OV - attention-clang-skl512 - Loop 48 - attention-clang-skl512

	Loop Id: 48	Module: attention-clang-skl512	Source: attention_v2.cpp:52-53	Coverage: 0.02%

0x4ec0 VMOVAPS	%XMM2,0xe0(%RSP)    [1]

0x4ec9 VMOVUPS	(%RDI,%RBX,4),%XMM0    [2]

0x4ece VSUBPS	0x280(%RSP),%XMM0,%XMM0    [1]

0x4ed7 VZEROUPPER

0x4eda CALL	1080 <_ZGVbN4v_expf@plt>

0x4edf VMOVAPS	0xe0(%RSP),%XMM2    [1]

0x4ee8 MOV	0x8(%RSP),%RDI    [1]

0x4eed VADDPS	%XMM2,%XMM0,%XMM2

0x4ef1 ADD	$0x4,%RBX

0x4ef5 CMP	%RBX,0x2a0(%RSP)    [1]

0x4efd JNE	4ec0

/home/eoseret/Applications/llm-attention/attention_v2.cpp: 52 - 53

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

52:       for (int idx = 0; idx <= row; ++idx) // vectorised

53:          sum += expf(S_row[idx] - max_val);

Coverage (%)	Name	Module
►100.00+	__libc_init_first	libc.so.6
○	__libc_start_main	libc.so.6
○	_start	attention-clang-skl512

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	1.27
CQA speedup if FP arith vectorized	1.80
CQA speedup if fully vectorized	4.00
CQA speedup if no inter-iteration dependency	NA
CQA speedup if next bottleneck killed	1.40
Bottlenecks	micro-operation queue,
Function	main
Source	attention_v2.cpp:52-53
Source loop unroll info	unrolled by 32
Source loop unroll confidence level	max
Unroll/vectorization loop type	intermediate
Unroll factor	4
CQA cycles	3.50
CQA cycles if no scalar integer	2.75
CQA cycles if FP arith vectorized	1.95
CQA cycles if fully vectorized	0.88
Front-end cycles	3.50
P0 cycles	1.50
P1 cycles	1.00
P2 cycles	2.50
P3 cycles	2.50
P4 cycles	2.00
P5 cycles	1.00
P6 cycles	1.50
P7 cycles	2.00
DIV/SQRT cycles	0.00
Inter-iter dependencies cycles	1
FE+BE cycles (UFS)	NA
Stall cycles (UFS)	NA
Nb insns	11.00
Nb uops	14.00
Nb loads	5.00
Nb stores	1.00
Nb stack references	4.00
FLOP/cycle	2.29
Nb FLOP add-sub	8.00
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	22.86
Bytes prefetched	0.00
Bytes loaded	64.00
Bytes stored	16.00
Stride 0	1.00
Stride 1	1.00
Stride n	0.00
Stride unknown	0.00
Stride indirect	0.00
Vectorization ratio all	100.00
Vectorization ratio load	100.00
Vectorization ratio store	100.00
Vectorization ratio mul	NA
Vectorization ratio add_sub	100.00
Vectorization ratio fma	NA
Vectorization ratio div_sqrt	NA
Vectorization ratio other	100.00
Vector-efficiency ratio all	25.00
Vector-efficiency ratio load	25.00
Vector-efficiency ratio store	25.00
Vector-efficiency ratio mul	NA
Vector-efficiency ratio add_sub	25.00
Vector-efficiency ratio fma	NA
Vector-efficiency ratio div_sqrt	NA
Vector-efficiency ratio other	25.00

Metric	Value
CQA speedup if no scalar integer	1.27
CQA speedup if FP arith vectorized	1.80
CQA speedup if fully vectorized	4.00
CQA speedup if no inter-iteration dependency	NA
CQA speedup if next bottleneck killed	1.40
Bottlenecks	micro-operation queue,
Function	main
Source	attention_v2.cpp:52-53
Source loop unroll info	unrolled by 32
Source loop unroll confidence level	max
Unroll/vectorization loop type	intermediate
Unroll factor	4
CQA cycles	3.50
CQA cycles if no scalar integer	2.75
CQA cycles if FP arith vectorized	1.95
CQA cycles if fully vectorized	0.88
Front-end cycles	3.50
P0 cycles	1.50
P1 cycles	1.00
P2 cycles	2.50
P3 cycles	2.50
P4 cycles	2.00
P5 cycles	1.00
P6 cycles	1.50
P7 cycles	2.00
DIV/SQRT cycles	0.00
Inter-iter dependencies cycles	1
FE+BE cycles (UFS)	NA
Stall cycles (UFS)	NA
Nb insns	11.00
Nb uops	14.00
Nb loads	5.00
Nb stores	1.00
Nb stack references	4.00
FLOP/cycle	2.29
Nb FLOP add-sub	8.00
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	22.86
Bytes prefetched	0.00
Bytes loaded	64.00
Bytes stored	16.00
Stride 0	1.00
Stride 1	1.00
Stride n	0.00
Stride unknown	0.00
Stride indirect	0.00
Vectorization ratio all	100.00
Vectorization ratio load	100.00
Vectorization ratio store	100.00
Vectorization ratio mul	NA
Vectorization ratio add_sub	100.00
Vectorization ratio fma	NA
Vectorization ratio div_sqrt	NA
Vectorization ratio other	100.00
Vector-efficiency ratio all	25.00
Vector-efficiency ratio load	25.00
Vector-efficiency ratio store	25.00
Vector-efficiency ratio mul	NA
Vector-efficiency ratio add_sub	25.00
Vector-efficiency ratio fma	NA
Vector-efficiency ratio div_sqrt	NA
Vector-efficiency ratio other	25.00

Path /

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

Function	main
Source file and lines	attention_v2.cpp:52-53
Module	attention-clang-skl512

The loop is defined in /home/eoseret/Applications/llm-attention/attention_v2.cpp:52-53.

It is intermediate loop of related source loop which is unrolled by 32 (including vectorization).

gain
potential
hint
expert

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 3.50 to 2.75 cycles (1.27x speedup).

Workaround

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

Unrolling/vectorization cost

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

Details

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

Workaround

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

Vectorization

Your loop is vectorized, but using only 128 out of 512 bits (SSE/AVX-128 instructions on AVX-512 processors).

Details

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

Execution units bottlenecks

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

No data for this section

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.

_ZGVbN4v_expf@plt: 1 occurrences►
- /home/eoseret/Applications/llm-attention/attention_v2.cpp:53

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.

VZEROUPPER: 1 occurrences►
- /home/eoseret/Applications/llm-attention/attention_v2.cpp:53

Type of elements and instruction set

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

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

The binary loop is composed of 8 FP arithmetical operations:

8: addition or subtraction

The binary loop is loading 64 bytes (16 single precision FP elements). The binary loop is storing 16 bytes (4 single precision FP elements).

Arithmetic intensity

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

General properties

nb instructions	11
nb uops	14
loop length	63
used x86 registers	3
used mmx registers	0
used xmm registers	2
used ymm registers	0
used zmm registers	0
nb stack references	4

Front-end

ASSUMED MACRO FUSION FIT IN UOP CACHE

micro-operation queue	3.50 cycles
front end	3.50 cycles

Back-end

	P0	P1	P2	P3	P4	P5	P6	P7
uops	1.50	1.00	2.50	2.50	2.00	1.00	1.50	2.00
cycles	1.50	1.00	2.50	2.50	2.00	1.00	1.50	2.00

Execution ports to units layout:

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

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

Cycles summary

Front-end	3.50
Dispatch	2.50
Data deps.	1.00
Overall L1	3.50

Vectorization ratios

INT

all	100%
load	NA (no load vectorizable/vectorized instructions)
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)
other	100%

all	100%
load	100%
store	100%
mul	NA (no mul vectorizable/vectorized instructions)
add-sub	100%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	NA (no other vectorizable/vectorized instructions)

INT+FP

all	100%
load	100%
store	100%
mul	NA (no mul vectorizable/vectorized instructions)
add-sub	100%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	100%

Vector efficiency ratios

INT

all	25%
load	NA (no load vectorizable/vectorized instructions)
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)
other	25%

all	25%
load	25%
store	25%
mul	NA (no mul vectorizable/vectorized instructions)
add-sub	25%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	NA (no other vectorizable/vectorized instructions)

INT+FP

all	25%
load	25%
store	25%
mul	NA (no mul vectorizable/vectorized instructions)
add-sub	25%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	25%

Cycles and memory resources usage

Assuming all data fit into the L1 cache, each iteration of the binary loop takes 3.50 cycles. At this rate:

14% of peak load performance is reached (18.29 out of 128.00 bytes loaded per cycle (GB/s @ 1GHz))
7% of peak store performance is reached (4.57 out of 64.00 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 3.50 to 2.50 cycles (1.40x speedup).

ASM code

In the binary file, the address of the loop is: 4ec0

Instruction	Nb FU	P0	P1	P2	P3	P4	P5	P6	P7	Latency	Recip. throughput	Vectorization
VMOVAPS %XMM2,0xe0(%RSP)	1	0	0	0.33	0.33	1	0	0	0.33	3	1	vect (25.0%)
VMOVUPS (%RDI,%RBX,4),%XMM0	1	0	0	0.50	0.50	0	0	0	0	4-5	0.50	vect (25.0%)
VSUBPS 0x280(%RSP),%XMM0,%XMM0	1	0.50	0.50	0.50	0.50	0	0	0	0	4	0.50	vect (25.0%)
VZEROUPPER	4	0	0	0	0	0	0	0	0	0	1	vect (25.0%)
CALL 1080 <_ZGVbN4v_expf@plt>	2	0	0	0.33	0.33	1	0	1	0.33	0	1	N/A
VMOVAPS 0xe0(%RSP),%XMM2	1	0	0	0.50	0.50	0	0	0	0	4-5	0.50	vect (25.0%)
MOV 0x8(%RSP),%RDI	1	0	0	0.50	0.50	0	0	0	0	4-5	0.50	N/A
VADDPS %XMM2,%XMM0,%XMM2	1	0.50	0.50	0	0	0	0	0	0	4	0.50	vect (25.0%)
ADD $0x4,%RBX	1	0.25	0.25	0	0	0	0.25	0.25	0	1	0.25	N/A
CMP %RBX,0x2a0(%RSP)	1	0.25	0.25	0.50	0.50	0	0.25	0.25	0	1	0.50	N/A
JNE 4ec0 <main+0x2920>	1	0.50	0	0	0	0	0	0.50	0	0	0.50-1	N/A

Function	main
Source file and lines	attention_v2.cpp:52-53
Module	attention-clang-skl512

The loop is defined in /home/eoseret/Applications/llm-attention/attention_v2.cpp:52-53.

It is intermediate loop of related source loop which is unrolled by 32 (including vectorization).

gain
potential
hint
expert

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 3.50 to 2.75 cycles (1.27x speedup).

Workaround

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

Unrolling/vectorization cost

Details

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

Workaround

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

Vectorization

Your loop is vectorized, but using only 128 out of 512 bits (SSE/AVX-128 instructions on AVX-512 processors).

Details

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

Execution units bottlenecks

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

No data for this section

CALL instructions

Detected function call instructions.

Details

_ZGVbN4v_expf@plt: 1 occurrences►
- /home/eoseret/Applications/llm-attention/attention_v2.cpp:53

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.

VZEROUPPER: 1 occurrences►
- /home/eoseret/Applications/llm-attention/attention_v2.cpp:53

Type of elements and instruction set

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

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

The binary loop is composed of 8 FP arithmetical operations:

8: addition or subtraction

The binary loop is loading 64 bytes (16 single precision FP elements). The binary loop is storing 16 bytes (4 single precision FP elements).

Arithmetic intensity

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

General properties

nb instructions	11
nb uops	14
loop length	63
used x86 registers	3
used mmx registers	0
used xmm registers	2
used ymm registers	0
used zmm registers	0
nb stack references	4

Front-end

ASSUMED MACRO FUSION FIT IN UOP CACHE

micro-operation queue	3.50 cycles
front end	3.50 cycles

Back-end

	P0	P1	P2	P3	P4	P5	P6	P7
uops	1.50	1.00	2.50	2.50	2.00	1.00	1.50	2.00
cycles	1.50	1.00	2.50	2.50	2.00	1.00	1.50	2.00

Execution ports to units layout:

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

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

Cycles summary

Front-end	3.50
Dispatch	2.50
Data deps.	1.00
Overall L1	3.50

Vectorization ratios

INT

all	100%
load	NA (no load vectorizable/vectorized instructions)
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)
other	100%

all	100%
load	100%
store	100%
mul	NA (no mul vectorizable/vectorized instructions)
add-sub	100%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	NA (no other vectorizable/vectorized instructions)

INT+FP

all	100%
load	100%
store	100%
mul	NA (no mul vectorizable/vectorized instructions)
add-sub	100%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	100%

Vector efficiency ratios

INT

all	25%
load	NA (no load vectorizable/vectorized instructions)
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)
other	25%

all	25%
load	25%
store	25%
mul	NA (no mul vectorizable/vectorized instructions)
add-sub	25%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	NA (no other vectorizable/vectorized instructions)

INT+FP

all	25%
load	25%
store	25%
mul	NA (no mul vectorizable/vectorized instructions)
add-sub	25%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	NA (no div/sqrt vectorizable/vectorized instructions)
other	25%

Cycles and memory resources usage

Assuming all data fit into the L1 cache, each iteration of the binary loop takes 3.50 cycles. At this rate:

14% of peak load performance is reached (18.29 out of 128.00 bytes loaded per cycle (GB/s @ 1GHz))
7% of peak store performance is reached (4.57 out of 64.00 bytes stored per cycle (GB/s @ 1GHz))

Front-end bottlenecks

ASM code

In the binary file, the address of the loop is: 4ec0

Instruction	Nb FU	P0	P1	P2	P3	P4	P5	P6	P7	Latency	Recip. throughput	Vectorization
VMOVAPS %XMM2,0xe0(%RSP)	1	0	0	0.33	0.33	1	0	0	0.33	3	1	vect (25.0%)
VMOVUPS (%RDI,%RBX,4),%XMM0	1	0	0	0.50	0.50	0	0	0	0	4-5	0.50	vect (25.0%)
VSUBPS 0x280(%RSP),%XMM0,%XMM0	1	0.50	0.50	0.50	0.50	0	0	0	0	4	0.50	vect (25.0%)
VZEROUPPER	4	0	0	0	0	0	0	0	0	0	1	vect (25.0%)
CALL 1080 <_ZGVbN4v_expf@plt>	2	0	0	0.33	0.33	1	0	1	0.33	0	1	N/A
VMOVAPS 0xe0(%RSP),%XMM2	1	0	0	0.50	0.50	0	0	0	0	4-5	0.50	vect (25.0%)
MOV 0x8(%RSP),%RDI	1	0	0	0.50	0.50	0	0	0	0	4-5	0.50	N/A
VADDPS %XMM2,%XMM0,%XMM2	1	0.50	0.50	0	0	0	0	0	0	4	0.50	vect (25.0%)
ADD $0x4,%RBX	1	0.25	0.25	0	0	0	0.25	0.25	0	1	0.25	N/A
CMP %RBX,0x2a0(%RSP)	1	0.25	0.25	0.50	0.50	0	0.25	0.25	0	1	0.50	N/A
JNE 4ec0 <main+0x2920>	1	0.50	0	0	0	0	0	0.50	0	0	0.50-1	N/A

Report Configuration

Code clean check

Workaround

Unrolling/vectorization cost

Details

Workaround

Vectorization

Details

Execution units bottlenecks

CALL instructions

Details

Complex instructions

Details

Type of elements and instruction set

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

Arithmetic intensity

General properties

Front-end

Back-end

Cycles summary

Vectorization ratios

Vector efficiency ratios

Cycles and memory resources usage

Front-end bottlenecks

ASM code

Code clean check

Workaround

Unrolling/vectorization cost

Details

Workaround

Vectorization

Details

Execution units bottlenecks

CALL instructions

Details

Complex instructions

Details

Type of elements and instruction set

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

Arithmetic intensity

General properties

Front-end

Back-end

Cycles summary

Vectorization ratios

Vector efficiency ratios

Cycles and memory resources usage

Front-end bottlenecks

ASM code