Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81240
Title: Limits of Statically-Scheduled Token Dataflow Processing
Authors: Kapre, Nachiket
Siddhartha
Keywords: Computer Science and Engineering
Issue Date: 2014
Source: Kapre, N., & Siddhartha. (2014). Limits of Statically-Scheduled Token Dataflow Processing. 2014 Fourth Workshop on Data-Flow Execution Models for Extreme Scale Computing, 1-8.
Conference: 2014 Fourth Workshop on Data-Flow Execution Models for Extreme Scale Computing
Abstract: FPGA-based token dataflow processing has been shown to accelerate hard-to-parallelize problems exhibiting irregular dataflow parallelism by as much as an order of magnitude when compared to conventional compute organizations. However, when the structure of the dataflow computation is known upfront, either at compile time or at the start of execution, we can employ static scheduling techniques to further improve performance and enhance compute density of the dataflow hardware. In this paper, we identify the costs and performance trends of both static and dynamic scheduling approaches when considering hardware acceleration of SPICE device equations and Sparse LU factorization in circuit graphs. While the experiments are limited to a case study, the hardware design and dataflow compiler are general and can be extended to other problems and instances where dataflow computing may be applicable. With this study, we hope to develop a quantitative basis for the design of a hybrid dataflow architecture that combines both static and dynamic scheduling techniques. We observe a performance benefit of 2 - 4× and a resource utilization saving of 2 - 3× in favor of statically scheduled hardware.
URI: https://hdl.handle.net/10356/81240
http://hdl.handle.net/10220/39193
DOI: 10.1109/DFM.2014.21
Schools: School of Computer Engineering 
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/DFM.2014.21].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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