Speculative precomputation

Author:

Collins Jamison D.1,Wang Hong2,Tullsen Dean M.1,Hughes Christopher3,Lee Yong-Fong4,Lavery Dan4,Shen John P.2

Affiliation:

1. Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA

2. Microprocessor Research Lab, Intel Corporation, Santa Clara, CA

3. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL

4. Microcomputer Software Lab, Intel Corporation, Santa Clara, CA

Abstract

This paper explores Speculative Precomputation, a technique that uses idle thread context in a multithreaded architecture to improve performance of single-threaded applications. It attacks program stalls from data cache misses by pre-computing future memory accesses in available thread contexts, and prefetching these data. This technique is evaluated by simulating the performance of a research processor based on the Itanium™ ISA supporting Simultaneous Multithreading. Two primary forms of Speculative Precomputation are evaluated. If only the non-speculative thread spawns speculative threads, performance gains of up to 30% are achieved when assuming ideal hardware. However, this speedup drops considerably with more realistic hardware assumptions. Permitting speculative threads to directly spawn additional speculative threads reduces the overhead associated with spawning threads and enables significantly more aggressive speculation, overcoming this limitation. Even with realistic costs for spawning threads, speedups as high as 169% are achieved, with an average speedup of 76%.

Publisher

Association for Computing Machinery (ACM)

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1. Tyche: An Efficient and General Prefetcher for Indirect Memory Accesses;ACM Transactions on Architecture and Code Optimization;2024-03-23

2. Trends in Computing and Memory Technologies;Emerging Computing: From Devices to Systems;2022-07-09

3. Post-Fabrication Microarchitecture;MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture;2021-10-17

4. Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning;MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture;2021-10-17

5. Parallel Precomputation with Input Value Prediction for Model Predictive Control Systems;IEICE Transactions on Information and Systems;2018-12-01

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