Towards hybrid supercomputing architectures

Author:

Korolija NenadORCID,Milfeld Kent

Abstract

In light of recent work on combining control-flow and dataflow architectures on the same chip die, a new architecture based on an asymmetric multicore processor is proposed. The control-flow architectures are described as a most commonly used computer architecture today. Both multicore and manycore architectures are explained, as they are based on the same principles. A dataflow computing model assumes that data input flows through hardware as either a software or hardware dataflow implementation. In software dataflow, processors based on the control-flow paradigm process tasks based on their availability from the same queue (if there are any). In hardware dataflow architectures, the hardware is configured for a particular algorithm, and data input is streamed into the hardware, and the output is streamed back to the multicore processor for further processing. Hardware dataflow architectures are usually implemented with FPGAs. Hybrid architectures employ asymmetric multicore and manycore computer architectures that are based on the control-flow and hardware dataflow architecture, all combined on the same chip die. Advantages include faster processing time, lower power consumption (and heating), and less space needed for the hardware.

Publisher

Centre for Evaluation in Education and Science (CEON/CEES)

Reference31 articles.

1. A. Kos, V. Ranković, and S. Tomažič, "Sorting networks on Maxeler dataflow supercomputing systems, " Advances in Computers, Vol. 96, pp. 139-186, 2015;

2. V. Ranković, A. Kos, and V. Milutinović, "Bitonic merge sort implementation on the maxeler dataflow supercomputing system, " The IPSI BgD Transactions on Internet Research, Vol. 9, No. 2, pp. 5-10, 2013;

3. N. Korolija, V. Milutinovic, and S. Milosevic, "Accelerating conjugate gradient solver: temporal versus spatial data, " The IPSI BgD Transactions on Advanced Research, Vol. 3, No. 1, pp. 21-25, 2007;

4. V. Milutinovic, M. Kotlar, S. Stojanovic, I. Dundic, N. Trifunovic, and Z. Babovic, "Implementing Neural Networks by Using the DataFlow Paradigm, " In DataFlow Supercomputing Essentials, New York: Springer Cham, 2017, pp. 3-44;

5. V. Jelisavcic, I. Stojkovic, V. Milutinovic, and Z. Obradovic, "Fast learning of scale-free networks based on Cholesky factorization, " International Journal of Intelligent Systems, Vol. 33, No. 6, pp. 1322-1339, 2018;

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Drawbacks of Programming Dataflow Architectures and Methods to Overcome Them;Lecture Notes in Networks and Systems;2024

2. Merging control-flow and dataflow architectures on a single chip;Journal of Computer and Forensic Sciences;2024

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3