The Locality Descriptor: A Holistic Cross-Layer Abstraction to Express Data Locality in GPUs

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Exploiting data locality in GPUs is critical to making more efficient use of the existing caches and the NUMA-based memory hierarchy expected in future GPUs. While modern GPU programming models are designed to explicitly express parallelism, there is no clear explicit way to express data locality—i.e., reusebased locality to make efficient use of the caches, or NUMA

locality to efficiently utilize a NUMA system. On the one hand, this lack of expressiveness makes it a very challenging task for the programmer to write code to get the best performance out of the memory hierarchy. On the other hand, hardware-only architectural techniques are often suboptimal as they miss key higher-level program semantics that are essential to effectively exploit data locality. In this work, we propose the Locality Descriptor, a crosslayer abstraction to explicitly express and exploit data locality in GPUs. The Locality Descriptor (i) provides the software a flexible and portable interface to optimize for data locality, requiring no knowledge of the underlying memory techniques and resources, and (ii) enables the architecture to leverage key program semantics and effectively coordinate a range of techniques (e.g., CTA scheduling, cache management, memory placement) to exploit locality in a programmer-transparent manner. We demonstrate that the Locality Descriptor improves performance by 26.6% on average (up to 46.6%) when exploiting reuse-based locality in the cache hierarchy, and by 53.7% (up to 2.8X) when exploiting NUMA locality in a NUMA memory system.


Nandita Vijayumar (Carnegie Mellon University)
Eiman Ebrahimi (NVIDIA)
Kevin Hsieh (Carnegie Mellon University)
Phillip B. Gibbons (Carnegie Mellon University)
Onur Mutlu (ETH Zurich and Carnegie Mellon University)

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