Scalar Vector Runahead: Removing the Shackles of Indirect Memory Chains on In-Order Cores

Modern processors often face the memory wall as a significant bottleneck, a problem that becomes particularly severe when using stall-on-use in-order cores. Despite this limitation, there is growing demand for energy-efficient in-order cores due to privacy and sustainability concerns. Scalar Vector Runahead (SVR)1 provides an elegant solution by extracting high memory-level parallelism through piggybacking on existing instructions executed on the processor that lead to future irregular memory accesses. SVR speculatively executes multiple transient, independent, parallel instances of memory accesses and their instruction chains, by initiating memory accesses from many different values of a predicted induction variable. This approach moves mutually independent memory accesses next to each other to hide dependent stalls. With a hardware overhead of only 2 KiB and without the need for hardware vector extensions, SVR delivers 3.2× higher performance than a baseline 3-wide in-order core inspired by an Arm Cortex A510, and 1.3× higher performance than a full out-of-order core, while halving energy consumption.

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Publication Details

Journal
IEEE Micro
Published
2025-06-09
DOI
https://doi.org/10.1109/mm.2025.3577524
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Scalar Vector Runahead: Removing the Shackles of Indirect Memory Chains on In-Order Cores

Lieven Eeckhout, Sam Ainsworth, Ajeya Naithani, Timothy M. Jones et al.
IEEE Micro
Parallel Computing and Optimization Techniques
article

Scalar Vector Runahead: Removing the Shackles of Indirect Memory Chains on In-Order Cores

Lieven Eeckhout, Sam Ainsworth, Ajeya Naithani, Timothy M. Jones, Jaime Roelandts
article en

Abstract

Modern processors often face the memory wall as a significant bottleneck, a problem that becomes particularly severe when using stall-on-use in-order cores. Despite this limitation, there is growing demand for energy-efficient in-order cores due to privacy and sustainability concerns. Scalar Vector Runahead (SVR)1 provides an elegant solution by extracting high memory-level parallelism through piggybacking on existing instructions executed on the processor that lead to future irregular memory accesses. SVR speculatively executes multiple transient, independent, parallel instances of memory accesses and their instruction chains, by initiating memory accesses from many different values of a predicted induction variable. This approach moves mutually independent memory accesses next to each other to hide dependent stalls. With a hardware overhead of only 2 KiB and without the need for hardware vector extensions, SVR delivers 3.2× higher performance than a baseline 3-wide in-order core inspired by an Arm Cortex A510, and 1.3× higher performance than a full out-of-order core, while halving energy consumption.

IEEE MicroVol. 45(4)
University of Cambridge (GB), Ghent University Hospital (BE), Eindhoven University of Technology (NL), University of Edinburgh (GB)
Fonds Wetenschappelijk Onderzoek, Vlaamse regering, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 88%
Parallel Computing and Optimization Techniques
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Scalar Vector Runahead: Removing the Shackles of Indirect Memory Chains on In-Order Cores — Lieven Eeckhout, Sam Ainsworth, et al. · IEEE Micro (2025) | TGRS Research Map | TGRS