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Core-Assisted Bottleneck Acceleration in GPUs: Enabling Flexible Data Compression with Assist Warps

Explore the use of helper threads in GPUs to accelerate bottleneck tasks, including data compression, prefetching, and memoization. Introducing CABA (Core-Assisted Bottleneck Acceleration) framework for flexible data compression, achieving a performance improvement of 41.7%.

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Core-Assisted Bottleneck Acceleration in GPUs: Enabling Flexible Data Compression with Assist Warps

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  1. A Case for Core-Assisted Bottleneck Acceleration in GPUsEnabling Flexible Data Compression with Assist Warps Nandita Vijaykumar Gennady Pekhimenko, Adwait Jog, Abhishek Bhowmick, RachataAusavarangnirun, Chita Das, MahmutKandemir, Todd C. Mowry, Onur Mutlu

  2. Observation • Imbalances in execution leave GPU resources underutilized Memory Hierarchy Saturated Memory Bandwidth Cores Register File Idle! Full! Idle! GPU Threads GPU Streaming Multiprocessor

  3. Our Goal • Employ idle resources to do something useful: accelerate the bottleneck − using helper threads Memory Hierarchy Cores Register File Helper Threads Running GPU Threads GPU Streaming Multiprocessor

  4. Challenge How do you manage and use helper threads in a throughput-orientedarchitecture?

  5. Our Solution: CABA • A new framework to enable helper threading in GPUs • CABA (Core-Assisted Bottleneck Acceleration) • Wide set of use cases • Compression, prefetching, memoization, … • Flexible data compression using CABA alleviates the memory bandwidth bottleneck • 41.7% performance improvement

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