Matryoshka: Optimization of Dynamic Diverse Quantum Chemistry Systems via Elastic Parallelism Transformation
Guardado en:
| Udgivet i: | arXiv.org (Dec 23, 2024), p. n/a |
|---|---|
| Hovedforfatter: | |
| Andre forfattere: | , , , , , , , |
| Udgivet: |
Cornell University Library, arXiv.org
|
| Fag: | |
| Online adgang: | Citation/Abstract Full text outside of ProQuest |
| Tags: |
Ingen Tags, Vær først til at tagge denne postø!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3147267343 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3147267343 | ||
| 045 | 0 | |b d20241223 | |
| 100 | 1 | |a Wang, Tuowei | |
| 245 | 1 | |a Matryoshka: Optimization of Dynamic Diverse Quantum Chemistry Systems via Elastic Parallelism Transformation | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 23, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a AI infrastructures, predominantly GPUs, have delivered remarkable performance gains for deep learning. Conversely, scientific computing, exemplified by quantum chemistry systems, suffers from dynamic diversity, where computational patterns are more diverse and vary dynamically, posing a significant challenge to sponge acceleration off GPUs. In this paper, we propose Matryoshka, a novel elastically-parallel technique for the efficient execution of quantum chemistry system with dynamic diversity on GPU. Matryoshka capitalizes on Elastic Parallelism Transformation, a property prevalent in scientific systems yet underexplored for dynamic diversity, to elastically realign parallel patterns with GPU architecture. Structured around three transformation primitives (Permutation, Deconstruction, and Combination), Matryoshka encompasses three core components. The Block Constructor serves as the central orchestrator, which reformulates data structures accommodating dynamic inputs and constructs fine-grained GPU-efficient compute blocks. Within each compute block, the Graph Compiler operates offline, generating high-performance code with clear computational path through an automated compilation process. The Workload Allocator dynamically schedules workloads with varying operational intensities to threads online. It achieves highly efficient parallelism for compute-intensive operations and facilitates fusion with neighboring memory-intensive operations automatically. Extensive evaluation shows that Matryoshka effectively addresses dynamic diversity, yielding acceleration improvements of up to 13.86x (average 9.41x) over prevailing state-of-the-art approaches on 13 quantum chemistry systems. | |
| 653 | |a Elastic properties | ||
| 653 | |a Workload | ||
| 653 | |a Parallel processing | ||
| 653 | |a Quantum chemistry | ||
| 653 | |a Permutations | ||
| 653 | |a Graphics processing units | ||
| 653 | |a Data structures | ||
| 700 | 1 | |a Li, Kun | |
| 700 | 1 | |a Bai, Donglin | |
| 700 | 1 | |a Ju, Fusong | |
| 700 | 1 | |a Xia, Leo | |
| 700 | 1 | |a Cao, Ting | |
| 700 | 1 | |a Ren, Ju | |
| 700 | 1 | |a Zhang, Yaoxue | |
| 700 | 1 | |a Mao, Yang | |
| 773 | 0 | |t arXiv.org |g (Dec 23, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3147267343/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.13203 |