Polly First successful optimizations - How to proceed? Tobias Grosser, Raghesh A

Transcription

Polly
First successful optimizations - How to proceed?
Tobias Grosser, Raghesh A
November 18, 2011
Polly - First successful optimizations - How to proceed?
November 18, 2011
1 / 27
Me - Tobias Grosser
Doctoral Student at INRIA/ENS, Paris
Interests: Automatic parallelization, data-locality optimizations
Compiler Experience (5 years)
I
I
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GCC/Graphite
Polly
LLVM
clang complete
November 18, 2011
2 / 27
Direct Contributors / Funding
Universities
I
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ENS/INRIA Paris (Albert Cohen)
Ohio State University (P. Sadayappan)
University of Passau (Christian Lengauer)
Funding
I
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Google Europe Fellowship in Efficient Computing
Mentor: Aart Bik
2 x Google Summer of Code Students
NSF Awards 0811781 and 0926688 (partially)
Qualcomm travel support
November 18, 2011
3 / 27
The Problem
Life
is complicated!
November 18, 2011
4 / 27
The Problem
Life
of a programmer
is complicated!
November 18, 2011
5 / 27
Life is complicated - Why?
We want:
Fast and power-efficient code
We have:
SIMD, Caches, Multi-Core, Accelerators
But:
Optimized code is needed
Optimization is complex and not performance portable
Architectures are too diverse to optimize ahead of time
November 18, 2011
6 / 27
Get Polly
Install Polly
http://polly.grosser.es/get_started.html
Load Polly automatically
alias clang
clang -Xclang -load -Xclang LLVMPolly.so
alias opt
opt -load LLVMPolly.so
Default behaviour preserved
clang/opt now provide options to enable Polly
November 18, 2011
7 / 27
Optimize a program with Polly
gemm.c [1024 x 1024 (static size), double]
for (int i = 0; i < N; i++)
for (int j = 0; j < M; j++) {
C[i][j] = 0;
for (int k = 0; k < K; k++)
C[i][j] += A[i][k] + B[k][j];
}
$ clang -O3 gemm.c -o gemm.clang
$ time ./gemm.clang
real 0m15.336s
$ clang -O3 -mllvm -o gemm.polly -mllvm -polly
$ time ./gemm.polly
real 0m2.144s
November 18, 2011
8 / 27
The Architecture
Transformations
* Loop transformations
* Data layout optimizations
* Expose parallelism
Dependency
Analysis
Code Generation
SCoP Detection
SIMD
PSCoP
LLVM IR
LLVM IR
OpenMP
OpenCL
Import
Export
JSCoP
Manual Optimization / PoCC+Pluto
November 18, 2011
9 / 27
Can Polly analyze our code?
entry
entry.split
for.cond1.preheader
$ clang -O3 gemm.c \
-mllvm -polly-show-only \
-mllvm -polly-detect-only=gemm
for.body3
for.body8
Highlight the detected Scops
for.inc22
Only check in function ’gemm’
for.inc25
for.end27
Scop Graph for 'gemm' function
November 18, 2011
10 / 27
Some code can not be analyzed
entry
-mllvm -polly-show-only \
gemm (possible aliasing)
void gemm(double A[N][K],
double B[K][M],
double C[N][M]) {
for (int i = 0; i < N; i++)
for (int j = 0; j < M; j++) {
C[i][j] = 0;
for (int k = 0; k < K; k++)
C[i][j] += A[i][k] + B[k][j];
}
}
entry.split
for.cond1.preheader
for.body3
for.body8
%B may possibly alias
for.inc22
%A may possibly alias
for.inc25
%A may possibly alias
for.end27
November 18, 2011
11 / 27
How to fix it?
entry
Add ’restrict’
void gemm(double A[restrict N][K],
double B[restrict K][M],
double C[restrict N][M]);
entry.split
for.cond1.preheader
for.body3
for.body8
Other options:
Inlining
Improved alias analysis
for.inc22
for.inc25
Run time checks
for.end27
November 18, 2011
12 / 27
Extract polyhedral representation
gemm
for (int i = 0; i < 512; i++)
for (int j = 0; j < 512; j++) {
C[i][j] = 0;
// Stmt1
for (int k = 0; k < 512; k++)
C[i][j] += A[i][k] + B[k][j]; // Stmt2
}
-mllvm -polly-run-export-jscop \
Writing JScop ’for.cond1.preheader => for.end27’ in function ’gemm’ to
’./gemm___%for.cond1.preheader---%for.end27.jscop’.
Domain = {Stmt1 [i, j] : 0 <= i, j < 512;
Schedule = {Stmt1 [i, j] → [i, j, 0];
Stmt2 [i, j, k] : 0 <= i, j, k < 512}
Stmt2 [i, j, k] → [i, j, 1, k]}
Writes = {Stmt1 [i, j] → C [i, j];
Stmt2 [i, j, k] → C [i, j]}
Reads = {Stmt2 [i, j, k] → A[i, k];
Stmt2 [i, j, k] → B[k, j]}
November 18, 2011
13 / 27
Applying transformations
D = {Stmt[i, j] : 0 <= i < 32 ∧ 0 <= j < 1000}
S = {Stmt[i, j] → [i, j]}
S0 = S
for (i = 0; i < 32; i++)
for (j = 0; j < 1000; j++)
A[j][i] += 1;
November 18, 2011
14 / 27
D = {Stmt[i, j] : 0 <= i < 32 ∧ 0 <= j < 1000}
S = {Stmt[i, j] → [i, j]}
TInterchange = {[i, j] → [j, i]}
S 0 = S ◦ TInterchange
for (j = 0; j < 1000; j++)
for (i = 0; i < 32; i++)
A[j][i] += 1;
November 18, 2011
15 / 27
D = {Stmt[i, j] : 0 <= i < 32 ∧ 0 <= j < 1000}
S = {Stmt[i, j] → [i, j]}
TInterchange = {[i, j] → [j, i]}
TStripMine = {[i, j] → [i, jj, j] : jj mod 4 = 0 ∧ jj <= j < jj + 4}
S 0 = S ◦ TInterchange ◦ TStripMine
for (j = 0; j < 1000; j++)
for (ii = 0; ii < 32; ii+=4)
for (i = ii; i < ii+4; i++)
A[j][i] += 1;
November 18, 2011
16 / 27
Polly takes advantage of available parallelism
It creates automatically:
OpenMP calls
for loops that are not surrounded by any other parallel loops
SIMD instructions
for innermost loops with a constant number of iterations
→ Optimizing code becomes the problem of finding the right schedule.
November 18, 2011
17 / 27
Optimizing of Matrix Multiply
9
8
7
Speedup
6
5
4
3
2
1
0
cla
ng
-O3
gcc
-ffa
Po
Po
icc
Po
Po
Po
lly:
lly:
lly:
lly:
lly:
-f
+=
+=
On
+S
+=
st-m ast
l
H
Un
V
t
y
rip
e
o
LLV
ath
c
i
roll
s
tori
min
t
i
n
Ming
- O3
g
z
i
a
n
O3
tion
g
32x32 double, Transposed matric Multiply, C[i][j] += A[k][i] * B[j][k];
R Core
R i5 @ 2.40GH
Intel
November 18, 2011
18 / 27
Automatic optimization with the Pluto algorithm
Polly provides two automatic optimizers
PoCC
ISL
-polly-optimizer=pocc
-polly-optimizer=isl (default)
Original implementation
Reimplementation
We call the pocc binary
ISL is already linked into Polly,
no additional library needed
More mature
Integrated with a large set of
research tools
Still untuned heuristics
Will be used for production.
November 18, 2011
19 / 27
Polly on Polybench - Sequential execution times
Speedup relative to "clang -O3"
5
clang -O3
pollycc -ftile
pollycc -ftile -fvector
4
3
2
1
0
16
14
12
10
8
6
4
2
0
2mm 3mm adi atax bicg cho corr cov doit dur dyn fdtd fdtd gau gem gem gesu gra jaco jaco lu ludc mvt reg seid sym syr2 syrk tris trm
olv m
_de el m k
lesk ela aria gen bin pro -2d -ap ss-f m ver mm msc bi-1 bi-2
mp
tect
g
ml ilter
y tion nce
v hmid d-im d-im
per per
t
clang -O3
pollycc -ftile
pollycc -ftile -fvector
olv m
_de el m k
mp
tect
g
ml ilter
y tion nce
v hmid d-im d-im
per per
t
November 18, 2011
20 / 27
Polly on Polybench - Parallel execution times
25
clang -O3
pollycc -ftile -fparallel
pollycc -ftile -fparallel -fvector
20
15
10
5
0
olv m
_de el m k
mp
tect
g
ml ilter
y tion nce
v hmid d-im d-im
per per
t
120
clang -O3
pollycc -ftile -fparallel
pollycc -ftile -fparallel -fvector
100
80
60
40
20
0
olv m
_de el m k
mp
tect
g
ml ilter
y tion nce
v hmid d-im d-im
per per
t
November 18, 2011
21 / 27
Current Status
Transformations
* Loop transformations
* Data layout optimizations
* Expose parallelism
Dependency
Analysis
Code Generation
SCoP Detection
SIMD
PSCoP
LLVM IR
LLVM IR
OpenMP
OpenCL
Usable for experiments
Planned
Import
Export
Under Construction
JSCoP
Manual Optimization / PoCC+Pluto
November 18, 2011
22 / 27
How to proceed? Where can we copy?
Short Vector Instructions
→ Vectorizing compiler
Data Locality
→ Optimizing compilers
, Pluto
Thread Level Parallelism
→ Optimizing compilers
, Pluto
Vector Accelerators
→ Par4All , C-to-CUDA
, ppcg
The overall problem:
November 18, 2011
23 / 27
Polly
Idea: Integrated vectorization
Target the overall problem
Re-use existing concepts and libraries
November 18, 2011
24 / 27
Next Steps
My agenda:
Data-locality optimizations for larger programs (production quality)
Expose SIMDization opportunities with the core optimizers
Offload computations to vector accelerators
Your ideas?
Use Polly to drive instruction scheduling for VLIW architectures
...
November 18, 2011
25 / 27
Conclusion
Polly
Language Independent
Optimizations for Data-Locality & Parallelism
SIMD & OpenMP code generation support
Planned: OpenCL Generation
http://polly.grosser.es
November 18, 2011
26 / 27
Multi dimensional arrays
#define N;
void foo(int
A[5][5] =
B + 5 * n
C[5][5] =
}
n, float A[][N], float **B, C[][n]) {
1;
+ 5 = 1;
1;
A - Constant Size → already linear
B - Self-made made multi dimensional arrays
I
I
Guess & Prove
Guess & Runtime Check
C - C99 Variable Length Arrays / Fortran Arrays
I
I
I
Guess & Prove
Guess & Runtime Check
Pass information from Clang / GFORTRAN
November 18, 2011
27 / 27

Polly First successful optimizations - How to proceed? Tobias Grosser, Raghesh A

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