Affiliation:
1. Duke University, Durham, NC
2. North Carolina State University, Raleigh, NC
Abstract
Instruction window size is an important design parameter for many modern processors. Large instruction windows offer the potential advantage of exposing large amounts of instruction level parallelism. Unfortunately naively scaling conventional window designs can significantly degrade clock cycle time, undermining the benefits of increased parallelism.This paper presents a new instruction window design targeted at achieving the latency tolerance of large windows with the clock cycle time of small windows. The key observation is that instructions dependent on a long latency operation (e.g., cache miss) cannot execute until that source operation completes. These instructions are moved out of the conventional, small, issue queue to a much larger waiting instruction buffer (WIB). When the long latency operation completes, the instructions are reinserted into the issue queue. In this paper, we focus specifically on load cache misses and their dependent instructions. Simulations reveal that, for an 8-way processor, a 2K-entry WIB with a 32-entry issue queue can achieve speedups of 20%, 84%, and 50% over a conventional 32-entry issue queue for a subset of the SPEC CINT2000, SPEC CFP2000, and Olden benchmarks, respectively.
Publisher
Association for Computing Machinery (ACM)
Cited by
18 articles.
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1. EMISSARY: Enhanced Miss Awareness Replacement Policy for L2 Instruction Caching;Proceedings of the 50th Annual International Symposium on Computer Architecture;2023-06-17
2. CRISP: critical slice prefetching;Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems;2022-02-22
3. Decoupling loads for nano-instruction set computers;ACM SIGARCH Computer Architecture News;2016-10-12
4. MLP-Aware Dynamic Instruction Window Resizing in Superscalar Processors for Adaptively Exploiting Available Parallelism;IEICE Transactions on Information and Systems;2014
5. Instruction Scheduling in Microprocessors;Studies in Computational Intelligence;2013