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Mapping high level algorithms onto massively parallel reconfigurable hardware

Authors: I. Damaj; J. Hawkins; A. Abdallah;

Mapping high level algorithms onto massively parallel reconfigurable hardware

Abstract

Summary form only given, as follows. We focus on implementing high level functional algorithms in reconfigurable hardware. The approach adopts the transformational programming paradigm for deriving massively parallel algorithms from functional specifications. It extends previous work by systematically generating efficient circuits and mapping them onto reconfigurable hardware. The massive parallelisation of the algorithm works by carefully composing "off the shelf" highly parallel implementations of each of the basic building blocks involved in the algorithm. These basic building blocks are a small collection of well-known higher order functions such as map, fold, and zipwith. By using function decomposition and data refinement techniques, these powerful functions are refined into highly parallel implementations described in Hoare's CSP. The CSP descriptions are very closely associated with Handle-C program fragments. Handle-C is a programming language based on C and extended by parallelism and communication primitives taken from CSP. In the final stage the circuit description is generated by compiling Handle-C programs and then mapped onto the targeted reconfigurable hardware such as the RC-1000 FPGA system from Celoxica. This approach is illustrated by a case study involving the generation of several versions of the matrix multiplication algorithm.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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