OV - attention-armclang-native - Loop 48 - attention-armclang-native

0x12848 ADD	W9, W9, #1

0x1284c ADD	X8, X8, X23

0x12850 CMP	W9, W27

0x12854 B.EQ	12a04

0x12858 LDR	X13, [SP, #608]

0x1285c MUL	W11, W9, W23

0x12860 MUL	W12, W9, W27

0x12864 ORR	X10, XZR, XZR

0x12868 CMN	W11, W13

0x1286c LDR	W13, [SP, #404]

0x12870 CSINC	W13, W13, WZR, #3

0x12874 B	128a0

  (49) 0x12880 FCVT	S0, D0

  (49) 0x12884 LDR	X15, [SP, #544]

  (49) 0x12888 ADD	W14, W12, W10

  (49) 0x1288c ADD	X10, X10, #1

  (49) 0x12890 CMP	X10, X26

  (49) 0x12894 FDIV	S0, S0, S9

  (49) 0x12898 STR	S0, [X15, W14,UXTW #2]

  (49) 0x1289c B.EQ	12848

  (49) 0x128a0 CNTW	X15, ALL

  (49) 0x128a4 MOVI	D0, #0

  (49) 0x128a8 AND	X14, X23, #0x7fffffff

  (49) 0x128ac CMP	X14, X15

  (49) 0x128b0 B.CC	128c4

  (49) 0x128b4 LDR	X14, [SP, #608]

  (49) 0x128b8 CMN	W10, W14

  (49) 0x128bc CSINC	W14, W13, WZR, #3

  (49) 0x128c0 TBZ	W14, #0, 12984

  (49) 0x128c4 ORR	X15, XZR, XZR

  (49) 0x128c8 SUB	W16, W23, W15

  (49) 0x128cc ADD	X14, X15, #1

  (49) 0x128d0 TBZ	W16, #0, 128fc

  (49) 0x128d4 LDR	X17, [SP, #600]

  (49) 0x128d8 ADD	W16, W11, W15

  (49) 0x128dc MADD	W15, W15, W27, W10

  (49) 0x128e0 LDR	S1, [X17, W16,UXTW #2]

  (49) 0x128e4 LDR	X16, [SP, #584]

  (49) 0x128e8 LDR	S2, [X16, W15,UXTW #2]

  (49) 0x128ec ORR	X15, XZR, X14

  (49) 0x128f0 FCVT	D1, S1

  (49) 0x128f4 FCVT	D2, S2

  (49) 0x128f8 FMADD	D0, D1, D2, D0

  (49) 0x128fc LDR	X0, [SP, #600]

  (49) 0x12900 LDR	X1, [SP, #520]

  (49) 0x12904 LDR	X2, [SP, #584]

  (49) 0x12908 AND	X16, X23, #0x7fffffff

  (49) 0x1290c CMP	X16, X14

  (49) 0x12910 B.EQ	12880

  (49) 0x12914 MUL	X17, X27, X15

  (49) 0x12918 AND	X14, X23, #0x7fffffff

  (49) 0x1291c ADD	X18, X10, X27

  (49) 0x12920 SUB	X14, X14, X15

  (49) 0x12924 ADD	W15, W8, W15

  (49) 0x12928 ADD	X16, X10, X17

  (49) 0x1292c ADD	X17, X18, X17

  (49) 0x12930 HINT	#0

  (49) 0x12934 HINT	#0

  (49) 0x12938 HINT	#0

  (49) 0x1293c HINT	#0

    (50) 0x12940 LDR	S1, [X0, W15,UXTW #2]

    (50) 0x12944 LDR	S2, [X2, W16,UXTW #2]

    (50) 0x12948 ADD	W18, W15, #1

    (50) 0x1294c SUBS	X14, X14, #2

    (50) 0x12950 ADD	X16, X16, X1

    (50) 0x12954 ADD	W15, W15, #2

    (50) 0x12958 FCVT	D1, S1

    (50) 0x1295c FCVT	D2, S2

    (50) 0x12960 FMADD	D0, D1, D2, D0

    (50) 0x12964 LDR	S1, [X0, W18,UXTW #2]

    (50) 0x12968 LDR	S2, [X2, W17,UXTW #2]

    (50) 0x1296c ADD	X17, X17, X1

    (50) 0x12970 FCVT	D1, S1

    (50) 0x12974 FCVT	D2, S2

    (50) 0x12978 FMADD	D0, D1, D2, D0

    (50) 0x1297c B.NE	12940

  (49) 0x12980 B	12880

  (49) 0x12984 LDR	X18, [SP, #480]

  (49) 0x12988 LDR	X0, [SP, #600]

  (49) 0x1298c LDR	X1, [SP, #584]

  (49) 0x12990 MOVI	V0.2D, #0

  (49) 0x12994 MOVI	V1.2D, #0

  (49) 0x12998 CNTW	X17, ALL

  (49) 0x1299c PTRUE	P0.D, ALL

  (49) 0x129a0 ORR	X14, XZR, XZR

    (51) 0x129a4 ADD	W15, W8, W14

    (51) 0x129a8 ADD	X16, X0, W15,UXTW #2

    (51) 0x129ac LD1W	{Z2.D}, P0/Z, [X0, X15,LSL #2]

    (51) 0x129b0 ADD	W15, W10, W14

    (51) 0x129b4 ADD	X14, X14, X17

    (51) 0x129b8 LD1W	{Z4.D}, P0/Z, [X1, X15,LSL #2]

    (51) 0x129bc CMP	X18, X14

    (51) 0x129c0 LD1W	{Z3.D}, P0/Z, [X16, #1, MUL VL]

    (51) 0x129c4 ADD	X16, X1, W15,UXTW #2

    (51) 0x129c8 LD1W	{Z5.D}, P0/Z, [X16, #1, MUL VL]

    (51) 0x129cc FCVT	Z2.D, P0/M, Z2.S

    (51) 0x129d0 FCVT	Z4.D, P0/M, Z4.S

    (51) 0x129d4 FCVT	Z3.D, P0/M, Z3.S

    (51) 0x129d8 FCVT	Z5.D, P0/M, Z5.S

    (51) 0x129dc FMLA	Z0.D, P0/M, Z2.D, Z4.D

    (51) 0x129e0 FMLA	Z1.D, P0/M, Z3.D, Z5.D

    (51) 0x129e4 B.NE	129a4

  (49) 0x129e8 FADD	Z0.D, Z1.D, Z0.D

  (49) 0x129ec AND	X14, X23, #0x7fffffff

  (49) 0x129f0 ORR	X15, XZR, X18

  (49) 0x129f4 CMP	X14, X18

  (49) 0x129f8 FADDV	D0, P0, Z0.D

  (49) 0x129fc B.EQ	12880

  (49) 0x12a00 B	128c8

/home/eoseret/llm-attention/attention_v2.cpp: 26 - 33

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

26:    for (unsigned int i = 0; i < M; ++i) {

27:       for (unsigned int j = 0; j < N; ++j) {  //vectorized

28:          double sum = 0.0;

29: #pragma clang loop vectorize(enable)

30:          for (unsigned int k = 0; k < K; ++k) { //vectorized

31:             sum += (double)A[i * K + k] * (double)B[k * N + j];

32:          }

33:          C[i * N + j] = alpha * static_cast<float>(sum);

Coverage (%)	Name	Module
►100.00+	__libc_start_call_main	libc.so.6
○	__libc_start_main	libc.so.6
○	_start	attention-armclang-native

min	med	avg	max

Percentile Index	10	20	30	40	50	60	70	80	90	100
Value

min	med	avg	max

Percentile Index	10	20	30	40	50	60	70	80	90	100
Value

Path /

Metric	Value
CQA speedup if no scalar integer	1.00
CQA speedup if FP arith vectorized	1.00
CQA speedup if fully vectorized	6.40
CQA speedup if no inter-iteration dependency	NA
CQA speedup if next bottleneck killed	1.33
Bottlenecks	P2, P3, P4, P5,
Function	main
Source	attention_v2.cpp:26-26
Source loop unroll info	NA
Source loop unroll confidence level	NA
Unroll/vectorization loop type	NA
Unroll factor	NA
CQA cycles	2.00
CQA cycles if no scalar integer	2.00
CQA cycles if FP arith vectorized	2.00
CQA cycles if fully vectorized	0.31
Front-end cycles	1.50
P0 cycles	1.00
P1 cycles	1.00
P2 cycles	2.00
P3 cycles	2.00
P4 cycles	2.00
P5 cycles	2.00
P6 cycles	0.00
P7 cycles	0.00
P8 cycles	0.00
P9 cycles	0.00
P10 cycles	0.67
P11 cycles	0.67
P12 cycles	0.67
P13 cycles	0.00
P14 cycles	0.00
DIV/SQRT cycles	0.00
Inter-iter dependencies cycles	NA
FE+BE cycles (UFS)	NA
Stall cycles (UFS)	NA
Nb insns	12.00
Nb uops	12.00
Nb loads	NA
Nb stores	0.00
Nb stack references	2.00
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	1.50
Bytes prefetched	0.00
Bytes loaded	3.00
Bytes stored	0.00
Stride 0	NA
Stride 1	NA
Stride n	NA
Stride unknown	NA
Stride indirect	NA
Vectorization ratio all	0.00
Vectorization ratio load	NA
Vectorization ratio store	NA
Vectorization ratio mul	0.00
Vectorization ratio add_sub	0.00
Vectorization ratio fma	NA
Vectorization ratio div_sqrt	NA
Vectorization ratio other	0.00
Vector-efficiency ratio all	17.50
Vector-efficiency ratio load	NA
Vector-efficiency ratio store	NA
Vector-efficiency ratio mul	12.50
Vector-efficiency ratio add_sub	18.75
Vector-efficiency ratio fma	NA
Vector-efficiency ratio div_sqrt	NA
Vector-efficiency ratio other	18.75

Metric	Value
CQA speedup if no scalar integer	1.00
CQA speedup if FP arith vectorized	1.00
CQA speedup if fully vectorized	6.40
CQA speedup if no inter-iteration dependency	NA
CQA speedup if next bottleneck killed	1.33
Bottlenecks	P2, P3, P4, P5,
Function	main
Source	attention_v2.cpp:26-26
Source loop unroll info	NA
Source loop unroll confidence level	NA
Unroll/vectorization loop type	NA
Unroll factor	NA
CQA cycles	2.00
CQA cycles if no scalar integer	2.00
CQA cycles if FP arith vectorized	2.00
CQA cycles if fully vectorized	0.31
Front-end cycles	1.50
P0 cycles	1.00
P1 cycles	1.00
P2 cycles	2.00
P3 cycles	2.00
P4 cycles	2.00
P5 cycles	2.00
P6 cycles	0.00
P7 cycles	0.00
P8 cycles	0.00
P9 cycles	0.00
P10 cycles	0.67
P11 cycles	0.67
P12 cycles	0.67
P13 cycles	0.00
P14 cycles	0.00
DIV/SQRT cycles	0.00
Inter-iter dependencies cycles	NA
FE+BE cycles (UFS)	NA
Stall cycles (UFS)	NA
Nb insns	12.00
Nb uops	12.00
Nb loads	NA
Nb stores	0.00
Nb stack references	2.00
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	1.50
Bytes prefetched	0.00
Bytes loaded	3.00
Bytes stored	0.00
Stride 0	NA
Stride 1	NA
Stride n	NA
Stride unknown	NA
Stride indirect	NA
Vectorization ratio all	0.00
Vectorization ratio load	NA
Vectorization ratio store	NA
Vectorization ratio mul	0.00
Vectorization ratio add_sub	0.00
Vectorization ratio fma	NA
Vectorization ratio div_sqrt	NA
Vectorization ratio other	0.00
Vector-efficiency ratio all	17.50
Vector-efficiency ratio load	NA
Vector-efficiency ratio store	NA
Vector-efficiency ratio mul	12.50
Vector-efficiency ratio add_sub	18.75
Vector-efficiency ratio fma	NA
Vector-efficiency ratio div_sqrt	NA
Vector-efficiency ratio other	18.75

Path /

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

Function	main
Source file and lines	attention_v2.cpp:26-33
Module	attention-armclang-native

The loop is defined in /home/eoseret/llm-attention/attention_v2.cpp:26-33.

Analyzed code is defined in /home/eoseret/llm-attention/attention_v2.cpp:26.

Warnings:

Non-innermost loop: analyzing only self part (ignoring child loops).
Ignoring paths for analysis
Failed to get the number of paths
RecMII not computed since number of paths is unknown or > max_paths
Streams not analyzed since number of paths is unknown or > max_paths

gain
potential
hint
expert

Vectorization

Your loop is not vectorized. Only 17% of vector register length is used (average across all VPU instructions). By vectorizing your loop, you can lower the cost of an iteration from 2.00 to 0.31 cycles (6.40x 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 ffast-math (included in Ofast) to extend loop vectorization to FP reductions.
Remove inter-iterations dependences from your loop and make it unit-stride:
- If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly: C storage order is row-major: for(i) for(j) a[j][i] = b[j][i]; (slow, non stride 1) => for(i) for(j) a[i][j] = b[i][j]; (fast, stride 1)
- 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)

No data for this section

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 3 bytes.

General properties

nb instructions	12
nb uops	12
loop length	48
used w registers	7
used x registers	5
used b registers	0
used h registers	0
used s registers	1
used d registers	0
used q registers	0
used v registers	0
used z registers	0
nb stack references	2

Front-end

MACRO FUSION NOT POSSIBLE FIT IN UOP CACHE

micro-operation queue	1.50 cycles
front end	1.50 cycles

Back-end

	P0	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11	P12	P13	P14
uops	1.00	1.00	2.00	2.00	2.00	2.00	0.00	0.00	0.00	0.00	0.67	0.67	0.67	0.00	0.00
cycles	1.00	1.00	2.00	2.00	2.00	2.00	0.00	0.00	0.00	0.00	0.67	0.67	0.67	0.00	0.00

Execution ports to units layout:

P0: BRU
P1: BRU
P2: ALU
P3: ALU
P4: ALU
P5: ALU
P6 (128 bits): VPU, FP store data, ALU, DIV/SQRT
P7 (128 bits): VPU, ALU, FP store data
P8 (128 bits): VPU, ALU, DIV/SQRT
P9 (128 bits): ALU, VPU
P10 (256 bits): store address, load
P11 (256 bits): store address, load
P12 (256 bits): load
P13 (64 bits): store data
P14 (64 bits): store data

Cycles executing div or sqrt instructions

NA

Cycles summary

Front-end	1.50
Dispatch	2.00
Overall L1	2.00

Vectorization ratios

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

Vector efficiency ratios

all	17%
load	NA (no load vectorizable/vectorized instructions)
store	NA (no store vectorizable/vectorized instructions)
mul	12%
add-sub	18%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	18%

Cycles and memory resources usage

Assuming all data fit into the L1 cache, each iteration of the binary loop takes 2.00 cycles. At this rate:

1% of peak load performance is reached (1.50 out of 96.00 bytes loaded per cycle (GB/s @ 1GHz))

Front-end bottlenecks

Found no such bottlenecks.

ASM code

In the binary file, the address of the loop is: 12858

Instruction	Nb FU	P0	P1	P2	P3	P4	P5	P10	P11	P12	Latency	Recip. throughput	Vectorization
ADD W9, W9, #1	1	0	0	0.25	0.25	0.25	0.25	0	0	0	1	0.25	scal (12.5%)
ADD X8, X8, X23	1	0	0	0.25	0.25	0.25	0.25	0	0	0	1	0.25	scal (25.0%)
CMP W9, W27	1	0	0	0.25	0.25	0.25	0.25	0	0	0	1	0.33	scal (12.5%)
B.EQ 12a04 <main+0x1a44>	1	0.50	0.50	0	0	0	0	0	0	0	1	0.50	N/A
LDR X13, [SP, #608]	1	0	0	0	0	0	0	0.33	0.33	0.33	4	0.33	N/A
MUL W11, W9, W23	1	0	0	0	0	0.50	0.50	0	0	0	2	0.50	N/A
MUL W12, W9, W27	1	0	0	0	0	0.50	0.50	0	0	0	2	0.50	scal (12.5%)
ORR X10, XZR, XZR	1	0	0	0.25	0.25	0.25	0.25	0	0	0	1	0.25	scal (25.0%)
CMN W11, W13	1	0	0	0.25	0.25	0.25	0.25	0	0	0	1	0.33	N/A
LDR W13, [SP, #404]	1	0	0	0	0	0	0	0.33	0.33	0.33	4	0.33	N/A
CSINC W13, W13, WZR, #3	1	0	0	0.25	0.25	0.25	0.25	0	0	0	1	0.25	N/A
B 128a0 <main+0x18e0>	1	0.50	0.50	0	0	0	0	0	0	0	1	0.50	N/A

Function	main
Source file and lines	attention_v2.cpp:26-33
Module	attention-armclang-native

The loop is defined in /home/eoseret/llm-attention/attention_v2.cpp:26-33.

Analyzed code is defined in /home/eoseret/llm-attention/attention_v2.cpp:26.

Warnings:

Non-innermost loop: analyzing only self part (ignoring child loops).
Ignoring paths for analysis
Failed to get the number of paths
RecMII not computed since number of paths is unknown or > max_paths
Streams not analyzed since number of paths is unknown or > max_paths

gain
potential
hint
expert

Vectorization

Your loop is not vectorized. Only 17% of vector register length is used (average across all VPU instructions). By vectorizing your loop, you can lower the cost of an iteration from 2.00 to 0.31 cycles (6.40x 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 ffast-math (included in Ofast) to extend loop vectorization to FP reductions.
Remove inter-iterations dependences from your loop and make it unit-stride:
- If your arrays have 2 or more dimensions, check whether elements are accessed contiguously and, otherwise, try to permute loops accordingly: C storage order is row-major: for(i) for(j) a[j][i] = b[j][i]; (slow, non stride 1) => for(i) for(j) a[i][j] = b[i][j]; (fast, stride 1)
- 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)

No data for this section

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 3 bytes.

General properties

nb instructions	12
nb uops	12
loop length	48
used w registers	7
used x registers	5
used b registers	0
used h registers	0
used s registers	1
used d registers	0
used q registers	0
used v registers	0
used z registers	0
nb stack references	2

Front-end

MACRO FUSION NOT POSSIBLE FIT IN UOP CACHE

micro-operation queue	1.50 cycles
front end	1.50 cycles

Back-end

	P0	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11	P12	P13	P14
uops	1.00	1.00	2.00	2.00	2.00	2.00	0.00	0.00	0.00	0.00	0.67	0.67	0.67	0.00	0.00
cycles	1.00	1.00	2.00	2.00	2.00	2.00	0.00	0.00	0.00	0.00	0.67	0.67	0.67	0.00	0.00

Execution ports to units layout:

P0: BRU
P1: BRU
P2: ALU
P3: ALU
P4: ALU
P5: ALU
P6 (128 bits): VPU, FP store data, ALU, DIV/SQRT
P7 (128 bits): VPU, ALU, FP store data
P8 (128 bits): VPU, ALU, DIV/SQRT
P9 (128 bits): ALU, VPU
P10 (256 bits): store address, load
P11 (256 bits): store address, load
P12 (256 bits): load
P13 (64 bits): store data
P14 (64 bits): store data

Cycles executing div or sqrt instructions

NA

Cycles summary

Front-end	1.50
Dispatch	2.00
Overall L1	2.00

Vectorization ratios

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

Vector efficiency ratios

all	17%
load	NA (no load vectorizable/vectorized instructions)
store	NA (no store vectorizable/vectorized instructions)
mul	12%
add-sub	18%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	18%

Cycles and memory resources usage

Assuming all data fit into the L1 cache, each iteration of the binary loop takes 2.00 cycles. At this rate:

1% of peak load performance is reached (1.50 out of 96.00 bytes loaded per cycle (GB/s @ 1GHz))

Front-end bottlenecks

Found no such bottlenecks.

ASM code

In the binary file, the address of the loop is: 12858

Instruction	Nb FU	P0	P1	P2	P3	P4	P5	P10	P11	P12	Latency	Recip. throughput	Vectorization
ADD W9, W9, #1	1	0	0	0.25	0.25	0.25	0.25	0	0	0	1	0.25	scal (12.5%)
ADD X8, X8, X23	1	0	0	0.25	0.25	0.25	0.25	0	0	0	1	0.25	scal (25.0%)
CMP W9, W27	1	0	0	0.25	0.25	0.25	0.25	0	0	0	1	0.33	scal (12.5%)
B.EQ 12a04 <main+0x1a44>	1	0.50	0.50	0	0	0	0	0	0	0	1	0.50	N/A
LDR X13, [SP, #608]	1	0	0	0	0	0	0	0.33	0.33	0.33	4	0.33	N/A
MUL W11, W9, W23	1	0	0	0	0	0.50	0.50	0	0	0	2	0.50	N/A
MUL W12, W9, W27	1	0	0	0	0	0.50	0.50	0	0	0	2	0.50	scal (12.5%)
ORR X10, XZR, XZR	1	0	0	0.25	0.25	0.25	0.25	0	0	0	1	0.25	scal (25.0%)
CMN W11, W13	1	0	0	0.25	0.25	0.25	0.25	0	0	0	1	0.33	N/A
LDR W13, [SP, #404]	1	0	0	0	0	0	0	0.33	0.33	0.33	4	0.33	N/A
CSINC W13, W13, WZR, #3	1	0	0	0.25	0.25	0.25	0.25	0	0	0	1	0.25	N/A
B 128a0 <main+0x18e0>	1	0.50	0.50	0	0	0	0	0	0	0	1	0.50	N/A

Report Configuration

Vectorization

Details

Workaround

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

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

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

General properties

Front-end

Back-end

Cycles summary

Vectorization ratios

Vector efficiency ratios

Cycles and memory resources usage

Front-end bottlenecks

ASM code