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Optimal Scheduling for Exposed Datapath Architectures with Buffered Processing Units by ASP

Published online by Cambridge University Press:  10 August 2018

MARC DAHLEM
Affiliation:
Insiders Technologies GmbH, Kaiserslautern, Germanyhttps://insiders-technologies.de
ANOOP BHAGYANATH
Affiliation:
Department of Computer Science, University of Kaiserslautern, Germanyhttps://es.cs.uni-kl.de
KLAUS SCHNEIDER
Affiliation:
Department of Computer Science, University of Kaiserslautern, Germanyhttps://es.cs.uni-kl.de
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Abstract

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Conventional processor architectures are restricted in exploiting instruction level parallelism (ILP) due to the relatively low number of programmer-visible registers. Therefore, more recent processor architectures expose their datapaths so that the compiler (1) can schedule parallel instructions to different processing units and (2) can make effective use of local storage of the processing units. Among these architectures, the Synchronous Control Asynchronous Dataflow (SCAD) architecture is a new exposed datapath architecture whose processing units are equipped with first-in first-out (FIFO) buffers at their input and output ports.

In contrast to register-based machines, the optimal code generation for SCAD is still a matter of research. In particular, SAT and SMT solvers were used to generate optimal resource constrained and optimal time constrained schedules for SCAD, respectively. As Answer Set Programming (ASP) offers better flexibility in handling such scheduling problems, we focus in this paper on using an answer set solver for both resource and time constrained optimal SCAD code generation. As a major benefit of using ASP, we are able to generate all optimal schedules for a given program which allows one to study their properties. Furthermore, the experimental results of this paper demonstrate that the answer set solver can compete with SAT solvers and outperforms SMT solvers. This paper is under consideration for acceptance in TPLP.

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Type
Original Article
Copyright
Copyright © Cambridge University Press 2018