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Merchandiser: Data Placement on Heterogeneous Memory for Task-Parallel HPC Applications with Load-Balance Awareness

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Dong LiAuthors Info & Claims

PPoPP '23: Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

Pages 204 - 217

https://doi.org/10.1145/3572848.3577497

Published: 21 February 2023 Publication History

Abstract

The emergence of heterogeneous memory (HM) provides a cost-effective and high-performance solution to memory-consuming HPC applications. Deciding the placement of data objects on HM is critical for high performance. We reveal a performance problem related to data placement on HM. The problem is manifested as load imbalance among tasks in task-parallel HPC applications. The root of the problem comes from being unaware of parallel-task semantics and an incorrect assumption that bringing frequently accessed pages to fast memory always leads to better performance. To address this problem, we introduce a load balance-aware page management system, named Merchandiser. Merchandiser introduces task semantics during memory profiling, rather than being application-agnostic. Using the limited task semantics, Merchandiser effectively sets up coordination among tasks on the usage of HM to finish all tasks fast instead of only considering any individual task. Merchandiser is highly automated to enable high usability. Evaluating with memory-consuming HPC applications, we show that Merchandiser reduces load imbalance and leads to an average of 17.1% and 15.4% (up to 26.0% and 23.2%) performance improvement, compared with a hardware-based solution and an industry-quality software-based solution.

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Parasyris KGeorgakoudis GRangel ELaguna IDoerfert JMohror KArnold DBadia R(2023)Scalable Tuning of (OpenMP) GPU Applications via Kernel Record and ReplayProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607098(1-14)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607098
Xie ZRaskar SEmani MVishwanath V(2023)TrainBF: High-Performance DNN Training Engine Using BFloat16 on AI AcceleratorsEuro-Par 2023: Parallel Processing10.1007/978-3-031-39698-4_31(458-473)Online publication date: 28-Aug-2023
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Merchandiser: Data Placement on Heterogeneous Memory for Task-Parallel HPC Applications with Load-Balance Awareness

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cover image ACM Conferences

PPoPP '23: Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

February 2023

480 pages

ISBN:9798400700156

DOI:10.1145/3572848

General Chair:
Maryam Mehri Dehnavi
University of Toronto
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Milind Kulkarni
Purdue University
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Cited By

Parasyris KGeorgakoudis GRangel ELaguna IDoerfert JMohror KArnold DBadia R(2023)Scalable Tuning of (OpenMP) GPU Applications via Kernel Record and ReplayProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607098(1-14)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607098
Xie ZRaskar SEmani MVishwanath V(2023)TrainBF: High-Performance DNN Training Engine Using BFloat16 on AI AcceleratorsEuro-Par 2023: Parallel Processing10.1007/978-3-031-39698-4_31(458-473)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-39698-4_31

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