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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qi, Ruixuan Yang, Wangdong Yan, Xiuwen Li, Honglu Zhang, Yifei Li, Kenli Hunan University College of Computer Science and Electronic Engineering Hunan Changsha China The 48th Research Institute of China Electronic Technology Corporation Hunan Changsha China

ISBN: (纸本)9798350330717

We design a computational framework for ion implantation into silicon that can simulate the reaction process of cascade collisions in silicon and the subsequent annealing process. Since simulating materials requires large-scale particle data and high-precision simulation requirements, which often take up a lot of computing time, the conventional computing process needs to be accelerated in parallel to solve current problems. However, the current field of ion implantation focuses on the development of algorithms, often neglecting the design of parallel algorithms, and has not tested them on large-scale supercomputing clusters. Based on Tianhe new generation high-performance computers, this paper proposes a parallel computing framework for numerical simulation of ion implanted silicon based on multi-core processor architecture. By optimizing the data structure, the local continuity of the data is enhanced to improve the overall communication efficiency. Divide three-dimensional data for processes and adapt to MPI parallel computing strategies. Finally, a multi-level parallel computing strategy based on OpenMP is added to improve computing efficiency. After being deployed on the Tianhe new generation high-performance computer platform, experimental results show that on a single node, the multi-level parallel computing framework can increase the computing speed by 20.81X. After conducting tests at different scales, we can see that the parallel efficiency can be maintained at a high level. © 2023 IEEE.

关键词： data structure optimization ion implantation multi-level parallel strategy

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Adaptive just-in-time value class optimization for lowering memory consumption and improving execution time performance

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SCIENCE OF COMPUTER PROGRAMMING 2017年 140卷 17-29页

作者： Pape, Tobias Bolz, Carl Friedrich Hirschfeld, Robert Univ Potsdam Hasso Plattner Inst Software Architecture Grp Potsdam Germany Kings Coll London Software Dev Team London England

The performance of value classes is highly dependent on how they are represented in the virtual machine. Value class instances are immutable, have no identity, and can only refer to other value objects or primitive values and since they should be very lightweight and fast, it is important to optimize them carefully. In this paper we present a technique to detect and compress common patterns of value class usage to improve memory usage and performance. The technique identifies patterns of frequent value object references and introduces abbreviated forms for them. This allows to store multiple inter-referenced value objects in an inlined memory representation, reducing the overhead stemming from meta data and object references. Applied to a small prototype and an implementation of the Racket language, we found improvements in memory usage and execution time for several micro-benchmarks. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Meta-tracing JIT data structure optimization Value classes

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Adaptive Just-in-time Value Class optimization Transparent data structure Inlining for Fast Execution 15

Adaptive Just-in-time Value Class Optimization Transparent D...

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30th ACM Symposium on Applied Computing (SAC)

作者： Pape, Tobias Bolz, Carl Friedrich Hirschfeld, Robert Univ Potsdam Hasso Plattner Inst Potsdam Germany Kings Coll London Software Dev Team London WC2R 2LS England

ISBN: (纸本)9781450331968

关键词： Meta-tracing JIT data structure optimization Value Classes

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