SCHEDULE: NOV 12-18, 2011

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CudaDMA: Optimizing GPU Memory Bandwidth via Warp Specialization

SESSION: GPU Optimizations

EVENT TYPE: Paper

TIME: 10:30AM - 11:00AM

AUTHOR(S):Michael Bauer, Henry Cook, Brucek Khailany

ROOM:TCC 303

ABSTRACT:
As the computational power of GPUs continues to scale with Moore's Law, an increasing number of applications are becoming limited by memory bandwidth. We propose an approach for programming GPUs with tightly-coupled specialized DMA warps for performing memory transfers between on-chip and off-chip memories. Separate DMA warps improve memory bandwidth utilization by better exploiting available memory-level parallelism and by leveraging efficient inter-warp producer-consumer synchronization mechanisms. DMA warps also improve programmer productivity by decoupling the need for thread array shapes to match data layout. To illustrate the benefits of this approach, we present an extensible API, CudaDMA, that encapsulates synchronization and common sequential and strided data transfer patterns. Using CudaDMA, we demonstrate speedup of up to 1.37x on representative synthetic micro-benchmarks, and 1.15x-3.2x on several kernels from scientific applications written in CUDA running on NVIDIA Fermi GPUs.

Chair/Author Details:

Michael Bauer - Stanford University

Henry Cook - University of California, Berkeley

Brucek Khailany - NVIDIA

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The full paper can be found in the ACM Digital Library