APRIL: A Processor Architecture for Multiprocessing

Abstract

Processors in large scale multiprocessors must be able to tolerate large communication latencies and synchronization delays. This paper describes the architecture of a rapid-context-switching processor called APRIL with support for fine-grain threads and synchronization. APRIL achieves high single- thread performance and supports virtual dynamic threads. A commercial RISC-based implementation of APRIL and a run time software system that can switch contexts in about 10 cycles is described. Measurements taken for several parallel applications on an APRIL simulator show that the overhead for supporting parallel tasks based on futures is reduced by a factor of two over a corresponding implementation on the Encore Multimax. The scalability of a multiprocessor based on APRIL is explored using a performance model. We show that the SPARC-based implementation of APRIL can achieve close to 80% processor utilization with as few as three resident threads per processor in a large-scale cache-based machine with an average base network latency of 55 cycles.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1991
Accession Number
ADA237476

Entities

People

  • Anant Agarwal
  • Beng-hong Lim
  • David Kranz
  • John Kubiatowicz

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Coding
  • Computations
  • Computer Architecture
  • Computer Programming
  • Computer Science
  • Computers
  • Computing System Architectures
  • Information Processing
  • Instruction Set Architecture
  • Language
  • Multithreading
  • New York
  • Notation
  • Programming Languages
  • Simulators

Fields of Study

  • Computer science
  • Engineering

Readers

  • Library and Information Science/ Studies, Southeast Asia Studies, Bibliography of Vietnam and Lao Studies.
  • Parallel and Distributed Computing.