OV - exec - Loop 2247

	Loop Id: 2247	Module: exec	Source: :0-0	Coverage: 1.04%

0x48f020 MOVSD	(%RAX),%XMM4    [2]

0x48f024 MOVSD	(%R12),%XMM3    [6]

0x48f02a ADD	$0x1,%RDI

0x48f02e MOVSD	(%RBP),%XMM1    [5]

0x48f033 MOVSD	(%RSI),%XMM0    [8]

0x48f037 ADDSD	%XMM3,%XMM3

0x48f03b DIVSD	(%R11),%XMM3    [10]

0x48f040 MULSD	%XMM3,%XMM4

0x48f044 MOVSD	(%R13),%XMM2    [7]

0x48f04a ADDSD	%XMM1,%XMM1

0x48f04e DIVSD	(%R8),%XMM1    [4]

0x48f053 MULSD	%XMM1,%XMM0

0x48f057 ADD	%R15,%R8

0x48f05a ADDSD	(%R10),%XMM0    [1]

0x48f05f ADDSD	%XMM2,%XMM2

0x48f063 DIVSD	(%R14),%XMM2    [9]

0x48f068 ADD	%RBX,%R10

0x48f06b ADDSD	%XMM3,%XMM1

0x48f06f ADDSD	%XMM4,%XMM0

0x48f073 MOVSD	(%RDX),%XMM4    [13]

0x48f077 ADDSD	%XMM2,%XMM1

0x48f07b ADDSD	(%R9),%XMM1    [11]

0x48f080 ADD	%RBX,%R9

0x48f083 MULSD	%XMM2,%XMM4

0x48f087 ADDSD	%XMM4,%XMM0

0x48f08b DIVSD	%XMM1,%XMM0

0x48f08f MOVSD	%XMM0,(%RCX)    [3]

0x48f093 ADDSD	%XMM0,%XMM0

0x48f097 ADD	%RBX,%RCX

0x48f09a MOVAPD	%XMM0,%XMM1

0x48f09e SUBSD	(%RSI),%XMM1    [8]

0x48f0a2 MOVSD	%XMM1,(%RSI)    [8]

0x48f0a6 MOVAPD	%XMM0,%XMM1

0x48f0aa SUBSD	(%RAX),%XMM1    [2]

0x48f0ae MOVSD	%XMM1,(%RAX)    [2]

0x48f0b2 SUBSD	(%RDX),%XMM0    [13]

0x48f0b6 ADD	%R15,%RAX

0x48f0b9 MOVSD	%XMM0,(%RDX)    [13]

0x48f0bd ADD	%R15,%RDX

0x48f0c0 CMP	%RDI,(%RSP)    [12]

0x48f0c4 JG	48f020

  ***  This Panel is Intentionally Left Blank.  ***

It is due to a lack of debug symbols in the given object
(loop or function).

Coverage (%)	Name	Source Location	Module
►96.87+	gomp_thread_start	team.c:130	libgomp.so.1.0.0
○	start_thread		libc.so.6
○	__GI___clone3		libc.so.6
►2.76+	GOMP_parallel	libgomp.h:982	libgomp.so.1.0.0
○	void Kripke::DispatchHelper<Kr[...]		exec
○	Kripke::Kernel::sweepSubdomain[...]		exec
○	Kripke::SweepSolver(Kripke::Co[...]		exec
○	Kripke::SteadyStateSolver(Krip[...]		exec
○	main		exec
○	__libc_start_call_main		libc.so.6
○	__libc_start_main		libc.so.6
○	_start		exec

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	3.53
CQA speedup if fully vectorized	8.00
CQA speedup if no inter-iteration dependency	NA
CQA speedup if next bottleneck killed	2.46
Bottlenecks	P10, P11,
Function	void RAJA::internal::StatementExecutor, RAJA::statement::For<2l, RAJA::policy::sequential::seq_exec, RAJA::statement::For<3l, RAJA::policy::sequential::seq_exec, RAJA::statement::For<4l, RAJA::policy::sequential::seq_exec, RAJA::statement::Lambda<0l> > > > >, RAJA::internal::LoopTypes, camp::list > >::exec, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long> >, camp::tuple<>, camp::resources::v1::Host, SweepSdom::operator() >(Kripke::ArchLayoutT, Kripke::Core::DataStore&, Kripke::SdomId) const::{lambda(Kripke::Direction, Kripke::Group, Kripke::ZoneK, Kripke::ZoneJ, Kripke::ZoneI)#1}>&>(RAJA::internal::LoopData, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long> >, camp::tuple<>, camp::resources::v1::Host, SweepSdom::operator() >(Kripke::ArchLayoutT, Kripke::Core::DataStore&, Kripke::SdomId) const::{lambda(Kripke::Direction, Kripke::Group, Kripke::ZoneK, Kripke::ZoneJ, Kripke::ZoneI)#1}>&) [clone ._omp_fn.0]
Source
Source loop unroll info	NA
Source loop unroll confidence level	NA
Unroll/vectorization loop type	NA
Unroll factor	NA
CQA cycles	16.00
CQA cycles if no scalar integer	16.00
CQA cycles if FP arith vectorized	4.53
CQA cycles if fully vectorized	2.00
Front-end cycles	5.00
P0 cycles	1.33
P1 cycles	1.33
P2 cycles	1.33
P3 cycles	1.33
P4 cycles	1.33
P5 cycles	1.33
P6 cycles	4.75
P7 cycles	4.75
P8 cycles	4.75
P9 cycles	4.75
P10 cycles	3.50
P11 cycles	3.50
P12 cycles	6.50
P13 cycles	6.50
P14 cycles	2.00
P15 cycles	2.00
DIV/SQRT cycles	16.00
Inter-iter dependencies cycles	1
FE+BE cycles (UFS)	NA
Stall cycles (UFS)	NA
Nb insns	41.00
Nb uops	40.00
Nb loads	15.00
Nb stores	4.00
Nb stack references	2.00
FLOP/cycle	1.25
Nb FLOP add-sub	13.00
Nb FLOP mul	3.00
Nb FLOP fma	0.00
Nb FLOP div	4.00
Nb FLOP rcp	0.00
Nb FLOP sqrt	0.00
Nb FLOP rsqrt	0.00
Bytes/cycle	9.50
Bytes prefetched	0.00
Bytes loaded	120.00
Bytes stored	32.00
Stride 0	7.00
Stride 1	0.00
Stride n	0.00
Stride unknown	6.00
Stride indirect	0.00
Vectorization ratio all	6.25
Vectorization ratio load	0.00
Vectorization ratio store	0.00
Vectorization ratio mul	0.00
Vectorization ratio add_sub	0.00
Vectorization ratio fma	NA
Vectorization ratio div_sqrt	0.00
Vectorization ratio other	100.00
Vector-efficiency ratio all	13.28
Vector-efficiency ratio load	12.50
Vector-efficiency ratio store	12.50
Vector-efficiency ratio mul	12.50
Vector-efficiency ratio add_sub	12.50
Vector-efficiency ratio fma	NA
Vector-efficiency ratio div_sqrt	12.50
Vector-efficiency ratio other	25.00

Metric	Value
CQA speedup if no scalar integer	1.00
CQA speedup if FP arith vectorized	3.53
CQA speedup if fully vectorized	8.00
CQA speedup if no inter-iteration dependency	NA
CQA speedup if next bottleneck killed	2.46
Bottlenecks	P10, P11,
Function	void RAJA::internal::StatementExecutor, RAJA::statement::For<2l, RAJA::policy::sequential::seq_exec, RAJA::statement::For<3l, RAJA::policy::sequential::seq_exec, RAJA::statement::For<4l, RAJA::policy::sequential::seq_exec, RAJA::statement::Lambda<0l> > > > >, RAJA::internal::LoopTypes, camp::list > >::exec, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long> >, camp::tuple<>, camp::resources::v1::Host, SweepSdom::operator() >(Kripke::ArchLayoutT, Kripke::Core::DataStore&, Kripke::SdomId) const::{lambda(Kripke::Direction, Kripke::Group, Kripke::ZoneK, Kripke::ZoneJ, Kripke::ZoneI)#1}>&>(RAJA::internal::LoopData, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long> >, camp::tuple<>, camp::resources::v1::Host, SweepSdom::operator() >(Kripke::ArchLayoutT, Kripke::Core::DataStore&, Kripke::SdomId) const::{lambda(Kripke::Direction, Kripke::Group, Kripke::ZoneK, Kripke::ZoneJ, Kripke::ZoneI)#1}>&) [clone ._omp_fn.0]
Source
Source loop unroll info	NA
Source loop unroll confidence level	NA
Unroll/vectorization loop type	NA
Unroll factor	NA
CQA cycles	16.00
CQA cycles if no scalar integer	16.00
CQA cycles if FP arith vectorized	4.53
CQA cycles if fully vectorized	2.00
Front-end cycles	5.00
P0 cycles	1.33
P1 cycles	1.33
P2 cycles	1.33
P3 cycles	1.33
P4 cycles	1.33
P5 cycles	1.33
P6 cycles	4.75
P7 cycles	4.75
P8 cycles	4.75
P9 cycles	4.75
P10 cycles	3.50
P11 cycles	3.50
P12 cycles	6.50
P13 cycles	6.50
P14 cycles	2.00
P15 cycles	2.00
DIV/SQRT cycles	16.00
Inter-iter dependencies cycles	1
FE+BE cycles (UFS)	NA
Stall cycles (UFS)	NA
Nb insns	41.00
Nb uops	40.00
Nb loads	15.00
Nb stores	4.00
Nb stack references	2.00
FLOP/cycle	1.25
Nb FLOP add-sub	13.00
Nb FLOP mul	3.00
Nb FLOP fma	0.00
Nb FLOP div	4.00
Nb FLOP rcp	0.00
Nb FLOP sqrt	0.00
Nb FLOP rsqrt	0.00
Bytes/cycle	9.50
Bytes prefetched	0.00
Bytes loaded	120.00
Bytes stored	32.00
Stride 0	7.00
Stride 1	0.00
Stride n	0.00
Stride unknown	6.00
Stride indirect	0.00
Vectorization ratio all	6.25
Vectorization ratio load	0.00
Vectorization ratio store	0.00
Vectorization ratio mul	0.00
Vectorization ratio add_sub	0.00
Vectorization ratio fma	NA
Vectorization ratio div_sqrt	0.00
Vectorization ratio other	100.00
Vector-efficiency ratio all	13.28
Vector-efficiency ratio load	12.50
Vector-efficiency ratio store	12.50
Vector-efficiency ratio mul	12.50
Vector-efficiency ratio add_sub	12.50
Vector-efficiency ratio fma	NA
Vector-efficiency ratio div_sqrt	12.50
Vector-efficiency ratio other	25.00

Path /

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

Function	void RAJA::internal::StatementExecutor, RAJA::statement::For<2l, RAJA::policy::sequential::seq_exec, RAJA::statement::For<3l, RAJA::policy::sequential::seq_exec, RAJA::statement::For<4l, RAJA::policy::sequential::seq_exec, RAJA::statement::Lambda<0l> > > > >, RAJA::internal::LoopTypes, camp::list > >::exec, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long> >, camp::tuple<>, camp::resources::v1::Host, SweepSdom::operator() >(Kripke::ArchLayoutT, Kripke::Core::DataStore&, Kripke::SdomId) const::{lambda(Kripke::Direction, Kripke::Group, Kripke::ZoneK, Kripke::ZoneJ, Kripke::ZoneI)#1}>&>(RAJA::internal::LoopData, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long> >, camp::tuple<>, camp::resources::v1::Host, SweepSdom::operator() >(Kripke::ArchLayoutT, Kripke::Core::DataStore&, Kripke::SdomId) const::{lambda(Kripke::Direction, Kripke::Group, Kripke::ZoneK, Kripke::ZoneJ, Kripke::ZoneI)#1}>&) [clone ._omp_fn.0]
Source file and lines
Module	exec

gain
potential
hint
expert

Vectorization

Your loop is probably not vectorized. Only 13% of vector register length is used (average across all SSE/AVX instructions). By vectorizing your loop, you can lower the cost of an iteration from 16.00 to 2.00 cycles (8.00x speedup).

Details

Store and arithmetical 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
- If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA)

Execution units bottlenecks

Performance is limited by execution of divide and square root operations (the divide/square root unit is a bottleneck). By removing all these bottlenecks, you can lower the cost of an iteration from 16.00 to 6.50 cycles (2.46x speedup).

Workaround

Reduce the number of division or square root instructions:
- If denominator is constant over iterations, use reciprocal (replace x/y with x*(1/y)). Check precision impact.
Check whether you really need double precision. If not, switch to single precision to speedup execution

Expensive FP math instructions/calls

Detected performance impact from expensive FP math instructions/calls. By removing/reexpressing them, you can lower the cost of an iteration from 16.00 to 8.50 cycles (1.88x speedup).

FMA

Presence of both ADD/SUB and MUL operations.

Workaround

Pass to your compiler a micro-architecture specialization option:
- Please look into your compiler manual for march=native or equivalent
Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible. For instance a + b*c is a valid FMA (MUL then ADD). However (a+b)* c cannot be translated into an FMA (ADD then MUL).

Slow data structures access

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

Details

Constant unknown stride: 6 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

20 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).

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

The binary loop is composed of 20 FP arithmetical operations:

13: addition or subtraction
3: multiply
4: divide

The binary loop is loading 120 bytes (15 double precision FP elements). The binary loop is storing 32 bytes (4 double precision FP elements).

Arithmetic intensity

Arithmetic intensity is 0.13 FP operations per loaded or stored byte.

General properties

nb instructions	41
nb uops	40
loop length	170
used x86 registers	16
used mmx registers	0
used xmm registers	5
used ymm registers	0
used zmm registers	0
nb stack references	2
ADD-SUB / MUL ratio	4.33

Front-end

ASSUMED MACRO FUSION FIT IN UOP CACHE

micro-operation queue	5.00 cycles
front end	5.00 cycles

Back-end

	P0	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11	P12	P13	P14	P15
uops	1.33	1.33	1.33	1.33	1.33	1.33	4.75	4.75	4.75	4.75	3.50	3.50	6.50	6.50	2.00	2.00
cycles	1.33	1.33	1.33	1.33	1.33	1.33	4.75	4.75	4.75	4.75	3.50	3.50	6.50	6.50	2.00	2.00

Execution ports to units layout:

P0: ALU
P1: ALU
P2: ALU
P3: ALU, BRU
P4: ALU, BRU
P5: ALU, BRU
P6 (512 bits): store address, load
P7 (512 bits): store address, load
P8 (512 bits): store address, load
P9 (512 bits): store address, load
P10 (512 bits): VPU, DIV/SQRT
P11 (512 bits): VPU, DIV/SQRT
P12 (512 bits): VPU
P13 (512 bits): VPU
P14 (512 bits): FP store data
P15 (512 bits): FP store data

Cycles executing div or sqrt instructions	16.00
Longest recurrence chain latency (RecMII)	1.00

Cycles summary

Front-end	5.00
Dispatch	6.50
DIV/SQRT	16.00
Data deps.	1.00
Overall L1	16.00

Vectorization ratios

all	6%
load	0%
store	0%
mul	0%
add-sub	0%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	0%
other	100%

Vector efficiency ratios

all	13%
load	12%
store	12%
mul	12%
add-sub	12%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	12%
other	25%

Cycles and memory resources usage

Assuming all data fit into the L1 cache, each iteration of the binary loop takes 16.00 cycles. At this rate:

2% of peak load performance is reached (7.50 out of 256.00 bytes loaded per cycle (GB/s @ 1GHz))
1% of peak store performance is reached (2.00 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: 48f020

Instruction	Nb FU	P0	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11	P12	P13	P14	P15	Latency	Recip. throughput	Vectorization
MOVSD (%RAX),%XMM4	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.50	scal (12.5%)
MOVSD (%R12),%XMM3	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.50	scal (12.5%)
ADD $0x1,%RDI	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
MOVSD (%RBP),%XMM1	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.50	scal (12.5%)
MOVSD (%RSI),%XMM0	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.50	scal (12.5%)
ADDSD %XMM3,%XMM3	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
DIVSD (%R11),%XMM3	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0.50	0.50	0	0	0	0	13	4	scal (12.5%)
MULSD %XMM3,%XMM4	1	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	0	0	3	0.50	scal (12.5%)
MOVSD (%R13),%XMM2	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.50	scal (12.5%)
ADDSD %XMM1,%XMM1	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
DIVSD (%R8),%XMM1	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0.50	0.50	0	0	0	0	13	4	scal (12.5%)
MULSD %XMM1,%XMM0	1	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	0	0	3	0.50	scal (12.5%)
ADD %R15,%R8	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
ADDSD (%R10),%XMM0	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
ADDSD %XMM2,%XMM2	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
DIVSD (%R14),%XMM2	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0.50	0.50	0	0	0	0	13	4	scal (12.5%)
ADD %RBX,%R10	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
ADDSD %XMM3,%XMM1	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
ADDSD %XMM4,%XMM0	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
MOVSD (%RDX),%XMM4	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.50	scal (12.5%)
ADDSD %XMM2,%XMM1	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
ADDSD (%R9),%XMM1	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
ADD %RBX,%R9	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
MULSD %XMM2,%XMM4	1	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	0	0	3	0.50	scal (12.5%)
ADDSD %XMM4,%XMM0	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
DIVSD %XMM1,%XMM0	1	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	0	0	13	4	scal (12.5%)
MOVSD %XMM0,(%RCX)	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0.50	0.50	1	0.50	scal (12.5%)
ADDSD %XMM0,%XMM0	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
ADD %RBX,%RCX	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
MOVAPD %XMM0,%XMM1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.17	vect (25.0%)
SUBSD (%RSI),%XMM1	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
MOVSD %XMM1,(%RSI)	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0.50	0.50	1	0.50	scal (12.5%)
MOVAPD %XMM0,%XMM1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.17	vect (25.0%)
SUBSD (%RAX),%XMM1	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
MOVSD %XMM1,(%RAX)	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0.50	0.50	1	0.50	scal (12.5%)
SUBSD (%RDX),%XMM0	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
ADD %R15,%RAX	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
MOVSD %XMM0,(%RDX)	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0.50	0.50	1	0.50	scal (12.5%)
ADD %R15,%RDX	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
CMP %RDI,(%RSP)	1	0.17	0.17	0.17	0.17	0.17	0.17	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.25	N/A
JG 48f020 <_ZN4RAJA8internal17StatementExecutorINS_9statement8CollapseINS_26omp_parallel_collapse_execEN4camp7int_seqIlJLl0ELl1EEEEJNS2_3ForILl2ENS_6policy10sequential8seq_execEJNS8_ILl3ESB_JNS8_ILl4ESB_JNS2_6LambdaILl0EJEEEEEEEEEEEEEEENS0_9LoopTypesINS5_4listIJvvvvvEEESK_EEE4execIRNS0_8LoopDataINS5_5tupleIJNS_4SpanINS_9Iterators16numeric_iteratorIN6Kripke9DirectionElPSU_EElEENSQ_INSS_INST_5GroupElPSY_EElEENSQ_INSR_24strided_numeric_iteratorINST_5ZoneKElPS13_EElEENSQ_INS12_INST_5ZoneJElPS17_EElEENSQ_INS12_INST_5ZoneIElPS1B_EElEEEEENSP_IJEEENS5_9resources2v14HostEJZNK9SweepSdomclINST_11ArchLayoutTINST_12ArchT_OpenMPENST_11LayoutT_DGZEEEEEvT_RNST_4Core9DataStoreENST_6SdomIdEEUlSU_SY_S13_S17_S1B_E_EEEEEvOS1Q_._omp_fn.0+0xd80>	1	0	0	0	0.33	0.33	0.33	0	0	0	0	0	0	0	0	0	0	1	0.50-1	N/A

Function	void RAJA::internal::StatementExecutor, RAJA::statement::For<2l, RAJA::policy::sequential::seq_exec, RAJA::statement::For<3l, RAJA::policy::sequential::seq_exec, RAJA::statement::For<4l, RAJA::policy::sequential::seq_exec, RAJA::statement::Lambda<0l> > > > >, RAJA::internal::LoopTypes, camp::list > >::exec, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long> >, camp::tuple<>, camp::resources::v1::Host, SweepSdom::operator() >(Kripke::ArchLayoutT, Kripke::Core::DataStore&, Kripke::SdomId) const::{lambda(Kripke::Direction, Kripke::Group, Kripke::ZoneK, Kripke::ZoneJ, Kripke::ZoneI)#1}>&>(RAJA::internal::LoopData, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long>, RAJA::Span, long> >, camp::tuple<>, camp::resources::v1::Host, SweepSdom::operator() >(Kripke::ArchLayoutT, Kripke::Core::DataStore&, Kripke::SdomId) const::{lambda(Kripke::Direction, Kripke::Group, Kripke::ZoneK, Kripke::ZoneJ, Kripke::ZoneI)#1}>&) [clone ._omp_fn.0]
Source file and lines
Module	exec

gain
potential
hint
expert

Vectorization

Details

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
- If your loop streams arrays of structures (AoS), try to use structures of arrays instead (SoA)

Execution units bottlenecks

Workaround

Reduce the number of division or square root instructions:
- If denominator is constant over iterations, use reciprocal (replace x/y with x*(1/y)). Check precision impact.
Check whether you really need double precision. If not, switch to single precision to speedup execution

Expensive FP math instructions/calls

Detected performance impact from expensive FP math instructions/calls. By removing/reexpressing them, you can lower the cost of an iteration from 16.00 to 8.50 cycles (1.88x speedup).

FMA

Presence of both ADD/SUB and MUL operations.

Workaround

Pass to your compiler a micro-architecture specialization option:
- Please look into your compiler manual for march=native or equivalent
Try to change order in which elements are evaluated (using parentheses) in arithmetic expressions containing both ADD/SUB and MUL operations to enable your compiler to generate FMA instructions wherever possible. For instance a + b*c is a valid FMA (MUL then ADD). However (a+b)* c cannot be translated into an FMA (ADD then MUL).

Slow data structures access

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

Details

Constant unknown stride: 6 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

20 SSE or AVX instructions are processing arithmetic or math operations on double precision FP elements in scalar mode (one at a time).

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

The binary loop is composed of 20 FP arithmetical operations:

13: addition or subtraction
3: multiply
4: divide

The binary loop is loading 120 bytes (15 double precision FP elements). The binary loop is storing 32 bytes (4 double precision FP elements).

Arithmetic intensity

Arithmetic intensity is 0.13 FP operations per loaded or stored byte.

General properties

nb instructions	41
nb uops	40
loop length	170
used x86 registers	16
used mmx registers	0
used xmm registers	5
used ymm registers	0
used zmm registers	0
nb stack references	2
ADD-SUB / MUL ratio	4.33

Front-end

ASSUMED MACRO FUSION FIT IN UOP CACHE

micro-operation queue	5.00 cycles
front end	5.00 cycles

Back-end

	P0	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11	P12	P13	P14	P15
uops	1.33	1.33	1.33	1.33	1.33	1.33	4.75	4.75	4.75	4.75	3.50	3.50	6.50	6.50	2.00	2.00
cycles	1.33	1.33	1.33	1.33	1.33	1.33	4.75	4.75	4.75	4.75	3.50	3.50	6.50	6.50	2.00	2.00

Execution ports to units layout:

P0: ALU
P1: ALU
P2: ALU
P3: ALU, BRU
P4: ALU, BRU
P5: ALU, BRU
P6 (512 bits): store address, load
P7 (512 bits): store address, load
P8 (512 bits): store address, load
P9 (512 bits): store address, load
P10 (512 bits): VPU, DIV/SQRT
P11 (512 bits): VPU, DIV/SQRT
P12 (512 bits): VPU
P13 (512 bits): VPU
P14 (512 bits): FP store data
P15 (512 bits): FP store data

Cycles executing div or sqrt instructions	16.00
Longest recurrence chain latency (RecMII)	1.00

Cycles summary

Front-end	5.00
Dispatch	6.50
DIV/SQRT	16.00
Data deps.	1.00
Overall L1	16.00

Vectorization ratios

all	6%
load	0%
store	0%
mul	0%
add-sub	0%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	0%
other	100%

Vector efficiency ratios

all	13%
load	12%
store	12%
mul	12%
add-sub	12%
fma	NA (no fma vectorizable/vectorized instructions)
div/sqrt	12%
other	25%

Cycles and memory resources usage

Assuming all data fit into the L1 cache, each iteration of the binary loop takes 16.00 cycles. At this rate:

2% of peak load performance is reached (7.50 out of 256.00 bytes loaded per cycle (GB/s @ 1GHz))
1% of peak store performance is reached (2.00 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: 48f020

Instruction	Nb FU	P0	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11	P12	P13	P14	P15	Latency	Recip. throughput	Vectorization
MOVSD (%RAX),%XMM4	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.50	scal (12.5%)
MOVSD (%R12),%XMM3	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.50	scal (12.5%)
ADD $0x1,%RDI	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
MOVSD (%RBP),%XMM1	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.50	scal (12.5%)
MOVSD (%RSI),%XMM0	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.50	scal (12.5%)
ADDSD %XMM3,%XMM3	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
DIVSD (%R11),%XMM3	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0.50	0.50	0	0	0	0	13	4	scal (12.5%)
MULSD %XMM3,%XMM4	1	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	0	0	3	0.50	scal (12.5%)
MOVSD (%R13),%XMM2	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.50	scal (12.5%)
ADDSD %XMM1,%XMM1	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
DIVSD (%R8),%XMM1	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0.50	0.50	0	0	0	0	13	4	scal (12.5%)
MULSD %XMM1,%XMM0	1	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	0	0	3	0.50	scal (12.5%)
ADD %R15,%R8	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
ADDSD (%R10),%XMM0	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
ADDSD %XMM2,%XMM2	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
DIVSD (%R14),%XMM2	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0.50	0.50	0	0	0	0	13	4	scal (12.5%)
ADD %RBX,%R10	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
ADDSD %XMM3,%XMM1	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
ADDSD %XMM4,%XMM0	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
MOVSD (%RDX),%XMM4	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.50	scal (12.5%)
ADDSD %XMM2,%XMM1	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
ADDSD (%R9),%XMM1	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
ADD %RBX,%R9	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
MULSD %XMM2,%XMM4	1	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	0	0	3	0.50	scal (12.5%)
ADDSD %XMM4,%XMM0	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
DIVSD %XMM1,%XMM0	1	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	0	0	13	4	scal (12.5%)
MOVSD %XMM0,(%RCX)	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0.50	0.50	1	0.50	scal (12.5%)
ADDSD %XMM0,%XMM0	1	0	0	0	0	0	0	0	0	0	0	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
ADD %RBX,%RCX	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
MOVAPD %XMM0,%XMM1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.17	vect (25.0%)
SUBSD (%RSI),%XMM1	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
MOVSD %XMM1,(%RSI)	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0.50	0.50	1	0.50	scal (12.5%)
MOVAPD %XMM0,%XMM1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.17	vect (25.0%)
SUBSD (%RAX),%XMM1	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
MOVSD %XMM1,(%RAX)	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0.50	0.50	1	0.50	scal (12.5%)
SUBSD (%RDX),%XMM0	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0.50	0.50	0	0	2	0.50	scal (12.5%)
ADD %R15,%RAX	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
MOVSD %XMM0,(%RDX)	1	0	0	0	0	0	0	0.25	0.25	0.25	0.25	0	0	0	0	0.50	0.50	1	0.50	scal (12.5%)
ADD %R15,%RDX	1	0.17	0.17	0.17	0.17	0.17	0.17	0	0	0	0	0	0	0	0	0	0	1	0.17	N/A
CMP %RDI,(%RSP)	1	0.17	0.17	0.17	0.17	0.17	0.17	0.25	0.25	0.25	0.25	0	0	0	0	0	0	1	0.25	N/A
JG 48f020 <_ZN4RAJA8internal17StatementExecutorINS_9statement8CollapseINS_26omp_parallel_collapse_execEN4camp7int_seqIlJLl0ELl1EEEEJNS2_3ForILl2ENS_6policy10sequential8seq_execEJNS8_ILl3ESB_JNS8_ILl4ESB_JNS2_6LambdaILl0EJEEEEEEEEEEEEEEENS0_9LoopTypesINS5_4listIJvvvvvEEESK_EEE4execIRNS0_8LoopDataINS5_5tupleIJNS_4SpanINS_9Iterators16numeric_iteratorIN6Kripke9DirectionElPSU_EElEENSQ_INSS_INST_5GroupElPSY_EElEENSQ_INSR_24strided_numeric_iteratorINST_5ZoneKElPS13_EElEENSQ_INS12_INST_5ZoneJElPS17_EElEENSQ_INS12_INST_5ZoneIElPS1B_EElEEEEENSP_IJEEENS5_9resources2v14HostEJZNK9SweepSdomclINST_11ArchLayoutTINST_12ArchT_OpenMPENST_11LayoutT_DGZEEEEEvT_RNST_4Core9DataStoreENST_6SdomIdEEUlSU_SY_S13_S17_S1B_E_EEEEEvOS1Q_._omp_fn.0+0xd80>	1	0	0	0	0.33	0.33	0.33	0	0	0	0	0	0	0	0	0	0	1	0.50-1	N/A

Report Configuration

Vectorization

Details

Workaround

Execution units bottlenecks

Workaround

Expensive FP math instructions/calls

FMA

Workaround

Slow data structures access

Details

Workaround

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

Vectorization

Details

Workaround

Execution units bottlenecks

Workaround

Expensive FP math instructions/calls

FMA

Workaround

Slow data structures access

Details

Workaround

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