OV - attention-gcc-gnr-512 - Loop 1 - attention-gcc-gnr-512

0x4018c0 MOV	%RDX,%RAX

0x4018c3 SHR	$0x1e,%RAX

0x4018c7 XOR	%RDX,%RAX

0x4018ca IMUL	$0x6c078965,%RAX,%RAX

0x4018d1 LEA	(%RAX,%RCX,1),%EDX

0x4018d4 MOV	%RDX,-0x13c0(%RBP,%RCX,8)    [1]

0x4018dc INC	%RCX

0x4018df CMP	$0x270,%RCX

0x4018e6 JNE	4018c0

/cluster/comp/gcc/15.1.0/include/c++/15.1.0/bits/random.h: 248 - 248

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

248: 	    __res %= __m;

/cluster/comp/gcc/15.1.0/include/c++/15.1.0/bits/random.tcc: 333 - 339

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

333:       for (size_t __i = 1; __i < state_size; ++__i)

334: 	{

335: 	  _UIntType __x = _M_x[__i - 1];

336: 	  __x ^= __x >> (__w - 2);

337: 	  __x *= __f;

338: 	  __x += __detail::__mod<_UIntType, __n>(__i);

339: 	  _M_x[__i] = __detail::__mod<_UIntType,

Coverage (%)	Name	Source Location	Module

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	8.00
CQA speedup if no inter-iteration dependency	NA
CQA speedup if next bottleneck killed	4.29 - 7.14
Bottlenecks
Function	main
Source	random.h:248-248,random.tcc:333-339
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	6.00 - 10.00
CQA cycles if no scalar integer	6.00 - 10.00
CQA cycles if FP arith vectorized	6.00 - 10.00
CQA cycles if fully vectorized	0.75 - 1.25
Front-end cycles	1.33
P0 cycles	1.00
P1 cycles	1.40
P2 cycles	0.00
P3 cycles	0.00
P4 cycles	0.50
P5 cycles	1.00
P6 cycles	1.00
P7 cycles	0.50
P8 cycles	0.50
P9 cycles	0.50
P10 cycles	1.00
P11 cycles	0.00
DIV/SQRT cycles	0.00
Inter-iter dependencies cycles	6 - 10
FE+BE cycles (UFS)	NA
Stall cycles (UFS)	NA
Nb insns	9.00
Nb uops	8.00
Nb loads	0.00
Nb stores	1.00
Nb stack references	0.00
FLOP/cycle	0.00 - 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	0.80 - 1.33
Bytes prefetched	0.00
Bytes loaded	0.00
Bytes stored	8.00
Stride 0	0.00
Stride 1	1.00
Stride n	0.00
Stride unknown	0.00
Stride indirect	0.00
Vectorization ratio all	0.00
Vectorization ratio load	NA
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	NA
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	4.29 - 7.14
Bottlenecks
Function	main
Source	random.h:248-248,random.tcc:333-339
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	6.00 - 10.00
CQA cycles if no scalar integer	6.00 - 10.00
CQA cycles if FP arith vectorized	6.00 - 10.00
CQA cycles if fully vectorized	0.75 - 1.25
Front-end cycles	1.33
P0 cycles	1.00
P1 cycles	1.40
P2 cycles	0.00
P3 cycles	0.00
P4 cycles	0.50
P5 cycles	1.00
P6 cycles	1.00
P7 cycles	0.50
P8 cycles	0.50
P9 cycles	0.50
P10 cycles	1.00
P11 cycles	0.00
DIV/SQRT cycles	0.00
Inter-iter dependencies cycles	6 - 10
FE+BE cycles (UFS)	NA
Stall cycles (UFS)	NA
Nb insns	9.00
Nb uops	8.00
Nb loads	0.00
Nb stores	1.00
Nb stack references	0.00
FLOP/cycle	0.00 - 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	0.80 - 1.33
Bytes prefetched	0.00
Bytes loaded	0.00
Bytes stored	8.00
Stride 0	0.00
Stride 1	1.00
Stride n	0.00
Stride unknown	0.00
Stride indirect	0.00
Vectorization ratio all	0.00
Vectorization ratio load	NA
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	NA
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	main
Source file and lines	random.tcc:333-339
Module	attention-gcc-gnr-512

The loop is defined in:

/cluster/comp/gcc/15.1.0/include/c++/15.1.0/bits/random.h:248
/cluster/comp/gcc/15.1.0/include/c++/15.1.0/bits/random.tcc:333-339

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 10.00 to 1.25 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

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 storing 8 bytes.

General properties

nb instructions	9
nb uops	8
loop length	40
used x86 registers	4
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	1.33 cycles
front end	1.33 cycles

Back-end

	P0	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11
uops	1.00	1.00	0.00	0.00	0.50	1.00	1.00	0.50	0.50	0.50	1.00	0.00
cycles	1.00	1.40	0.00	0.00	0.50	1.00	1.00	0.50	0.50	0.50	1.00	0.00

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)	6.00-10.00

Cycles summary

Front-end	1.33
Dispatch	1.40
Data deps.	6.00-10.00
Overall L1	6.00-10.00

Vectorization ratios

all	0%
load	NA (no load vectorizable/vectorized instructions)
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	NA (no load vectorizable/vectorized instructions)
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 10.00 cycles. At this rate:

0% of peak store performance is reached (0.80 out of 128.00 bytes stored per cycle (GB/s @ 1GHz))

Front-end bottlenecks

Found no such bottlenecks.

ASM code

In the binary file, the address of the loop is: 4018c0

Instruction	Nb FU	P0	P1	P4	P5	P6	P7	P8	P9	P10	Latency	Recip. throughput	Vectorization
MOV %RDX,%RAX	1	0	0	0	0	0	0	0	0	0	1	0.17	N/A
SHR $0x1e,%RAX	1	0.50	0	0	0	0.50	0	0	0	0	0-2	0.50	N/A
XOR %RDX,%RAX	1	0.20	0.20	0	0.20	0.20	0	0	0	0.20	1-2	0.20	N/A
IMUL $0x6c078965,%RAX,%RAX	1	0	1	0	0	0	0	0	0	0	3	1	N/A
LEA (%RAX,%RCX,1),%EDX	1	0.20	0.20	0	0.20	0.20	0	0	0	0.20	1-2	0.20	N/A
MOV %RDX,-0x13c0(%RBP,%RCX,8)	1	0	0	0.50	0	0	0.50	0.50	0.50	0	1	0.50	scal (12.5%)
INC %RCX	1	0	0	0	0	0	0	0	0	0	1	0.17	N/A
CMP $0x270,%RCX	1	0.20	0.20	0	0.20	0.20	0	0	0	0.20	1	0.20	N/A
JNE 4018c0 <main+0x5c0>	1	0.50	0	0	0	0.50	0	0	0	0	0	0.50	N/A

Function	main
Source file and lines	random.tcc:333-339
Module	attention-gcc-gnr-512

The loop is defined in:

/cluster/comp/gcc/15.1.0/include/c++/15.1.0/bits/random.h:248
/cluster/comp/gcc/15.1.0/include/c++/15.1.0/bits/random.tcc:333-339

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 10.00 to 1.25 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

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 storing 8 bytes.

General properties

nb instructions	9
nb uops	8
loop length	40
used x86 registers	4
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	1.33 cycles
front end	1.33 cycles

Back-end

	P0	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11
uops	1.00	1.00	0.00	0.00	0.50	1.00	1.00	0.50	0.50	0.50	1.00	0.00
cycles	1.00	1.40	0.00	0.00	0.50	1.00	1.00	0.50	0.50	0.50	1.00	0.00

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)	6.00-10.00

Cycles summary

Front-end	1.33
Dispatch	1.40
Data deps.	6.00-10.00
Overall L1	6.00-10.00

Vectorization ratios

all	0%
load	NA (no load vectorizable/vectorized instructions)
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	NA (no load vectorizable/vectorized instructions)
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 10.00 cycles. At this rate:

0% of peak store performance is reached (0.80 out of 128.00 bytes stored per cycle (GB/s @ 1GHz))

Front-end bottlenecks

Found no such bottlenecks.

ASM code

In the binary file, the address of the loop is: 4018c0

Instruction	Nb FU	P0	P1	P4	P5	P6	P7	P8	P9	P10	Latency	Recip. throughput	Vectorization
MOV %RDX,%RAX	1	0	0	0	0	0	0	0	0	0	1	0.17	N/A
SHR $0x1e,%RAX	1	0.50	0	0	0	0.50	0	0	0	0	0-2	0.50	N/A
XOR %RDX,%RAX	1	0.20	0.20	0	0.20	0.20	0	0	0	0.20	1-2	0.20	N/A
IMUL $0x6c078965,%RAX,%RAX	1	0	1	0	0	0	0	0	0	0	3	1	N/A
LEA (%RAX,%RCX,1),%EDX	1	0.20	0.20	0	0.20	0.20	0	0	0	0.20	1-2	0.20	N/A
MOV %RDX,-0x13c0(%RBP,%RCX,8)	1	0	0	0.50	0	0	0.50	0.50	0.50	0	1	0.50	scal (12.5%)
INC %RCX	1	0	0	0	0	0	0	0	0	0	1	0.17	N/A
CMP $0x270,%RCX	1	0.20	0.20	0	0.20	0.20	0	0	0	0.20	1	0.20	N/A
JNE 4018c0 <main+0x5c0>	1	0.50	0	0	0	0.50	0	0	0	0	0	0.50	N/A

Report Configuration

Vectorization

Details

Workaround

Execution units bottlenecks

Type of elements and instruction set

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

Execution units bottlenecks

Type of elements and instruction set

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