SC22 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Technical Papers Archive

TD-NUCA: Runtime Driven Management of NUCA Caches in Task Dataflow Programming Models

Authors: Paul Caheny (Intel Corporation) and Lluc Alvarez, Marc Casas, and Miquel Moreto (Barcelona Supercomputing Center (BSC); Polytechnic University of Catalonia, Spain)

Abstract: In high performance processors, the design of on-chip memory hierarchies is crucial for performance and energy efficiency. Current processors rely on large shared Non-Uniform Cache Architectures (NUCA) to improve performance and reduce data movement. Multiple solutions exploit information available at the microarchitecture level or in the operating system to optimize NUCA performance. However, existing methods have not taken advantage of the information captured by task dataflow programming models to guide the management of NUCA caches.

In this paper, we propose TD-NUCA, a hardware/software co-designed approach that leverages information present in the runtime system of task dataflow programming models to efficiently manage NUCA caches. TD-NUCA identifies the data access and reuse patterns of parallel applications in the runtime system and guides the operation of the NUCA caches in the hardware. As a result, TD-NUCA achieves a 1.18x average speedup over the baseline S-NUCA while requiring only 0.62x the data movement.

Presentation: file

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