MS-CLS: An Effective Partitioning and Placement Metaheuristic for Wafer-Scale Physics Modeling

Finite element method (FEM) is one of the most successful numerical approaches for solving partial differential equations. It discretizes the target space into smaller and simpler parts to utilize the power of massively parallel computing, which usually involves two challenging combinatorial optimiz...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on emerging topics in computational intelligence Ročník 8; číslo 2; s. 1194 - 1208
Hlavní autoři: Luo, Canhui, Su, Zhouxing, Lu, Zhipeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial neural networks
Combinatorial analysis
Computing time
Costs
Finite element analysis
Finite element method
Hardware
Heuristic algorithms
Heuristic methods
heuristic search
Mathematical models
Parallel processing
Partial differential equations
Partitioning
Partitioning algorithms
Placement
placement and routing
Processors
Scheduling
Scheduling and task partitioning
Wafer scale integration
wafer-scale physics modeling
Wires
ISSN:2471-285X, 2471-285X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Finite element method (FEM) is one of the most successful numerical approaches for solving partial differential equations. It discretizes the target space into smaller and simpler parts to utilize the power of massively parallel computing, which usually involves two challenging combinatorial optimization problems: partitioning and placement. The partitioning decomposes the complete problem into a series of parallelizable tasks. The subsequent placement determines the mapping from computational tasks to parallel processors and optimizes the overall simulation overhead, such as latency and interference. Similar to the successful application of hardware acceleration for boosting the training of deep neural networks in the ISPD 2020 contest, a new challenge for exploiting the power of a wafer-scale engine to accelerate the finite element method was proposed in ISPD 2021. First, we need to partition the target space into non-uniform grids according to the heatmap to maximize the accuracy. Then, the computing task corresponding to each grid needs to be placed onto a 2D array of 848,000 processors to minimize the total communication overhead. In this paper, we propose an effective metaheuristic algorithm named MS-CLS for tackling this challenging problem by introducing a Merging-Splitting-based heuristic for partitioning and a Cluster-based Local Search metaheuristic for placement. Compared with the records of the ISPD 2021 contest, MS-CLS outperforms all the qualified teams in the contest on all the benchmark datasets in a short computational time. Moreover, similar techniques proposed in this work can be used on other hardware parallel acceleration applications.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2471-285X
2471-285X
DOI:10.1109/TETCI.2023.3336928