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An Execution Model for Heterogeneous Multicore Architectures. Gregory Diamos, Andrew Kerr, and Sudhakar Yalamanchili Computer Architecture and Systems Laboratory Center for Experimental Research in Computer Systems School of Electrical and Computer Engineering Georgia Institute of Technology.

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Slide1 l.jpg

An Execution Model for Heterogeneous Multicore Architectures

Gregory Diamos, Andrew Kerr, and Sudhakar Yalamanchili

Computer Architecture and Systems Laboratory

Center for Experimental Research in Computer Systems

School of Electrical and Computer Engineering

Georgia Institute of Technology


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Software Challenges of Heterogeneity

  • Programming Model

  • Execution Model

  • Portability

  • Performance


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System Space

Single GPU

Multicore CPU

Multi GPU

Multicore CPU

Multi-node

Level of Abstraction

Runtime Execution Model

(Harmony)

Runtime Translation of

Data-Parallel IR

(Ocelot)

System Size and Configuration


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Scalable Portable Execution – Harmony Runtime

Cap Model 3

readInputs();

computeInvariants();

for all chunks

{

simulateChunk();

}

generateResults();

Memory

Inputs

Outputs

Inputs

Outputs

kernel

chunk

chunk

Transparent scheduling, execution management of chunks

kernel

Harmony Run-time

CPU

CPU

CPU

ACC

ACC

ACC

FIFO

FIFO

FIFO

Local

Memory

Local

Memory

Local

Memory

Cache

Cache

Cache

DMA

DMA

DMA

Binary compatibility across system sizes

Network (e.g., Hypertransport, QPI, PCIe)

  • Minimize/avoid retuning and porting applications as you add accelerators

  • Advanced optimizations

    • Speculation, performance prediction, kernel fusion


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Emerging Environment

Datalog

CUDA/OpenCL

Language Front End

Language Front End

  • Status:

  • Summer 2009

  • With Prof. Nate Clark

Kernel IR

  • Status:

  • Single node/multi-GPU

Run Time (Harmony)

Ocelot

Emulator

LLVM I/F

  • Status:

  • Test and Debug

  • Status:

  • In progress (Fall 2009)

CUDAJIT

Prof. H. Kim

GPGPU Simulator

Supported ISAs (MIPS, SPARC, x86, etc.)


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Emerging HVM Platform Architecture

With K. Schwan and A. Gavrilovska


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Problem Scaling – Risk Analysis Application

Measured execution times

GPU interactive overhead dominates

With latest CPUs (2x faster) and GPUs(4x faster), GPU advantage should grow by 2x


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Other Applications


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GPU Compilation Flow

Abstract Syntax Tree

(Datalog Clauses)

Clauses to Execution Units

Execution Group

P

GPU

(EU)

GPU

(EU)

GPU

(EU)

P

Predicates to Data Structures

Execution Units to Algorithms (Kernels)

Data Structures

Compute Kernels

Runtime Mapping of Kernels to Cores

Runtime

GPU Core

CPU Core


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