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Approximation algorithm for data mapping on block multi-threaded network processor architectures

Published: 04 June 2007 Publication History

Abstract

Network processor architectures incorporate block multi-threading to alleviate the performance degradation due to memory access latencies. Application design on such architectures requires the determination of the number of threads, and mapping of data items to the various memory elements such that the overall throughput is maximized. The paper presents a quasi-polynomial time approximation algorithm for the multi-threading aware data mapping problem which can be shown to be NP complete. The algorithm generates solutions with throughput no less than 1 / 2(1+ε) of optimal and data memory requirements no more than (1 + ε) times the memory constraints. Experimental results obtained by mapping applications on the Intel IXP 2400 network processor demonstrate that the algorithm is able to generate solutions whose throughput is within 80% of the optimal when ε = 0.5.

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  1. Approximation algorithm for data mapping on block multi-threaded network processor architectures

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    cover image ACM Conferences
    DAC '07: Proceedings of the 44th annual Design Automation Conference
    June 2007
    1016 pages
    ISBN:9781595936271
    DOI:10.1145/1278480
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 04 June 2007

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    Author Tags

    1. block multi-threading
    2. network processing

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    DAC '07 Paper Acceptance Rate 152 of 659 submissions, 23%;
    Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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