How to Manage High-Bandwidth Memory Automatically Rathish Das Kunal Agrawal Michael A. Bender Stony Brook University Washington University in St. Louis Stony Brook University
[email protected] [email protected] [email protected] Jonathan Berry Benjamin Moseley Cynthia A. Phillips Sandia National Laboratories Carnegie Mellon University Sandia National Laboratories
[email protected] [email protected] [email protected] ABSTRACT paging algorithms; Parallel algorithms; Shared memory al- This paper develops an algorithmic foundation for automated man- gorithms; • Computer systems organization ! Parallel ar- agement of the multilevel-memory systems common to new super- chitectures; Multicore architectures. computers. In particular, the High-Bandwidth Memory (HBM) of these systems has a similar latency to that of DRAM and a smaller KEYWORDS capacity, but it has much larger bandwidth. Systems equipped with Paging, High-bandwidth memory, Scheduling, Multicore paging, HBM do not fit in classic memory-hierarchy models due to HBM’s Online algorithms, Approximation algorithms. atypical characteristics. ACM Reference Format: Unlike caches, which are generally managed automatically by Rathish Das, Kunal Agrawal, Michael A. Bender, Jonathan Berry, Benjamin the hardware, programmers of some current HBM-equipped super- Moseley, and Cynthia A. Phillips. 2020. How to Manage High-Bandwidth computers can choose to explicitly manage HBM themselves. This Memory Automatically. In Proceedings of the 32nd ACM Symposium on process is problem specific and resource intensive. Vendors offer Parallelism in Algorithms and Architectures (SPAA ’20), July 15–17, 2020, this option because there is no consensus on how to automatically Virtual Event, USA. ACM, New York, NY, USA, 13 pages. https://doi.org/10.