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

TL;DR

This paper explores using answer set programming (ASP) to generate all optimal schedules for the SCAD exposed datapath architecture, demonstrating competitive performance with SAT and SMT solvers and enabling detailed analysis of scheduling properties.

Contribution

It introduces ASP-based methods for optimal code generation in SCAD architectures, allowing comprehensive schedule enumeration and analysis.

Findings

ASP can generate all optimal schedules for SCAD.

ASP-based approach outperforms SMT solvers and competes with SAT solvers.

Enables detailed study of schedule properties.

Abstract

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. \emph{This paper is under consideration for acceptance in TPLP.}