stream programming: luring programmers into the multicore era bill thies computer science and...
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Stream Programming: LuringProgrammers into the Multicore Era
Bill Thies
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Spring 2008
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Multicores are Here
1985 199019801970 1975 1995 2000
4004
8008
80868080 286 386 486 Pentium P2 P3P4Itanium
Itanium 2
2005
Raw
Power4Opteron
Power6
Niagara
YonahPExtreme
Tanglewood
Cell
IntelTflops
Xbox360
CaviumOcteon
RazaXLR
PA-8800
CiscoCSR-1
PicochipPC102
Broadcom 1480
20??
# ofcores
1
2
4
8
16
32
64
128
256
512
Opteron 4PXeon MP
Athlon
AmbricAM2045
Tilera
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Multicores are Here
1985 199019801970 1975 1995 2000
4004
8008
80868080 286 386 486 Pentium P2 P3P4Itanium
Itanium 2
2005
Raw
Power4Opteron
Power6
Niagara
YonahPExtreme
Tanglewood
Cell
IntelTflops
Xbox360
CaviumOcteon
RazaXLR
PA-8800
CiscoCSR-1
PicochipPC102
Broadcom 1480
20??
# ofcores
1
2
4
8
16
32
64
128
256
512
Opteron 4PXeon MP
Athlon
AmbricAM2045
Tilera
Hardware wasresponsible forimproving performance
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Multicores are Here
1985 199019801970 1975 1995 2000
4004
8008
80868080 286 386 486 Pentium P2 P3P4Itanium
Itanium 2
2005
Raw
Power4Opteron
Power6
Niagara
YonahPExtreme
Tanglewood
Cell
IntelTflops
Xbox360
CaviumOcteon
RazaXLR
PA-8800
CiscoCSR-1
PicochipPC102
Broadcom 1480
20??
# ofcores
1
2
4
8
16
32
64
128
256
512
Opteron 4PXeon MP
Athlon
AmbricAM2045
Tilera
Now, performanceburden falls onprogrammers
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Is Parallel Programming a New Problem?
• No! Decades of research targeting multiprocessors– Languages, compilers, architectures, tools…
• What is different today?1. Multicores vs. multiprocessors. Multicores have:
- New interconnects with non-uniform communication costs
- Faster on-chip communication than off-chip I/O, memory ops
- Limited per-core memory availability
2. Non-expert programmers- Supercomputers with >2048 processors today: 100 [top500.org]
- Machines with >2048 cores in 2020: >100 million [ITU, Moore]
3. Application trends- Embedded: 2.7 billion cell phones vs 850 million PCs [ITU 2006]
- Data-centric: YouTube streams 200 TB of video daily
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Streaming Application Domain
• For programs based on streams of data– Audio, video, DSP, networking, and
cryptographic processing kernels – Examples: HDTV editing, radar
tracking, microphone arrays, cell phone base stations, graphics
Adder
Speaker
AtoD
FMDemod
LPF1
Duplicate
RoundRobin
LPF2 LPF3
HPF1 HPF2 HPF3
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Streaming Application Domain
• For programs based on streams of data– Audio, video, DSP, networking, and
cryptographic processing kernels – Examples: HDTV editing, radar
tracking, microphone arrays, cell phone base stations, graphics
• Properties of stream programs– Regular and repeating computation– Independent filters
with explicit communication– Data items have short lifetimes
Adder
Speaker
AtoD
FMDemod
LPF1
Duplicate
RoundRobin
LPF2 LPF3
HPF1 HPF2 HPF3
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Brief History of Streaming1960 1970 1980 1990 2000
Models of Computation
Languages / Compilers
Modeling Environments
Petri NetsComp. Graphs
Kahn Proc. NetworksCommunicating Sequential Processes
SisalOccam
Lucid IdVALlazy
Synchronous Dataflow
Gabriel
LUSTRE
Ptolemy
Esterel
C
Grape-IIMatlab/Simulink
etc.
ErlangpH
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Weaknesses• Unsuitable for static analysis• Cannot leverage deep results from DSP / modeling community
Strengths• Elegance• Generality
Brief History of Streaming1960 1970 1980 1990 2000
Models of Computation
Languages / Compilers
Modeling Environments
Petri NetsComp. Graphs
Kahn Proc. NetworksCommunicating Sequential Processes
SisalOccam
Lucid IdVALlazy
Synchronous Dataflow
Gabriel
LUSTRE
Ptolemy
Esterel
C
Grape-IIMatlab/Simulink
etc.
ErlangpH
![Page 10: Stream Programming: Luring Programmers into the Multicore Era Bill Thies Computer Science and Artificial Intelligence Laboratory Massachusetts Institute](https://reader035.vdocument.in/reader035/viewer/2022062322/56649f255503460f94c3bd06/html5/thumbnails/10.jpg)
Weaknesses• Unsuitable for static analysis• Cannot leverage deep results from DSP / modeling community
Strengths• Elegance• Generality
Brief History of Streaming1960 1970 1980 1990 2000
Models of Computation
Languages / Compilers
Modeling Environments
Petri NetsComp. Graphs
Kahn Proc. NetworksCommunicating Sequential Processes
SisalOccam
Lucid IdVALlazy
Synchronous Dataflow
Gabriel
LUSTRE
Ptolemy
Esterel
C
Grape-IIMatlab/Simulink
etc.
ErlangpH
StreamItCg StreamC
Brook
“StreamProgramming”
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StreamIt: A Language and Compilerfor Stream Programs
• Key idea: design language that enables static analysis
• Goals:1. Expose and exploit the parallelism in stream programs
2. Improve programmer productivity in the streaming domain
• Project contributions:– Language design for streaming [CC'02, CAN'02, PPoPP'05, IJPP'05]
– Automatic parallelization [ASPLOS'02, G.Hardware'05, ASPLOS'06]
– Domain-specific optimizations [PLDI'03, CASES'05, TechRep'07]
– Cache-aware scheduling [LCTES'03, LCTES'05]
– Extracting streams from legacy code [MICRO'07]
– User + application studies [PLDI'05, P-PHEC'05, IPDPS'06]
– 7 years, 25 people, 300 KLOC
– 700 external downloads, 5 external publications
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StreamIt: A Language and Compilerfor Stream Programs
• Key idea: design language that enables static analysis
• Goals:1. Expose and exploit the parallelism in stream programs
2. Improve programmer productivity in the streaming domain
• I contributed to:– Language design for streaming [CC'02, CAN'02, PPoPP'05, IJPP'05]
– Automatic parallelization [ASPLOS'02, G.Hardware'05, ASPLOS'06]
– Domain-specific optimizations [PLDI'03, CASES'05, TechRep'07]
– Cache-aware scheduling [LCTES'03, LCTES'05]
– Extracting streams from legacy code [MICRO'07]
– User + application studies [PLDI'05, P-PHEC'05, IPDPS'06]
– 7 years, 25 people, 300 KLOC
– 700 external downloads, 5 external publications
![Page 13: Stream Programming: Luring Programmers into the Multicore Era Bill Thies Computer Science and Artificial Intelligence Laboratory Massachusetts Institute](https://reader035.vdocument.in/reader035/viewer/2022062322/56649f255503460f94c3bd06/html5/thumbnails/13.jpg)
StreamIt: A Language and Compilerfor Stream Programs
• Key idea: design language that enables static analysis
• Goals:1. Expose and exploit the parallelism in stream programs
2. Improve programmer productivity in the streaming domain
• This talk:– Language design for streaming [CC'02, CAN'02, PPoPP'05, IJPP'05]
– Automatic parallelization [ASPLOS'02, G.Hardware'05, ASPLOS'06]
– Domain-specific optimizations [PLDI'03, CASES'05, TechRep'07]
– Cache-aware scheduling [LCTES'03, LCTES'05]
– Extracting streams from legacy code [MICRO'07]
– User + application studies [PLDI'05, P-PHEC'05, IPDPS'06]
– 7 years, 25 people, 300 KLOC
– 700 external downloads, 5 external publications
![Page 14: Stream Programming: Luring Programmers into the Multicore Era Bill Thies Computer Science and Artificial Intelligence Laboratory Massachusetts Institute](https://reader035.vdocument.in/reader035/viewer/2022062322/56649f255503460f94c3bd06/html5/thumbnails/14.jpg)
Part 1: Language Design
Joint work with Michael GordonWilliam Thies, Michal Karczmarek, Saman Amarasinghe (CC’02)
William Thies, Michal Karczmarek, Janis Sermulins, Rodric Rabbah,Saman Amarasinghe (PPoPP’05)
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StreamIt Language Basics
• High-level, architecture-independent language– Backend support for uniprocessors, multicores (Raw, SMP),
cluster of workstations
• Model of computation: synchronous dataflow– Program is a graph of independent filters– Filters have an atomic execution step
with known input / output rates– Compiler is responsible for
scheduling and buffer management
• Extensions to synchronous dataflow – Dynamic I/O rates– Support for sliding window operations– Teleport messaging [PPoPP’05]
Decimate
Input
Output
1
10
1
1
x 10
x 1
x 1
[Lee & Messerschmidt, 1987]
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Representing Streams
• Conventional wisdom: stream programs are graphs– Graphs have no simple textual representation– Graphs are difficult to analyze and optimize
• Insight: stream programs have structure
structuredunstructured
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Structured Streams
may be any StreamIt language construct
joinersplitter
pipeline
feedback loop
joiner splitter
splitjoin
filter • Each structure is single-input, single-output
• Hierarchical and composable
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Radar-Array Front End
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Filterbank
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FFT
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Block Matrix Multiply
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MP3 Decoder
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Bitonic Sort
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FM Radio with Equalizer
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Ground Moving Target Indicator (GMTI)
99 filters
3566 filter instances
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26
void->void pipeline FMRadio(int N, float lo, float hi) {add AtoD();
add FMDemod();
add splitjoin {
split duplicate;
for (int i=0; i<N; i++) {add pipeline {
add LowPassFilter(lo + i*(hi - lo)/N);
add HighPassFilter(lo + i*(hi - lo)/N);}
}join roundrobin();
}add Adder();
add Speaker();}
Adder
Speaker
AtoD
FMDemod
LPF1
Duplicate
RoundRobin
LPF2 LPF3
HPF1 HPF2 HPF3
Example Syntax: FMRadio
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• Software radio
• Frequency hopping radio
• Acoustic beam former
• Vocoder
• FFTs and DCTs
• JPEG Encoder/Decoder
• MPEG-2 Encoder/Decoder
• MPEG-4 (fragments)
• Sorting algorithms
• GMTI (Ground Moving Target Indicator)
• DES and Serpent crypto algorithms
• SSCA#3 (HPCS scalable benchmark for synthetic aperture radar)
• Mosaic imaging using RANSAC algorithm
StreamIt Application Suite
Total size: 60,000 lines of code
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Control Messages
• Occasionally, low-bandwidth control messages are sent between actors
• Often demands precise timing
– Communications: adjust protocol,amplification, compression
– Network router: cancel invalid packet
– Adaptive beamformer: track a target
– Respond to user input, runtime errors
– Frequency hopping radio
• Traditional techniques:
– Direct method call (no timing guarantees)
– Embed message in stream (opaque, slow)
AtoD
duplicate
LPF2LPF1 LPF3
HPF2HPF1 HPF3
Transmit
roundrobin
Encode
Decode
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• Looks like method call, but timed relative to data in the stream
– Exposes dependences to compiler– Simple and precise for user
- Adjustable latency
- Can send upstream or downstream
void setProtocol(int p) { reconfig(p);}
TargetFilter x;if newProtocol(p) { x.setProtocol(p) @ 2;}
Idea 2: Teleport Messaging
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Part 2: Automatic Parallelization
Joint work with Michael GordonMichael I. Gordon, William Thies, Saman Amarasinghe (ASPLOS’06)
Michael I. Gordon, William Thies, Michal Karczmarek, Jasper Lin, Ali S. Meli, Andrew A. Lamb, Chris Leger, Jeremy Wong, Henry Hoffmann, David Maze, Saman
Amarasinghe (ASPLOS’02)
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Streaming is an Implicitly Parallel Model
• Programmer thinks about functionality, not parallelism
• More explicit models may…– Require knowledge of target [MPI] [cG]
– Require parallelism annotations [OpenMP] [HPF] [Cilk] [Intel TBB]
• Novelty over other implicit models?[Erlang] [MapReduce] [Sequoia] [pH] [Occam] [Sisal] [Id] [VAL] [LUSTRE][HAL] [THAL] [SALSA] [Rosette] [ABCL] [APL] [ZPL] [NESL] […]
Exploiting streaming structure for robust performance
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Parallelism in Stream Programs
Task parallelism– Analogous to thread (fork/join)
parallelism
Data Parallelism
– Peel iterations of filter, place within scatter/gather pair (fission)
– parallelize filters with state
Pipeline Parallelism
– Between producers and consumers– Stateful filters can be parallelized
Splitter
Joiner
Task
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Parallelism in Stream Programs
Task parallelism– Analogous to thread (fork/join)
parallelism
Data parallelism– Analogous to DOALL loops
Pipeline parallelism– Analogous to ILP that is
exploited in hardware
Splitter
Joiner
Splitter
Joiner
Task
Pip
elin
e
Data
Stateless
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Baseline: Fine-Grained Data Parallelism
Adder
Splitter
Joiner
BandStopBandStopBandStopAdder
Splitter
Joiner
ExpandExpandExpand
ProcessProcessProcess
Joiner
BandPassBandPassBandPass
CompressCompressCompress
BandStopBandStopBandStop
Expand
BandStop
Splitter
Joiner
Splitter
Process
BandPass
Compress
Splitter
Joiner
Splitter
Joiner
Splitter
Joiner
ExpandExpandExpand
ProcessProcessProcess
Joiner
BandPassBandPassBandPass
CompressCompressCompress
BandStopBandStopBandStop
Expand
BandStop
Splitter
Joiner
Splitter
Process
BandPass
Compress
Splitter
Joiner
Splitter
Joiner
Splitter
Joiner
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0
2
4
6
8
10
12
14
16
18
Bitoni
cSor
t
Chann
elVoc
oder
DCTDES
FFT
Filterb
ank
FMRad
io
Serpe
ntTDE
MPEG2-
subs
et
Vocod
er
Radar
Geom
etric
Mea
nTh
rou
gh
pu
t N
orm
aliz
ed t
o S
ing
le C
ore
Str
eam
It
Fine-Grained Data
Coarse-Grained Task + Data
Evaluation:Fine-Grained Data Parallelism
Raw Microprocessor 16 inorder, single-issue cores with D$ and I$
16 memory banks, each bank with DMACycle accurate simulator
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0
2
4
6
8
10
12
14
16
18
Bitoni
cSor
t
Chann
elVoc
oder
DCTDES
FFT
Filterb
ank
FMRad
io
Serpe
ntTDE
MPEG2-
subs
et
Vocod
er
Radar
Geom
etric
Mea
nTh
rou
gh
pu
t N
orm
aliz
ed t
o S
ing
le C
ore
Str
eam
It
Fine-Grained Data
Coarse-Grained Task + Data
Evaluation:Fine-Grained Data Parallelism
Good Parallelism! Too Much Synchronization!
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Splitter
Joiner
Expand
BandStop
Process
BandPass
Compress
Expand
BandStop
Process
BandPass
Compress
Adder
Coarsening the Granularity
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Splitter
Joiner
BandPassCompressProcessExpand
BandPassCompressProcessExpand
BandStop BandStop
Adder
Coarsening the Granularity
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Splitter
Joiner
BandPassCompressProcessExpand
Splitter
Joiner
BandPassCompressProcessExpand
BandPassCompressProcessExpand
Splitter
Joiner
BandPassCompressProcessExpand
BandStop BandStop
Coarsening the Granularity
Adder
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BandStop BandStop
Splitter
Joiner
BandPassCompressProcessExpand
Splitter
Joiner
BandPassCompressProcessExpand
BandPassCompressProcessExpand
Splitter
Joiner
BandPassCompressProcessExpand
Splitter
Joiner
BandStop
Splitter
Joiner
BandStop
Coarsening the Granularity
AdderAdderAdderAdderAdder
Splitter
Joiner
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0
2
4
6
8
10
12
14
16
18
Bitoni
cSor
t
Chann
elVoc
oder
DCTDES
FFT
Filterb
ank
FMRad
io
Serpe
ntTDE
MPEG2-
subs
et
Vocod
er
Radar
Geom
etric
Mea
nTh
rou
gh
pu
t N
orm
aliz
ed t
o S
ing
le C
ore
Str
eam
It
Fine-Grained Data
Coarse-Grained Task + Data
Evaluation: Coarse-Grained Data Parallelism
Good Parallelism! Low Synchronization!
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Simplified Vocoder
RectPolar
Splitter
Joiner
AdaptDFT AdaptDFT
Splitter
Splitter
Amplify
Diff
UnWrap
Accum
Amplify
Diff
Unwrap
Accum
Joiner
Joiner
PolarRect
66
20
2
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1
1
2
1
1
1
20 Data Parallel
Data Parallel
Target a 4-core machine
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Data Parallelize
RectPolarRectPolarRectPolar
Splitter
Joiner
AdaptDFT AdaptDFT
Splitter
Splitter
Amplify
Diff
UnWrap
Accum
Amplify
Diff
Unwrap
Accum
Joiner
RectPolar
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Joiner
RectPolarRectPolarRectPolarPolarRect
Splitter
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Joiner
66
20
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20
5
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Target a 4-core machine
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Data + Task Parallel Execution
Time
Cores
21
Splitter
Joiner
Splitter
Splitter
Joiner
Splitter
Joiner
RectPolarSplitter
Joiner
Joiner
66
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Target a 4-core machine
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We Can Do Better
Time
Cores
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16
Target a 4-core machine
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RectPolar
RectPolar
RectPolar
RectPolar
Prologue
New Steady
State
Coarse-Grained Software Pipelining
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Bitoni
cSor
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Chann
elVoc
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DCTDES
FFT
Filterb
ank
FMRad
io
Serpe
ntTDE
MPEG2-
subs
et
Vocod
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Radar
Geom
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Mea
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eam
It
Fine-Grained DataCoarse-Grained Task + DataCoarse-Grained Task + Data + Software Pipeline
Evaluation: Coarse-Grained Task + Data + Software Pipelining
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subs
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Radar
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Mea
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eam
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Fine-Grained DataCoarse-Grained Task + DataCoarse-Grained Task + Data + Software Pipeline
Evaluation: Coarse-Grained Task + Data + Software Pipelining
Best Parallelism! Lowest Synchronization!
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Parallelism: Take Away
• Stream programs have abundant parallelism– However, parallelism is obfuscated in language like C
• Stream languages enable new & effective mapping
– In C, analogous transformations impossibly complex – In StreamC or Brook, similar transformations possible
[Khailany et al., IEEE Micro’01] [Buck et al., SIGGRAPH’04] [Das et al., PACT’06] […]
• Results should extend to other multicores– Parameters: local memory, comm.-to-comp. cost– Preliminary results on Cell are promising [Zhang, dasCMP’07]
Coarsen Granularity
Data Parallelize
Software Pipeline
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Part 3: Domain-Specific Optimizations
Joint work with Andrew Lamb, Sitij AgrawalAndrew Lamb, William Thies, Saman Amarasinghe (PLDI’03)
Sitij Agrawal, William Thies, Saman Amarasinghe (CASES’05)
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DSP Optimization Process
• Given specification of algorithm,minimize the computation cost
Adder
Speaker
AtoD
FMDemod
LPF1
Duplicate
RoundRobin
LPF2 LPF3
HPF1 HPF2 HPF3
Linear
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Equalizer
DSP Optimization Process
• Given specification of algorithm,minimize the computation cost
AtoD
Adder
LPF1
Duplicate
RoundRobin
LPF2 LPF3
HPF1 HPF2 HPF3
LinearFMDemod
Speaker
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Speaker
Equalizer
DSP Optimization Process
• Given specification of algorithm,minimize the computation cost
AtoD
FMDemod
FFT
IFFT
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DSP Optimization Process
• Given specification of algorithm,minimize the computation cost– Currently done by hand (MATLAB)
• Can compiler replace DSP expert?– Library generators limited [Spiral] [FFTW] [ATLAS]
– Enable unified development environment
Speaker
Equalizer
AtoD
FMDemod
IFFT
FFT
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Focus: Linear State Space Filters
• Properties:– Outputs are linear function of inputs and states– New states are linear function of inputs and states
• Most common target of DSP optimizations– FIR / IIR filters– Linear difference equations– Upsamplers / downsamplers– DCTs
u
x’ = Ax + Bu
y = Cx + Du
inputs
states
outputs
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Focus: Linear State Space Filters
u
x’ = Ax + Bu
y = Cx + Du
inputs
states
outputs
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Focus: Linear Filters
float->float filter Scale { work push 2 pop 1 { float u = pop(); push(u); push(2*u); }}
u
y = Du
inputs
outputs
Linear dataflow analysis
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Focus: Linear Filters
float->float filter Scale { work push 2 pop 1 { float u = pop(); push(u); push(2*u); }}
uinputs
outputs
Linear dataflow analysis
=y1y2
12
u
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Combining Adjacent Filters
y = Du
z = EyG
z = EDu
Filter 1
Filter 2
y
u
z
CombinedFilter
u
z
z = Gu
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Combination Example
Filter 1
Filter 2
y
u
z
CombinedFilter
u
z 654A E
3
2
1
B D
C = [ 32 ]G
1 mults
output6 mults
output
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• If matrix dimensions mis-match?
The General Case
[D]U
E
[D]U
E
[D][D]
[D]
Original Expanded
pop =
Matrix expansion:
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• If matrix dimensions mis-match?
The General Case
[D]U
E
[D]U
E
[D][D]
[D]
Original Expanded
pop =
Matrix expansion:
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Pipelines
Feedback Loops
The General Case
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Splitjoins
The General Case
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60%
80%
100%
Benchmark
Flo
ps
Rem
ove
d (
%)
linear
0.3%
Floating-Point Operations Reduction
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80%
100%
Benchmark
Flo
ps
Rem
ove
d (
%)
linear
freq
-140%
0.3%
Floating-Point Operations Reduction
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Splitter
Sink
RR
Mag
Detect
Duplicate
Mag
Detect
Mag
Detect
BeamForm
BeamForm
BeamForm
BeamForm
Filter Filter Filter Filter
Mag
Detect
RR
Splitter(null)
Input Input Input Input Input Input Input Input Input Input Input Input
Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec
Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec
FIR1 FIR1 FIR1 FIR1 FIR1 FIR FIR1 FIR1 FIR1 FIR1 FIR1 FIR1
FIR2 FIR2 FIR2 FIR2 FIR2 FIR FIR2 FIR2 FIR2 FIR2 FIR2 FIR2
Radar (Transformation Selection)
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RR
RR
RR
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Detect
Filter
Mag
Detect
Filter
Mag
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Duplicate
BeamForm
BeamForm
BeamForm
BeamForm
Filter
Mag
Detect
Splitter(null)
Input Input Input Input Input Input Input Input Input Input Input Input
Radar (Transformation Selection)
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RR
RR
Splitter
Sink
RR
Filter
Mag
Detect
Filter
Mag
Detect
Filter
Mag
Detect
RR
Duplicate
BeamForm
BeamForm
BeamForm
BeamForm
Filter
Mag
Detect
Splitter(null)
Input Input Input Input Input Input Input Input Input Input Input Input
Radar (Transformation Selection)
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2.4 times as many FLOPS
half as many FLOPS
Radar (Transformation Selection)
RR
RR
Splitter
Sink
RR
Filter
Mag
Detect
Filter
Mag
Detect
Filter
Mag
Detect
Filter
Mag
Detect
Splitter(null)
Input Input Input Input Input Input Input Input Input Input Input Input
Splitter
Sink
RR
Mag
Duplicate
Mag Mag Mag
RR
Splitter(null)
Input Input Input Input Input Input Input Input Input Input Input Input
Maximal Combination andShifting to Frequency Domain
Using TransformationSelection
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-40%
-20%
0%
20%
40%
60%
80%
100%
FIR
RateCon
vert
Targe
tDet
ect
FMRad
io
Radar
FilterB
ank
Vocode
r
Overs
ample
DToA
Benchmark
Flo
ps
Rem
ove
d (
%)
linear
freq
autosel
-140%
0.3%
Floating Point Operations Reduction
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-200%
-100%
0%
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200%
300%
400%
500%
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700%
800%
900%
FIR
RateCon
vert
Targe
tDet
ect
FMRad
io
Radar
FilterB
ank
Vocode
r
Overs
ample
DToA
Benchmark
Sp
eed
up
(%
)
linear
freq
autosel
Execution Speedup
On a Pentium IV
5%
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-200%
-100%
0%
100%
200%
300%
400%
500%
600%
700%
800%
900%
FIR
RateCon
vert
Targe
tDet
ect
FMRad
io
Radar
FilterB
ank
Vocode
r
Overs
ample
DToA
Benchmark
Sp
eed
up
(%
)
linear
freq
autosel
Execution Speedup
On a Pentium IV
5%
Additional transformations:1. Eliminating redundant states2. Eliminating parameters (non-zero, non-unary coefficients)3. Translation to the compressed domain
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StreamIt: Lessons Learned
• In practice, I/O rates of filters are often matched [LCTES’03]
– Over 30 publications study an uncommon case (CD-DAT)
• Multi-phase filters complicate programs, compilers– Should maintain simplicity of only one atomic step per filter
• Programmers accidentally introduce mutable filter state
1 2 3 2 7 8 7 5
x 147 x 98 x 28 x 32
void>int filter SquareWave() { int x = 0;
work push 1 { push(x); x = 1 - x;} }
void>int filter SquareWave() {
work push 2 { push(0); push(1); }} stateful stateless
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Future of StreamIt
• Goal: influence the next big language
Source: B. Stroustrup, The Design and Evolution of C++
1960
1970
1980
1990
Structural influenceFeature influenceFortran
Algol 60CPL
BCPL
C
ANSI C
Simula 67
C with Classes
C++
C++arm
C++std
ML CluAlgol 68
Ada
Origins of C++
Academic origin
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Research Trajectory
• Vision: Make emerging computational substrates universally accessible and useful
1. Languages, compilers, & tools for multicores – I believe new language / compiler technology
can enable scalable and robust performance
– Next inroads: expose & exploit flexibility in programs
2. Programmable microfluidics – We have developed programming languages,
tools, and flexible new devices for microfluidics
– Potential to revolutionize biology experimentation
3. Technologies for the developing world – TEK: enable Internet experience over email account
– Audio Wiki: publish content from a low-cost phone
– uBox / uPhone: monitor & improve rural healthcare
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Conclusions
• A parallel programming model will succeed only by luring programmers, making them do less, not more
• Stream programminglures programmers with:– Elegant programming primitives– Domain-specific optimizations
• Meanwhile, streamingis implicitly parallel– Robust performance via task,
data, & pipeline parallelism
• We believe stream programming will play a key rolein enabling a transition to multicore processors
Contributions– Structured streams
– Teleport messaging
– Unified algorithm for task,data, pipeline parallelism
– Software pipelining of whole procedures
– Algebraic simplification ofwhole procedures
– Translation from time to frequency
– Selection of best DSP transforms
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Acknowledgments• Project supervisors
– Prof. Saman Amarasinghe – Dr. Rodric Rabbah
• Contributors to this talk– Michael I. Gordon (Ph.D. Candidate) – leads StreamIt backend efforts– Andrew A. Lamb (M.Eng) – led linear optimizations– Sitij Agrawal (M.Eng) – led statespace optimizations
• Compiler developers– Kunal Agrawal– Allyn Dimock– Qiuyuan Jimmy Li
• Application developers– Basier Aziz– Matthew Brown– Matthew Drake
• User interface developers– Kimberly Kuo
– Jasper Lin– Michal Karczmarek– David Maze
– Shirley Fung– Hank Hoffmann– Chris Leger
– Janis Sermulins– Phil Sung– David Zhang
– Ali Meli– Satish Ramaswamy– Jeremy Wong
– Juan Reyes