Presentation is loading. Please wait.

Presentation is loading. Please wait.

EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB.py OS/HW f() h() Specializer.c PLL Interp Productivity app.so cc/ld $ $ SEJITS logical.

Published byByron Quinn Modified over 9 years ago

Similar presentations

Presentation on theme: "EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB.py OS/HW f() h() Specializer.c PLL Interp Productivity app.so cc/ld $ $ SEJITS logical."— Presentation transcript:

1 EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB.py OS/HW f() h() Specializer.c PLL Interp Productivity app.so cc/ld $ $ SEJITS logical flow Selective Embedded JIT ASP.py Specialization Human-expert- authored templates & strategy engine

2 EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB.py OS/HW f() h() Specializer.c PLL Interp Productivity app.so cc/ld $ $ SEJITS logical flow ASP.py Asp DB

3 EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB Stencil example: input  Sum the distance-1 neighbors in a 2D stencil 3 import stencil_grid import numpy class ExampleKernel(object): def kernel(self, in_grid, out_grid): for x in out_grid.interior_points(): for y in in_grid.neighbors(x, 1): out_grid[x] = out_grid[x] + in_grid[y] in_grid = StencilGrid([5,5]) in_grid.data = numpy.ones([5,5]) out_grid = StencilGrid([5,5]) ExampleKernel().kernel(in_grid, out_grid) from stencil_kernel import *

4 EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB Neighbor iterator expanded Kernel function lowered from Python to C++ Specialized to dimensions of input data Stencil kernel example: unoptimized sequential C++ #include #include "arrayobject.h" namespace private_namespace_000 { void kernel(PyObject *in_grid, PyObject *out_grid) { for (int i = 1; i < 4; i++) { for (int j = 1; j < 4; j++) { #define _out_grid_array_macro(_d0,_d1) ((_d1) + 5 * (_d0)) #define _in_grid_array_macro(_d0,_d1) ((_d1) + 5 * (_d0)) double* _my_out_grid = (double *) PyArray_DATA(out_grid); double* _my_in_grid = (double *) PyArray_DATA(in_grid); int idxout = _out_grid_array_macro(i,j); int idxin = _in_grid_array_macro(i+1,j); _my_out_grid[idxout] = (_my_out_grid[idxout] + _my_in_grid[idxin]); idxin = _in_grid_array_macro(i-1,j); _my_out_grid[idxout] = (_my_out_grid[idxout] + _my_in_grid[idxin]); idxin = _in_grid_array_macro(i,j+1); _my_out_grid[idxout] = (_my_out_grid[idxout] + _my_in_grid[idxin]); idxin = _in_grid_array_macro(i,j-1); _my_out_grid[idxout] = (_my_out_grid[idxout] + _my_in_grid[idxin]); }

5 EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB C++ with loop unrolling by 4 void kernel_unroll_4(PyObject *in_grid, PyObject *out_grid) { for (int _EwzZcfet = 1; (_EwzZcfet <= 256); _EwzZcfet = (_EwzZcfet + 1)) cilk_for (int _HmEYIyVk = 1; (_HmEYIyVk <= 256); _HmEYIyVk += 1) for (int _QXalOpkr = 1; (_QXalOpkr <= 256); _QXalOpkr = (_QXalOpkr + 4)) { #define _out_grid_array_macro(_d0,_d1,_d2) (((_d2) + 258 * (_d1)) + 258 * (_d0)) #define _in_grid_array_macro(_d0,_d1,_d2) (((_d2) + 258 * (_d1)) + 258 * (_d0)) double* _my_out_grid = (double *) PyArray_DATA(out_grid); double* _my_in_grid = (double *) PyArray_DATA(in_grid);; int x; x = _in_grid_array_macro(_EwzZcfet, _HmEYIyVk, _QXalOpkr); { int y; y = _in_grid_array_macro((_EwzZcfet + 1), (_HmEYIyVk + 0), (_QXalOpkr + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + -1), (_HmEYIyVk + 0), (_QXalOpkr + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + 1), (_QXalOpkr + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + -1), (_QXalOpkr + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + 0), (_QXalOpkr + 1)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + 0), (_QXalOpkr + -1)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); } x = _in_grid_array_macro(_EwzZcfet, _HmEYIyVk, (_QXalOpkr+1)); { int y; y = _in_grid_array_macro((_EwzZcfet + 1), (_HmEYIyVk + 0), ((_QXalOpkr+1) + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + -1), (_HmEYIyVk + 0), ((_QXalOpkr+1) + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + 1), ((_QXalOpkr+1) + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + -1), ((_QXalOpkr+1) + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + 0), ((_QXalOpkr+1) + 1)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + 0), ((_QXalOpkr+1) + -1)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); } x = _in_grid_array_macro(_EwzZcfet, _HmEYIyVk, (_QXalOpkr+2)); { int y; y = _in_grid_array_macro((_EwzZcfet + 1), (_HmEYIyVk + 0), ((_QXalOpkr+2) + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + -1), (_HmEYIyVk + 0), ((_QXalOpkr+2) + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + 1), ((_QXalOpkr+2) + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + -1), ((_QXalOpkr+2) + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + 0), ((_QXalOpkr+2) + 1)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + 0), ((_QXalOpkr+2) + -1)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); } x = _in_grid_array_macro(_EwzZcfet, _HmEYIyVk, (_QXalOpkr+3)); { int y; y = _in_grid_array_macro((_EwzZcfet + 1), (_HmEYIyVk + 0), ((_QXalOpkr+3) + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + -1), (_HmEYIyVk + 0), ((_QXalOpkr+3) + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + 1), ((_QXalOpkr+3) + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + -1), ((_QXalOpkr+3) + 0)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + 0), ((_QXalOpkr+3) + 1)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); y = _in_grid_array_macro((_EwzZcfet + 0), (_HmEYIyVk + 0), ((_QXalOpkr+3) + -1)); _my_out_grid[x] = (_my_out_grid[x] + ((1.0 / 6.0) * _my_in_grid[y])); } 5

6 EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB Fully optimized, parallel C++ int c2; for (c2=chunkOffset_2;c2<=255;c2+=256) { int c1; for (c1=chunkOffset_1;c1<=255;c1+=8) { int c0; for (c0=chunkOffset_0;c0<=255;c0+=256) { int b2; for (b2=c2 + threadOffset_2;b2<=c2 + 255;b2+=256) { int b1; for (b1=c1 + threadOffset_1;b1<=c1 + 7;b1+=8) { int b0; for (b0=c0 + threadOffset_0;b0<=c0 + 255;b0+=256) { int k; for (k=b2 + 1;k<=b2 + 256;k+=2) { int j; for (j=b1 + 1;j<=b1 + 8;j+=2) { int i; for (i=b0 + 1;i<=b0 + 256;i+=8) { Anext[_Anext_Index(i - 1,j - 1,k - 1)] = fac * A0[_A0_Index(i - 1,j - 1,k - 1)] + fac2 * (A0[_A0_Index(i,j - 1,k - 1)] + A0[_A0_Index(i - 2,j - 1,k - 1)] + A0[_A0_Index(i - 1,j,k - 1)] + A0[_A0_Index(i - 1,j - 2,k - 1)] + A0[_A0_Index(i - 1,j - 1,k)] + A0[_A0_Index(i - 1,j - 1,k - 2)]); Anext[_Anext_Index(i,j - 1,k - 1)] = fac * A0[_A0_Index(i,j - 1,k - 1)] + fac2 * (A0[_A0_Index(i + 1,j - 1,k - 1)] + A0[_A0_Index(i - 1,j - 1,k - 1)] + A0[_A0_Index(i,j,k - 1)] + A0[_A0_Index(i,j - 2,k - 1)] + A0[_A0_Index(i,j - 1,k)] + A0[_A0_Index(i,j - 1,k - 2)]); Anext[_Anext_Index(i + 1,j - 1,k - 1)] = fac * A0[_A0_Index(i + 1,j - 1,k - 1)] + fac2 * (A0[_A0_Index(i + 2,j - 1,k - 1)] + A0[_A0_Index(i,j - 1,k - 1)] + A0[_A0_Index(i + 1,j,k - 1)] + A0[_A0_Index(i + 1,j - 2,k - 1)] + A0[_A0_Index(i + 1,j - 1,k)] + A0[_A0_Index(i + 1,j - 1,k - 2)]); Anext[_Anext_Index(i + 2,j - 1,k - 1)] = fac * A0[_A0_Index(i + 2,j - 1,k - 1)] + fac2 * (A0[_A0_Index(i + 3,j - 1,k - 1)] + A0[_A0_Index(i + 1,j - 1,k - 1)] + A0[_A0_Index(i + 2,j,k - 1)] + A0[_A0_Index(i + 2,j - 2,k - 1)] + A0[_A0_Index(i + 2,j - 1,k)] + A0[_A0_Index(i + 2,j - 1,k - 2)]);............. ~70 similar lines............ } 6

7 EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB Phase 1: Python AST => Domain AST def kernel(self, in_grid, out_grid): for x in out_grid.interior_points(): for y in in_grid.neighbors(x, 1): out_grid[x] = out_grid[x] + in_grid[y] def visit_for(self, node): if (node.isFunctionCall("interior_points")): grid = self.visit(node.iter.func.value).id body = map(self.visit, node.body) target = self.visit(node.target) newnode = StencilKernel.StencilInterior(grid, body, target) return newnode … Visit each AST node of Python call to specializer (available via Python’s introspection) Convert calls to interior_points and neighbors to domain- specific, target-independent AST nodes for later processing Note use of Python in manipulating/traversing AST

8 EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB AST phase 1 8 Call interior_point s For x iter Call neighbors(x,1) For y iter := out_grid[x]+ in_grid[y] StencilInterior out_grid, x StencilNeighbor in_grid, y, 1 := out_grid[x]+ in_grid[y] Generic=>Domain-Specific for x in out_grid.interior_points for y in in_grid.neighbors out_grid[x] = out_grid[x] + in_grid[y]

9 EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB out_grid[x] = out_grid[x] + in_grid[y] AST phase 2: Optimize DAST 9 For ( i over GridSize) := + Initialize variables … …… := +… …… +… …… +… …… Unrolled Neighbor Loop For ( j over GridSize) StencilInterior out_grid, x StencilNeighbor in_grid, y, 1 := out_grid[x] + in_grid[y] LoopUnroller().unroll(node, factor)

10 EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB AST phase 3 & 4: bind platform, generate/compile/cache code  Transform to platform-specific AST (PAST)  optimizations can be reused across "similar" platforms (eg, SIMDize generically, let compiler map onto ISA) 10 For ( i over GridSize) := Initialize variables := Unrolled Neighbor Loop For ( j over GridSize) CilkFor(...) := Initialize variables := block scope {... } CilkFor(...)

11 EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB Who writes what Application Specializer ASP core CodePy Kernel Python AST Target AST ASP Module Utilities Compiled module Kernel call Input data Kernel call Cache hit App author (PLL) Specializer author (ELL) SEJITS team 3 rd party library

12 EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB Results summary CA matrix powersStencilGMM Performance3x–9x faster than optimized C++ 53% of achievable peak mem BW Variant selection outperforms hand- coded CUDA on large problems PortabilityOpenMP; CILK + soon OpenMP, CILK+CUDA, CILK+ specializer LOC (logic + templates) 1320 + 318338 + 0675 + 3184 app LOC reductionN/A~20x~15x SEJITS benefitDelivery vehicle for first autotuned CA- Akx Inlining functionsStrategy selection hides hetero- geneous HW App integrationLinear solversCAT scan analysisSpeech recognition 12

Download ppt "EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB.py OS/HW f() h() Specializer.c PLL Interp Productivity app.so cc/ld $ $ SEJITS logical."

Similar presentations

Lecture 1 -- 1Computer Science I - Martin Hardwick Strings #include using namespace std; int main () { string word; cout

Lecture Computer Science I - Martin Hardwick Strings #include using namespace std; int main () { string word; cout
AP Computer Science Anthony Keen. Computer 101 What happens when you turn a computer on? –BIOS tries to start a system loader –A system loader tries to.

AP Computer Science Anthony Keen. Computer 101 What happens when you turn a computer on? –BIOS tries to start a system loader –A system loader tries to.
EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB P A R A L L E L C O M P U T I N G L A B O R A T O R Y EECS Electrical Engineering.

EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB P A R A L L E L C O M P U T I N G L A B O R A T O R Y EECS Electrical Engineering.
Dependency Test in Loops By Amala Gandhi. Data Dependence Three types of data dependence: 1. Flow (True) dependence : read-after-write int a, b, c; a.

Dependency Test in Loops By Amala Gandhi. Data Dependence Three types of data dependence: 1. Flow (True) dependence : read-after-write int a, b, c; a.
EXAMPLES (Arrays). Example Many engineering and scientific applications represent data as a 2-dimensional grid of values; say brightness of pixels in.

EXAMPLES (Arrays). Example Many engineering and scientific applications represent data as a 2-dimensional grid of values; say brightness of pixels in.
Factorial Preparatory Exercise #include using namespace std; double fact(double); int fact(int); int main(void) { const int n=20; ofstream my_file(results.txt);

Factorial Preparatory Exercise #include using namespace std; double fact(double); int fact(int); int main(void) { const int n=20; ofstream my_file("results.txt");
ELEC 206 Computer Applications for Electrical Engineers Dr. Ron Hayne

ELEC 206 Computer Applications for Electrical Engineers Dr. Ron Hayne
EE3190 Optical Sensing and Imaging, Fall 2004 © Timothy J. Schulz EE3190 Optical Sensing and Imaging Computing PSFs with a digital computer.

EE3190 Optical Sensing and Imaging, Fall 2004 © Timothy J. Schulz EE3190 Optical Sensing and Imaging Computing PSFs with a digital computer.
School of Engineering & Technology Computer Architecture Pipeline.

School of Engineering & Technology Computer Architecture Pipeline.
1 Convert this problem into our standard form: Minimize 3 x 1 + 4.5 x 2 – 6 x 3 subject to 1 x 1 - 2 x 2 – 3 x 3 ≥ 6.2 4 x 1 + 5 x 2 – 6 x 3 ≤ 5 7 x 1.

1 Convert this problem into our standard form: Minimize 3 x x 2 – 6 x 3 subject to 1 x x 2 – 3 x 3 ≥ x x 2 – 6 x 3 ≤ 5 7 x 1.
Riyadh Philanthropic Society For Science Prince Sultan College For Woman Dept. of Computer & Information Sciences CS 102 Computer Programming II (Lab:

Riyadh Philanthropic Society For Science Prince Sultan College For Woman Dept. of Computer & Information Sciences CS 102 Computer Programming II (Lab:
C++ Basics March 10th. A C++ program //if necessary include headers //#include void main() { //variable declaration //read values input from user //computation.

C++ Basics March 10th. A C++ program //if necessary include headers //#include void main() { //variable declaration //read values input from user //computation.
Tinaliah, S. Kom.. * * * * * * * * * * * * * * * * * #include using namespace std; void main () { for (int i = 1; i

Tinaliah, S. Kom.. * * * * * * * * * * * * * * * * * #include using namespace std; void main () { for (int i = 1; i
Triana Elizabeth, S.Kom. #include using namespace std; void main () { for (int i = 1; i

Triana Elizabeth, S.Kom. #include using namespace std; void main () { for (int i = 1; i
Selective, Embedded Just-in- Time Specialization (SEJITS) As a platform for implementing communication-avoiding algorithms accessible from Python.

Selective, Embedded Just-in- Time Specialization (SEJITS) As a platform for implementing communication-avoiding algorithms accessible from Python.
Lecture 8 Analysis of Recursive Algorithms King Fahd University of Petroleum & Minerals College of Computer Science & Engineering Information & Computer.

Lecture 8 Analysis of Recursive Algorithms King Fahd University of Petroleum & Minerals College of Computer Science & Engineering Information & Computer.
P A R A L L E L C O M P U T I N G L A B O R A T O R Y EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB Tuning Stencils Kaushik Datta.

P A R A L L E L C O M P U T I N G L A B O R A T O R Y EECS Electrical Engineering and Computer Sciences B ERKELEY P AR L AB Tuning Stencils Kaushik Datta.
General Computer Science for Engineers CISC 106 Lecture 33 Dr. John Cavazos Computer and Information Sciences 05/11/2009.

General Computer Science for Engineers CISC 106 Lecture 33 Dr. John Cavazos Computer and Information Sciences 05/11/2009.
General Computer Science for Engineers CISC 106 Lecture 09 James Atlas Computer and Information Sciences 9/25/2009.

General Computer Science for Engineers CISC 106 Lecture 09 James Atlas Computer and Information Sciences 9/25/2009.
Презентація за розділом “Гумористичні твори”

Презентація за розділом “Гумористичні твори”

Similar presentations

Ads by Google