add-sub vectorizable/vectorized instructions)
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	NA (no other vectorizable/vectorized instructions)

Vector efficiency ratios

all	12%
load	12%
store	12%
mul	NA (no mul vectorizable/vectorized instructions)
add-sub	NA (no add-sub vectorizable/vectorized instructions)
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	NA (no other vectorizable/vectorized instructions)

Cycles and memory resources usage

Assuming all data fit into the L1 cache, each iteration of the binary loop takes 5.67 cycles. At this rate:

11% of peak load performance is reached (22.59 out of 192.00 bytes loaded per cycle (GB/s @ 1GHz))
8% of peak store performance is reached (11.29 out of 128.00 bytes stored per cycle (GB/s @ 1GHz))

Front-end bottlenecks

Performance is limited by instruction throughput (loading/decoding program instructions to execution core) (front-end is a bottleneck). By removing all these bottlenecks, you can lower the cost of an iteration from 5.67 to 5.33 cycles (1.06x speedup).

ASM code

In the binary file, the address of the loop is: 42f2e0

Instruction	Nb FU	P0	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11	Latency	Recip. throughput	Vectorization
MOV (%R9),%R11	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%R11,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x8(%R9),%R12	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%R12,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x10(%R9),%RBX	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%RBX,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x18(%R9),%RCX	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%RCX,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x20(%R9),%RSI	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%RSI,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x28(%R9),%RAX	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%RAX,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x30(%R9),%RDX	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%RDX,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
MOV 0x38(%R9),%R8	1	0	0	0.33	0.33	0	0	0	0	0	0	0	0.33	1	0.33	N/A
LOCK INCQ (%RDI,%R8,8)	3	0.20	0.20	0.33	0.33	0.50	0.20	0.20	0.50	0.50	0.50	0.20	0.33	1	0.50	scal (12.5%)
ADD $0x40,%R9	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
CMP %R9,%R10	1	0.20	0.20	0	0	0	0.20	0.20	0	0	0	0.20	0	1	0.20	N/A
JNE 42f2e0 <hypre_BoomerAMGCoarsenPMIS._omp_fn.2+0xf0>	1	0.50	0	0	0	0	0	0.50	0	0	0	0	0	0	0.50	N/A

Report Configuration

Vectorization

Details

Workaround

Execution units bottlenecks

Complex instructions

Details

Slow data structures access

Details

Workaround

Type of elements and instruction set

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

Unroll opportunity

Workaround

General properties

Front-end

Back-end

Cycles summary

Vectorization ratios

Vector efficiency ratios

Cycles and memory resources usage

Front-end bottlenecks

ASM code

Vectorization

Details

Workaround

Execution units bottlenecks

Complex instructions

Details

Slow data structures access

Details

Workaround

Type of elements and instruction set

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

Unroll opportunity

Workaround

General properties

Front-end

Back-end

Cycles summary

Vectorization ratios

Vector efficiency ratios

Cycles and memory resources usage

Front-end bottlenecks

ASM code