OV - exec - Loop 2414

	Loop Id: 2414	Module: exec	Source: par_csr_matop.c:3607-3608	Coverage: 0.12%

0x513184 CMP	%RSI,(%RBX,%RAX,8)    [1]

0x513188 JE	513660

0x51318e LEA	0x1(%RAX),%R10

0x513192 MOV	%R10,%RAX

0x513195 CMP	%RSI,(%RBX,%R10,8)    [1]

0x513199 JE	513660

0x51319f INC	%RAX

0x5131a2 CMP	%RSI,(%RBX,%RAX,8)    [1]

0x5131a6 JE	513660

0x5131ac LEA	0x2(%R10),%RAX

0x5131b0 CMP	%RSI,0x10(%RBX,%R10,8)    [1]

0x5131b5 JE	513660

0x5131bb LEA	0x3(%R10),%RAX

0x5131bf CMP	%RSI,0x18(%RBX,%R10,8)    [1]

0x5131c4 JE	513660

0x5131ca LEA	0x4(%R10),%RAX

0x5131ce CMP	%RSI,0x20(%RBX,%R10,8)    [1]

0x5131d3 JE	513660

0x5131d9 LEA	0x5(%R10),%RAX

0x5131dd CMP	%RSI,0x28(%RBX,%R10,8)    [1]

0x5131e2 JE	513660

0x5131e8 LEA	0x6(%R10),%RAX

0x5131ec CMP	%RSI,0x30(%RBX,%R10,8)    [1]

0x5131f1 JE	513660

0x5131f7 LEA	0x7(%R10),%RAX

0x5131fb CMP	%R14,%RAX

0x5131fe JNE	513184

/home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_mv/par_csr_matop.c: 3607 - 3608

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

3607:               for (j1 = send_map_starts_A[jk]; j1 < send_map_starts_A[jk+1]; j1++)

3608:                  if (send_map_elmts_A[j1] == ik)

Coverage (%)	Name	Source Location	Module
○97.55	gomp_thread_start	team.c:130	libgomp.so.1.0.0
○2.45	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	1.26
Bottlenecks	micro-operation queue,
Function	hypre_ParTMatmul._omp_fn.0
Source	par_csr_matop.c:3607-3608
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	4.50
P1 cycles	2.80
P2 cycles	2.67
P3 cycles	2.67
P4 cycles	0.00
P5 cycles	2.60
P6 cycles	4.50
P7 cycles	0.00
P8 cycles	0.00
P9 cycles	0.00
P10 cycles	2.60
P11 cycles	2.67
DIV/SQRT cycles	0.00
Inter-iter dependencies cycles	0
FE+BE cycles (UFS)	NA
Stall cycles (UFS)	NA
Nb insns	27.00
Nb uops	26.00
Nb loads	8.00
Nb stores	0.00
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	11.29
Bytes prefetched	0.00
Bytes loaded	64.00
Bytes stored	0.00
Stride 0	0.00
Stride 1	0.00
Stride n	0.00
Stride unknown	1.00
Stride indirect	0.00
Vectorization ratio all	0.00
Vectorization ratio load	0.00
Vectorization ratio store	NA
Vectorization ratio mul	NA
Vectorization ratio add_sub	NA
Vectorization ratio fma	NA
Vectorization ratio div_sqrt	NA
Vectorization ratio other	0.00
Vector-efficiency ratio all	12.50
Vector-efficiency ratio load	12.50
Vector-efficiency ratio store	NA
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	12.50

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	1.26
Bottlenecks	micro-operation queue,
Function	hypre_ParTMatmul._omp_fn.0
Source	par_csr_matop.c:3607-3608
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	4.50
P1 cycles	2.80
P2 cycles	2.67
P3 cycles	2.67
P4 cycles	0.00
P5 cycles	2.60
P6 cycles	4.50
P7 cycles	0.00
P8 cycles	0.00
P9 cycles	0.00
P10 cycles	2.60
P11 cycles	2.67
DIV/SQRT cycles	0.00
Inter-iter dependencies cycles	0
FE+BE cycles (UFS)	NA
Stall cycles (UFS)	NA
Nb insns	27.00
Nb uops	26.00
Nb loads	8.00
Nb stores	0.00
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	11.29
Bytes prefetched	0.00
Bytes loaded	64.00
Bytes stored	0.00
Stride 0	0.00
Stride 1	0.00
Stride n	0.00
Stride unknown	1.00
Stride indirect	0.00
Vectorization ratio all	0.00
Vectorization ratio load	0.00
Vectorization ratio store	NA
Vectorization ratio mul	NA
Vectorization ratio add_sub	NA
Vectorization ratio fma	NA
Vectorization ratio div_sqrt	NA
Vectorization ratio other	0.00
Vector-efficiency ratio all	12.50
Vector-efficiency ratio load	12.50
Vector-efficiency ratio store	NA
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	12.50

Path /

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

Function	hypre_ParTMatmul._omp_fn.0
Source file and lines	par_csr_matop.c:3607-3608
Module	exec

The loop is defined in /home/eoseret/qaas_runs_GNR/173-927-0874/intel/AMG/build/AMG/AMG/parcsr_mv/par_csr_matop.c:3607-3608.

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

Slow data structures access

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

Details

Constant unknown stride: 1 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)

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 64 bytes.

Unroll opportunity

Loop is potentially data access bound.

Workaround

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

General properties

nb instructions	27
nb uops	26
loop length	124
used x86 registers	5
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	4.50	2.80	2.67	2.67	0.00	2.60	4.50	0.00	0.00	0.00	2.60	2.67
cycles	4.50	2.80	2.67	2.67	0.00	2.60	4.50	0.00	0.00	0.00	2.60	2.67

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)	0.00

Cycles summary

Front-end	5.67
Dispatch	4.50
Data deps.	0.00
Overall L1	5.67

Vectorization ratios

all	0%
load	0%
store	NA (no store vectorizable/vectorized instructions)
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	0%

Vector efficiency ratios

all	12%
load	12%
store	NA (no store vectorizable/vectorized instructions)
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	12%

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:

5% of peak load performance is reached (11.29 out of 192.00 bytes loaded 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 4.50 cycles (1.26x speedup).

ASM code

In the binary file, the address of the loop is: 513184

Instruction	Nb FU	P0	P1	P2	P3	P5	P6	P10	P11	Latency	Recip. throughput	Vectorization
CMP %RSI,(%RBX,%RAX,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x1(%RAX),%R10	1	0	0	0	0	0	0	0	0	0	0.17	N/A
MOV %R10,%RAX	1	0	0	0	0	0	0	0	0	1	0.17	N/A
CMP %RSI,(%RBX,%R10,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
INC %RAX	1	0	0	0	0	0	0	0	0	1	0.17	N/A
CMP %RSI,(%RBX,%RAX,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x2(%R10),%RAX	1	0	0	0	0	0	0	0	0	0	0.17	N/A
CMP %RSI,0x10(%RBX,%R10,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x3(%R10),%RAX	1	0	0	0	0	0	0	0	0	0	0.17	N/A
CMP %RSI,0x18(%RBX,%R10,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x4(%R10),%RAX	1	0	0	0	0	0	0	0	0	0	0.17	N/A
CMP %RSI,0x20(%RBX,%R10,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x5(%R10),%RAX	1	0	0	0	0	0	0	0	0	0	0.17	N/A
CMP %RSI,0x28(%RBX,%R10,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x6(%R10),%RAX	1	0	0	0	0	0	0	0	0	0	0.17	N/A
CMP %RSI,0x30(%RBX,%R10,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x7(%R10),%RAX	1	0	0	0	0	0	0	0	0	0	0.17	N/A
CMP %R14,%RAX	1	0.20	0.20	0	0	0.20	0.20	0.20	0	1	0.20	N/A
JNE 513184 <hypre_ParTMatmul._omp_fn.0+0x814>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A

Function	hypre_ParTMatmul._omp_fn.0
Source file and lines	par_csr_matop.c:3607-3608
Module	exec

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

Slow data structures access

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

Details

Constant unknown stride: 1 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)

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 64 bytes.

Unroll opportunity

Loop is potentially data access bound.

Workaround

General properties

nb instructions	27
nb uops	26
loop length	124
used x86 registers	5
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	4.50	2.80	2.67	2.67	0.00	2.60	4.50	0.00	0.00	0.00	2.60	2.67
cycles	4.50	2.80	2.67	2.67	0.00	2.60	4.50	0.00	0.00	0.00	2.60	2.67

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)	0.00

Cycles summary

Front-end	5.67
Dispatch	4.50
Data deps.	0.00
Overall L1	5.67

Vectorization ratios

all	0%
load	0%
store	NA (no store vectorizable/vectorized instructions)
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	0%

Vector efficiency ratios

all	12%
load	12%
store	NA (no store vectorizable/vectorized instructions)
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	12%

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:

5% of peak load performance is reached (11.29 out of 192.00 bytes loaded per cycle (GB/s @ 1GHz))

Front-end bottlenecks

ASM code

In the binary file, the address of the loop is: 513184

Instruction	Nb FU	P0	P1	P2	P3	P5	P6	P10	P11	Latency	Recip. throughput	Vectorization
CMP %RSI,(%RBX,%RAX,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x1(%RAX),%R10	1	0	0	0	0	0	0	0	0	0	0.17	N/A
MOV %R10,%RAX	1	0	0	0	0	0	0	0	0	1	0.17	N/A
CMP %RSI,(%RBX,%R10,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
INC %RAX	1	0	0	0	0	0	0	0	0	1	0.17	N/A
CMP %RSI,(%RBX,%RAX,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x2(%R10),%RAX	1	0	0	0	0	0	0	0	0	0	0.17	N/A
CMP %RSI,0x10(%RBX,%R10,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x3(%R10),%RAX	1	0	0	0	0	0	0	0	0	0	0.17	N/A
CMP %RSI,0x18(%RBX,%R10,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x4(%R10),%RAX	1	0	0	0	0	0	0	0	0	0	0.17	N/A
CMP %RSI,0x20(%RBX,%R10,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x5(%R10),%RAX	1	0	0	0	0	0	0	0	0	0	0.17	N/A
CMP %RSI,0x28(%RBX,%R10,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x6(%R10),%RAX	1	0	0	0	0	0	0	0	0	0	0.17	N/A
CMP %RSI,0x30(%RBX,%R10,8)	1	0.20	0.20	0.33	0.33	0.20	0.20	0.20	0.33	1	0.33	scal (12.5%)
JE 513660 <hypre_ParTMatmul._omp_fn.0+0xcf0>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A
LEA 0x7(%R10),%RAX	1	0	0	0	0	0	0	0	0	0	0.17	N/A
CMP %R14,%RAX	1	0.20	0.20	0	0	0.20	0.20	0.20	0	1	0.20	N/A
JNE 513184 <hypre_ParTMatmul._omp_fn.0+0x814>	1	0.50	0	0	0	0	0.50	0	0	0	0.50	N/A

Report Configuration

Vectorization

Details

Workaround

Execution units bottlenecks

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

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