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

EasyChair Preprint no. 205

14 pagesDate: May 31, 2018


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 \emph{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.

Keyphrases: Answer Set Programming, code generation, Exposed Datapath Architectures, optimal scheduling

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Marc Dahlem and Anoop Bhagyanath and Klaus Schneider},
  title = {Optimal Scheduling for Exposed Datapath Architectures with Buffered Processing Units by ASP},
  howpublished = {EasyChair Preprint no. 205},
  doi = {10.29007/678g},
  year = {EasyChair, 2018}}
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