Core-Assisted Bottleneck Acceleration in GPUs: A New Framework
The research presents Core-Assisted Bottleneck Acceleration (CABA), a novel framework for leveraging helper threads in GPUs to alleviate memory bandwidth bottlenecks. By utilizing idle resources effectively, CABA enables tasks such as compression, prefetching, and memoization, resulting in a significant 41.7% performance improvement. The use of helper threads in a throughput-oriented architecture addresses imbalances in resource utilization and optimizes GPU functionality.
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Presentation Transcript
A Case for Core-Assisted Bottleneck Acceleration in GPUs Enabling Flexible Data Compression with Assist Warps Nandita Vijaykumar Gennady Pekhimenko, Adwait Jog, Abhishek Bhowmick, Rachata Ausavarangnirun, Chita Das, Mahmut Kandemir, Todd C. Mowry, Onur Mutlu
Observation Imbalances in execution leave GPU resources underutilized Saturated Memory Bandwidth Cores Register File Idle! Memory Hierarchy Full! GPU Threads GPU Streaming Multiprocessor
Our Goal Employ idle resources to do something useful: accelerate the bottleneck using helper threads Cores Register File Helper Threads Memory Hierarchy GPU Threads GPU Streaming Multiprocessor
Challenge How do you manage and use helper threads in a throughput-oriented architecture?
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
