Toward a Microarchitecture for Efficient Execution of Irregular Applications

Author:

Leidel John D.1,Wang Xi2,Williams Brody2,Chen Yong2

Affiliation:

1. Tactical Computing Laboratories and Texas Tech University, Lubbock, Texas

2. Texas Tech University, Lubbock, Texas

Abstract

Given the increasing importance of efficient data-intensive computing, we find that modern processor designs are not well suited to the irregular memory access patterns often found in these algorithms. Applications and algorithms that do not exhibit spatial and temporal memory request locality induce high latency and low memory bandwidth due to the high cache miss rate. In response to the performance penalties inherently present in applications with irregular memory accesses, we introduce a GoblinCore-64 (GC64) architecture and a unique memory hierarchy that are explicitly designed to exploit memory performance from irregular memory access patterns. GC64 provides a pressure-driven hardware-managed concurrency control to minimize pipeline stalls and lower the latency of context switches. A novel memory coalescing model is also introduced to enhance the performance of memory systems via request aggregations. We have evaluated the performance benefits of our approach using a series of 24 benchmarks and the results show nearly 50% memory request reductions and a performance acceleration of up to 14.6×.

Funder

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Computational Theory and Mathematics,Computer Science Applications,Hardware and Architecture,Modelling and Simulation,Software

Reference45 articles.

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. VAPCNet: Viewpoint-Aware 3D Point Cloud Completion;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

2. A Survey on the Proposed Architectures for Efficient Execution of Irregular Applications Using Pipeline Parallelism;2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE);2023-07-24

3. DMM-GAPBS: Adapting the GAP Benchmark Suite to a Distributed Memory Model;2021 IEEE High Performance Extreme Computing Conference (HPEC);2021-09-20

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