Download Static Translation of Stream Program to a Parallel System

Communication Overhead
Estimation on Multicores
S. M. Farhad
The University of Sydney
Joint work with
Yousun Ko
Bernd Burgstaller
Bernhard Scholz
Outline

Motivation




Multicore trend
Stream programming
Profiling communication overhead
Related works
2
Motivation
Stream
Programming
512
Unicore
256
PicoChip AMBRIC
Homogeneous Multicore
CISCO CSR1
Heterogeneous Multicore
128
NVIDIA G80
Courtesy: Scott’08
Larrabee
# cores/chip
64
X10
Peakstream
32
RAZA
XLR
C/C++/Java
16
RAW
Accelerator
Ct
8
4
BCM 1480
4004
8008
8080
Xbox 360
Power4 PA8800
8086
286
386
486
Pentium
P2
1
P3 P4
Athlon
1975
1980
1985
1990
1995
2000
Fortress
Cavium
Cell
Niagara
2
CUDA
Opteron 4P
AMD Fusion
CTM
Core2Quad
Xeon
Power6
Rstream
Opteron
Core2Duo
CoreDuo
Rapidmind
Core
Itanium Itanium2
2005
2010
3
Stream Programming Paradigm

Programs expressed as stream
graphs
Stream


Streams: Infinite sequence of data
elements
Actor
Actors: Functions applied to streams
Stream
4
Properties of Stream Program
AtoD
FMDemod


Regular and repeating
computation
Independent actors with explicit
communication

Producer / Consumer
dependencies
Splitter
LPF1
LPF2
LPF3
HPF1
HPF2
HPF3
Joiner
Adder
Speaker
5
StreamIt Language
filter



An implementation
of stream prog.
Hierarchical
structure
Each construct has
single input/output
stream
pipeline
may be
any StreamIt
language construct
splitjoin
splitter
parallel computation
joiner
feedback loop
joiner
splitter
6
How to Estimate the Communication
Overhead?
7
Problems to Measure Communication
Overhead

Reasons:




Multicores are non-communication exposed
architecture
Complex cache hierarchy
Cache coherence protocols
Consequence:


Cannot directly measure the communication cost
Estimate the communication cost by measuring
the execution time of actors
8
Measuring the Communication Overhead
of an Edge
C(i ,k )  ti  ti  t k  t k
Processor 1
i
Processor 1
k
ti
tk
No communication cost
Processor 2
i
k
ti
t k
With communication cost
9
How to Minimize the Required Number
of Experiments
Processor 1
A
1
Requires
2+1 Exps
Processor 2
A
1
1
B
2
C
Processor 2
A
B
2
Processor 1
Graph
Coloring
B
2
C
C
3
3
D
4
E
4
D
E
5
F
Pipeline
Odd edges
across partition
Even edges
across partition
10
Obs. 1: There is no loop of three actors in a
stream graph
Processor 1
Processor 2
l
i
k
11
Obs. 2: There is no interference of adjacent
nodes between edges
P-1
A
P-2
B
C
D
E
P-3
F
P-4
For blue color edges
12
Remove Interference



Convert to a line
graph
Add interference
edges
Use vertex coloring
algorithm
A
AB
BC
B
BD
C
D
E
CE
DE
EF
F
Stream graph
Line graph
13
Processor Leveling Graph
A
B
C
A
D
B, C, D, E
E
F
F
For blue colored edge
Processor leveling graph
14
Coloring the Processor Labelling Graph
Processor 1
A
A
B, C, D, E
B, C, D, E
F
F
Processor 2
A
B, C, D, E
F
15
Measuring the Communication Cost
Processor 1
Processor 2
A
t A
B
t B
A
B, C, D, E
C
D
E
t E
F
tF
F
C( A, B )  (t A  t A )  (t B  t B )
C( E , F )  (t E  t E )  (t F  t F )
For blue colored edge
16
Profiling Performance
Benchmark Total Edge Prof Steps Steps/Edge (%) Err (%)
SAR
44
3
7
10
MatrixMult
88
21
24
17
MergeSort
37
4
11
31
FMRadio
21
3
14
24
DCT
28
9
32
14
RadixSort
12
2
17
5
FFT
26
3
12
27
MPEG
56
17
30
15
Channel
22
6
27
11
BeamFormer
39
5
13
13
GM
17%
15%
17
Related Works
[1] Static Scheduling of SDF Programs for DSP [Lee ‘87]
[2] StreamIt: A language for streaming applications [Thies ‘02]
[3] Phased Scheduling of Stream Programs [Thies ’03]
[4] Exploiting Coarse Grained Task, Data, and Pipeline Parallelism in
Stream Programs [Thies ‘06]
[5] Orchestrating the Execution of Stream Programs on Cell [Scott ’08]
[6] Software Pipelined Execution of Stream Programs on GPUs
[Udupa‘09]
[7] Synergistic Execution of Stream Programs on Multicores with
Accelerators [Udupa ‘09]
[8] Orchestration by approximation [Farhad ‘11]
18
Questions?