An empirical study of decentralized ILP execution models

ABSTRACT

Recent fascination for dynamic scheduling as a means for exploiting instruction-level parallelism has introduced significant interest in the scalability aspects of dynamic scheduling hardware. In order to overcome the scalability problems of centralized hardware schedulers, many decentralized execution models are being proposed and investigated recently. The crux of all these models is to split the instruction window across multiple processing elements (PEs) that do independent, scheduling of instructions. The decentralized execution models proposed so far can be grouped under 3 categories, based on the criterion used for assigning an instruction to a particular PE. They are: (i) execution unit dependence based decentralization (EDD), (ii) control dependence based decentralization (CDD), and (iii) data dependence based decentralization (DDD). This paper investigates the performance aspects of these three decentralization approaches. Using a suite of important benchmarks and realistic system parameters, we examine performance differences resulting from the type of partitioning as well as from specific implementation issues such as the type of PE interconnect.We found that with a ring-type PE interconnect, the DDD approach performs the best when the number of PEs is moderate, and that the CDD approach performs best when the number of PEs is large. The currently used approach---EDD---does not perform well for any configuration. With a realistic crossbar, performance does not increase with the number of PEs for any of the partitioning approaches. The results give insight into the best way to use the transistor budget available for implementing the instruction window.

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Index Terms

• Computer systems organization

  • Architectures

    • Parallel architectures

      • Very long instruction word

    • Serial architectures

      • Complex instruction set computing

      • Reduced instruction set computing

  • Dependable and fault-tolerant systems and networks

• General and reference

  • Cross-computing tools and techniques

    • Performance

• Mathematics of computing

  • Mathematical software

• Networks

  • Network performance evaluation

• Software and its engineering

  • Software organization and properties

    • Contextual software domains

      • Operating systems

        • Process management

          • Scheduling

• Theory of computation

  • Design and analysis of algorithms

    • Approximation algorithms analysis

      • Scheduling algorithms

    • Online algorithms

      • Online learning algorithms

        • Scheduling algorithms

  • Models of computation

    • Concurrency

      • Parallel computing models

  • Theory and algorithms for application domains

    • Machine learning theory

      • Reinforcement learning

        • Sequential decision making