Work

Managing High-Bandwidth Memory is a Parallel Scheduling Problem (full paper only)

Year: 2025

Type: conference-paper

Abstract: High-Bandwidth Memory (HBM) is a decade-old memory technology that is increasingly commonly being used in highly-parallel machines such as GPUs and multicores. Comparatively, HBM has higher bandwidth, smaller capacity, and similar latency to other DRAM technologies. Many systems use both HBM and other DRAM technologies, where HBM is naturally closer to the processor in the conceptual memory hierarchy. Thus, a natural resulting question is how one should best manage a collection of processes running on a HBM/DRAM memory hierarchy. Prior work introduced a theoretical model for addressing this question, and gave a competitive policy for the objective of minimizing makespan. Our main technical contribution is to give a competitive policy for the more commonly appropriate total/average response/completion time objective. However, we believe the broader, and more important contribution, is to make explicit the case (hinted at in the prior literature) that managing an HBM/DRAM hierarchy should be thought of as a parallel scheduling problem. To that end, we introduce a new online scheduling model that we call the semi-normal model. We then show how to use a competitive algorithm for scheduling in the semi-normal model as a black box to obtain a competitive algorithm for managing a HBM/DRAM memory hierarchy. Thus, as a result of this black-box conversion, competitiveness results in the semi-normal model translate (essentially) automatically into competitiveness results in the HBM/DRAM management model. Our main technical result is then an application of such a translation. That is, we show that a natural variant of the Round Robin (processor sharing) algorithm, naturally adapted for the seminormal model, is competitive for the objective of average/total completion time. Thus, we obtain an algorithm for managing a HBM/DRAM hierarchy that is competitive for the objective of average/total completion time, using this black-box reduction.

Source: ACM Symposium on Parallelism in Algorithms and Architectures (SPAA)

DOI: 10.1145/3694906.3743336

Authors: Kunal Agrawal, Michael A. Bender, Kirk Pruhs, Benjamin Moseley, Clifford Stein

Institutions: Washington University in St. Louis, Stony Brook University, University of Pittsburgh, Carnegie Mellon University, Columbia University

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Universitas Brawijaya, Office of Naval Research