Stanford Streaming Supercomputer (SSS) Project Meeting

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Stanford Streaming Supercomputer (SSS)Project
Meeting

• Bill Dally, Pat Hanrahan, and Ron FedkiwComputer
  Systems LaboratoryStanford University
• October 2, 2001

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Agenda

• Introductions (now)
• Vision subset of ASCI review slides
• Goals for the quarter
• Schedule of meetings for the quarter

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Computation is inexpensive and plentiful
nVidea GeForce3 80 Gflops/sec 800 Gops/sec
Velio VC3003 1Tb/s I/O BW
DRAM lt 0.20/MB
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But supercomputers are very expensive

• Cost more per GFLOPS, GUPS, and GByte than low
  end machines
• Hard to achieve high fraction of peak performance
  on global problems
• Based on clusters of CPUs that are scaling at
  only 20/year vs. 50 historically

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Microprocessors no longer realize the potential
of VLSI
52/year
19/year
301
74/year
1,0001
30,0001
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Streaming processors leverage emerging technology

• Streaming supercomputer can achieve
• 20/GFLOPs, 2/M-GUPS
• Scalable to PFLOPS and 1013 GUPS
• Enabled by
• Stream architecture
• Exposes and exploits parallelism and locality
• High arithmetic intensity (ops/BW)
• Hides latency
• Efficient interconnection networks
• High global bandwidth
• Low latency

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What is stream processing?
Operations within a kernel operate on local data
Kernels can be partitioned across chips to
exploit control parallelism
Image 0
convolve
convolve
Depth Map
SAD
Image 1
convolve
convolve
Streams expose data parallelism
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Why does it get good performance easily?
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Architecture of a Streaming Supercomputer
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Streaming processor
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A layered software system simplifies stream
programming
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Domain-specific languageexample Marble shader
in RTSL
float turbulence4_imagine_scalar (texref noise,
float4 pos) fragment float4 addr1 pos
fragment float4 addr2 pos 2, 2, 2, 1
fragment float4 addr3 pos 4, 4, 4, 1
fragment float4 addr4 pos 8, 8, 8, 1
fragment float val val (0.5)
texture(noise, addr1)0 val val
(0.25) texture(noise, addr2)0 val val
(0.125) texture(noise, addr3)0 val
val (0.0625) texture(noise, addr4)0
return val
float3 marble_color(float x) float x2 x
sqrt(x1.0).7071 x2 sqrt(x) return .30
.6x2, .30 .8x, .60
.4x2
surface shader float4 shiny_marble_imagine
(texref noise) float4 Cd lightmodel_diffuse(
0.4, 0.4, 0.4, 1 , 0.5, 0.5, 0.5, 1 )
float4 Cs lightmodel_specular( 0.35, 0.35,
0.35, 1 , Zero, 20) fragment float y
fragment float4 pos Pobj 10, 10, 10, 1 y
pos1 3.0 turbulence4_imagine_scalar(noise,
pos) y sin(ypi) return
(marble_color(y), 1.0f Cd Cs)
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Stream-level application descriptionexample
SHARP Raytracer
Camera
Grid
Triangles
Rays
Rays
Hits
VoxID
Rays
Rays
Pixels

• Computation expressed as streams of records
  passing through kernels
• Similar to computation required for Monte-Carlo
  radiation transport

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Expected application performance

• Arithmetic-limited applications
• Includes applications where domain decomposition
  can be applied
• Like TFLO and LES
• Expected to achieve a large fraction of peak
  performance
• Communication-limited applications
• Such as applications requiring matrix solution Ax
  b
• At the very least will benefit from high global
  bandwidth
• We hope to find new methods to solve matrix
  equations using streaming

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Conclusion

• Computation is cheap yet supercomputing is
  expensive
• Streams enable supercomputing to exploit
  advantages of emerging technology
• by exposing locality and concurrency
• Order of magnitude cost/performance improvement
  for both arithmetic-limited and
  communication-limited codes
• 20/GFLOPS and 2/M-GUPS
• Scalable from desktop (1 TFLOPS) to machine room
  (1 PFLOPS)
• A layered software system using domain-specific
  languages simplifies stream programming
• MCRT, ODEs, PDEs
• Early results on graphics and image processing
  are encouraging

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Plan for AY2001-2002
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Project Goals for Fall Quarter AY2001-2002

• Map two applications to the stream model
• Fluid flow (TFLO), and molecular dynamics
  candidates
• Define a high-level stream programming language
• Generalize stream access without destroying
  locality
• Draft strawman SSS architecture and identify key
  issues

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Meeting Schedule Fall Quarter AY2001-2002

• Goal shared knowledge base and vision across the
  project
• 10/9 TFLO (Juan)
• 10/16 RTSL (Bill M.)
• 10/23 Molecular Dynamics (Eric)
• 10/30 Imagine and its programming system
  (Ujval)
• 11/6 C, ZPL, etc SPL brainstorming (Ian)
• 11/13 Metacompilation (Ben C.)
• 11/20 Application followup (Ron/Heinz)
• 11/27 Strawman architecture (Ben S.)
• 12/4 Streams vs. CMP (Blue Gene/Light, etc)
  (Bill D.)