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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.