STI Publications - View Publication Form #17021
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Publication Information
| Title | 24 MemHC: An Optimized GPU Memory Management Framework for Accelerating Many-body Correlation | ||||
| Abstract | The many-body correlation function is a fundamental computation kernel in modern physics computing applications, e.g., Hadron Contractions in Lattice quantum chromodynamics (QCD). This kernel is both computation and memory intensive, involving a series of tensor contractions, and thus usually runs on accelerators like GPUs. Existing optimizations on many-body correlation mainly focus on individual tensor contractions (e.g., cuBLAS libraries and others). In contrast, this work discovers a new optimization dimension for many-body correlation by exploring the optimization opportunities among tensor contractions. More specifically, it targets general GPU architectures (both NVIDIA and AMD) and optimizes many-body correlation¿s memory management by exploiting a set of memory allocation and communication redundancy elimination opportunities: first, GPU memory allocation redundancy: the intermediate output frequently occurs as input in the subsequent calculations; second, CPU-GPU communicatio | ||||
| Author(s) | Qihan Wang, Zhen Peng, Bin Ren, Jie Chen, Robert Edwards | ||||
| Publication Date | June 2022 | ||||
| Document Type | Journal Article | ||||
| Primary Institution | Thomas Jefferson National Accelerator Facility, Newport News | ||||
| Affiliation | Comp Sci&Tech (CST) Div / Scientific Computing / Scientific Computing | ||||
| Funding Source | Nuclear Physics (NP) | ||||
| Proprietary? | No | ||||
| This publication conveys | Technical Science Results | ||||
| Document Numbers |
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| Associated with an experiment | No | ||||
| Associated with EIC | No | ||||
| Supported by Jefferson Lab LDRD Funding | No |
Journal Article
| Journal Name | ACM Transactions on Architecture and Code Optimization |
| Refereed | Yes |
| Volume | 19 |
| Issue | 2 |
| Page(s) | 24 |
Attachments/Datasets/DOI Link
| Document(s) |
3506705.pdf
(STI Document)
3506705.pdf
(Accepted Manuscript)
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| DOI Link | |
| Dataset(s) | (none) |
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