Power Point Slides in PDF - University of California, Santa Cruz

The Return of the SIMD
Computers:
UCSC Kestrel and Beyond
Andrea Di Blas
School of Engineering
University of California
Santa Cruz

Outline
Introduction: UCSC Kestrel
“Synchronous” applications
“Asynchronous” applications
A. Di Blas 2

INTRODUCTION
A. Di Blas 3

In a not-too-distant past…
A. Di Blas 4

A. Di Blas 5

A long time ago (late 1980’s)
the computing community
had high hope and
expectation in a new kind of
architecture, the “Single
Instruction-Multiple Instruction Multiple Data”
(SIMD) parallel computers.
A. Di Blas 6

However, almost all were
short-lived. short lived. Their high cost,
the ever-increasing ever increasing power of
the evil serial CPUs and,
above all, the effort required
to program such an
unfamiliar architecture,
forced big SIMD machines to
an early retirement.
A. Di Blas 7

By the mid-90’s, mid 90’s, SIMD
machines were already
disappearing from
Top500, the list of the
world’s largest
supercomputers.
A. Di Blas 8

But in late 1998, a small group
at UC Santa Cruz finally had
the first working prototype of a
new kind of high-performance,
high performance,
low-cost low cost SIMD co-processor.
co processor.
Originally designed for
computational biology, it
proved extremely powerful in a
variety of other applications.
A. Di Blas 9

A. Di Blas 10

A. Di Blas 11

Kestrel
A. Di Blas 12

Multiple Instruction-
Multiple Data
MIMD and SIMD
Single Instruction-
Multiple Data
A. Di Blas 13

Image Filters on Kestrel
2D Gaussian filter
Edge detector
A. Di Blas 14

2D Gaussian convolution
“Red Rocks Canyon”
A. Di Blas 15

2D Gaussian convolution
“Red Rocks Canyon”
A. Di Blas 16

2D Gaussian convolution
“Red Rocks Canyon”
A. Di Blas 17

2D Gaussian convolution
The 2D Gaussian kernel is separable
A. Di Blas 18

2D Gaussian convolution
512x512-pixel
512x512 pixel
Kernel size, time in s
Image (8bpp) 5x5 7x7 9x9 11x11
CPU time 0.050 0.070 0.070 0.080
Kestrel time 0.016 0.017 0.018 0.019
SPEEDUP 3.12 4.12 3.89 4.21
Kestrel runs at 20 MHz!
CPU: 1GHz Pentium-III 256 MB RAM cc –O2
A. Di Blas 19

Edge detector
“Big Sur”
A. Di Blas 20

Edge detector
“Big Sur”
A. Di Blas 21

Edge detector
A. Di Blas 22

Edge detector
512x512-pixel
512x512 pixel
Image (8bpp)
time [s]
CPU 0.040
Kestrel 0.018
SPEEDUP 2.22
CPU: 1GHz Pentium-III 256 MB RAM cc –O2
A. Di Blas 23

Asynchronous applications
Mandelbrot Set
2D Median filter
A. Di Blas 24

Mandelbrot set
A. Di Blas 25

Mandelbrot set (synchronous)
A. Di Blas 26

“SIMD SIMD Phase Programming Model”
Simple methodology to turn a sequential,
data-dependent data dependent algorithm into a SIMD-
parallel one
Can be used with “partitionable
“ partitionable” ” problems
Provides dynamic load balancing without
the need of a high-level high level support system
A. Di Blas 27

Mandelbrot set (SPPM)
A. Di Blas 28

Mandelbrot set (SPPM)
A. Di Blas 29

Mandelbrot set (SPPM)
A. Di Blas 30

Mandelbrot set (SPPM)
A. Di Blas 31

512x512-pixel
512x512 pixel
Mandelbrot set
Max # of iterations, time in s
Image (16bpp) 1000 5000 10000
CPU time 4.88 22.21 44.37
Kestrel time (synch) 3.65 17.18 34.79
Kestrel time (SPPM) 3.55 8.73 15.11
SPEEDUP (SPPM vs CPU) 1.37 2.54 2.94
SPEEDUP (SPPM vs synch) 1.03 1.97 2.30
CPU: 500 MHz UltraSPARC-II, 640MB RAM, cc –xO3
A. Di Blas 32

2D Median filter
“Office Hours”
A. Di Blas 33

2D Median filter
“Office Hours”
A. Di Blas 34

2D Median filter
“Office Hours”
A. Di Blas 35

2D Median filter
A. Di Blas 36

512x512-pixel
512x512 pixel
2D Median filter
Window size
Image (8bpp) 5x5 7x7 9x9 11x11
CPU time 0.190 0.370 0.540 0.760
Kestrel time 0.054 0.076 0.105 0.141
SPEEDUP 3.52 4.97 5.14 5.39
CPU: 1GHz Pentium-III 256 MB RAM cc –O2
A. Di Blas 37

The end
A. Di Blas 38