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FPGA-Based Range-Limited Molecular Dynamics Acceleration.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	FPGA-Based Range-Limited Molecular Dynamics Acceleration./
Author:	Wu, Chunshu.
Description:	1 online resource (191 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798380323536

FPGA-Based Range-Limited Molecular Dynamics Acceleration.
Wu, Chunshu.

FPGA-Based Range-Limited Molecular Dynamics Acceleration. - 1 online resource (191 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--Boston University, 2023.

Includes bibliographical references

Molecular Dynamics (MD) is a computer simulation technique that executes iteratively over discrete, infinitesimal time intervals. It has been a widely utilized application in the fields of material sciences and computer-aided drug design for many years, serving as a crucial benchmark in high-performance computing (HPC). Numerous MD packages have been developed and effectively accelerated using GPUs. However, as the limits of Moore's Law are reached, the performance of an individual computing node has reached its bottleneck, while the performance of multiple nodes is primarily hindered by scalability issues, particularly when dealing with small datasets.In this thesis, the acceleration with respect to small datasets is the main focus. With the recent COVID-19 pandemic, drug discovery has gained significant attention, and Molecular Dynamics (MD) has emerged as a crucial tool in this process. Particularly, in the critical domain of drug discovery, small simulations involving approximately ∼50K particles are frequently employed. However, it is important to note that small simulations do not necessarily translate to faster results, as long-term simulations comprising billions of MD iterations and more are essential in this context.In addition to dataset size, the problem of interest is further constrained. Referred to as the most computationally demanding aspect of MD, the evaluation of range-limited (RL) forces not only accounts for 90% of the MD computation workload but also involves irregular mapping patterns of 3-D data onto 2-D processor networks. To emphasize, this thesis centers around the acceleration of RL MD specifically for small datasets.In order to address the single-node bottleneck and multi-node scaling challenges, the thesis is organized into two progressive stages of investigation. The first stage delves extensively into enhancing single-node efficiency by examining various factors such as workload mapping from 3-D to 2-D, data routing, and data locality. The second stage focuses on studying multi-node scalability, with a particular emphasis on strong scaling, bandwidth demands, and the synchronization mechanisms between nodes.Through our study, the results show our design on a Xilinx U280 FPGA achieves 51.72x and 4.17x speedups with respect to an Intel Xeon Gold 6226R CPU, and a Quadro RTX 8000 GPU. Our research towards strong scaling also demonstrates that 8 Xilinx U280 FPGAs connected to a switch achieves 4.67x speedup compared to an Nvidia V100 GPU.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380323536Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Computer architectureIndex Terms--Genre/Form:

554714
Electronic books.

FPGA-Based Range-Limited Molecular Dynamics Acceleration.
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