Pipelining and dataflow techniques for designing supercomputers
Extensive research has been conducted over the last two decades in developing supercomputers to meet the demand of high computational performance. This thesis investigates some pipelining and dataflow techniques for designing supercomputers. In the pipelining area, new techniques are developed for scheduling vector instructions in a multi-pipeline supercomputer and for constructing VLSI matrix arithmetic pipelines for large-scale matrix computations. In the dataflow area, a new approach is proposed to dispatch high-level functions for dependence-driven computations. A parallel task scheduling model is proposed for multi-pipeline vector supercomputers. This model can be applied to explore maximal concurrencies in vector supercomputers with a structure generalized from the CRAY-1, CYBER-205, and TI-ASC. The optimization problem of simultaneously scheduling multiple pipelines is proved to be MP-complete. Thus, heuristic scheduling algorithms for some restricted classes of vector task systems are developed. Nearly optimal performance can be achieved with the proposed parallel pipeline scheduling method. Simulation results on randomly generated task systems are presented to verify the analytical performance bounds. For dependence-driven computations, a dataflow controller is used to perform run-time scheduling of compound functions. The scheduling problem is shown to be NP-complete. Several heuristic scheduling strategies are proposed based on the time and resource demands of compound functions.
- OSTI ID:
- 5114412
- Resource Relation:
- Other Information: Thesis (Ph.D.)
- Country of Publication:
- United States
- Language:
- English
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