Collections and garbage collection

  • Simon C. Merrall
  • Julian A. Padget
Massive Parrallel Architectures
Part of the Lecture Notes in Computer Science book series (LNCS, volume 637)


We present here a data parallel dialect of lisp, Plural EuLisp, which is a relatively low-level abstract model of massively parallel processing. It is not as rich as languages like Connection Machine Lisp and Paralation Lisp but encompasses ideas integral to at least Paralation Lisp. However its low-level nature makes the explanation of the underlying processor/memory management mechanisms easier as the low level structures are closer to the objects in Plural EuLisp. We describe how memory and processors are allocated and garbage collected, with particular interest in heterogeneous data parallel objects — which in general have been considered too expensive to be supported seriously.


Data Parallelism Garbage Collection Heterogeneous Collections Lisp Processor/Memory Management SIMD 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bale, A. Implementing Lisp on the ICL Distributed Array Processor. Queen Mary College, Dept. of Computer Science, 1986.Google Scholar
  2. 2.
    Blelloch, G. E. and Sabot, G. W. Compiling Collection-Oriented languages onto Massively Parallel Computers, volume 8, pages 119–134. Journal of Parallel and Distributed Computing, 1990.CrossRefGoogle Scholar
  3. 3.
    Lang et al. Gargbage Collecting the World. ACM Symposium on Principles of Programming Languages, New York, 1992.Google Scholar
  4. 4.
    Evett, M. and Hendler, J. Achieving Computationally Effective Knowledge Representation via Massively Parallel Lisp Implementation. Europal Workshop for High Performance and Parallel Computing in Lisp, Nov 1990.Google Scholar
  5. 5.
    Fitch, J. P. and Norman, A. C. A Note on Compacting Garbage Collection, volume 10, pages 31–34. The Computer Journal, July 1976.Google Scholar
  6. 6.
    Haddon, B. K. and Waite, Q. M. A Compacting Procedure for variable-length storage elements, volume 10, page 162. The Computer Journal, 1967.Google Scholar
  7. 7.
    Hillis, W. D. The Connection Machine. MIT Press, Cambridge, MA, 1985.Google Scholar
  8. 8.
    Padget, J. A. and Nuyens, G. The EuLisp Definition. to be published by the Commission of the European Communities, 1992.Google Scholar
  9. 9.
    Sabot, G. W. Paralation Lisp Reference Manual. Thinking Machines Corp., 1988. Tech. Report PL87-11.Google Scholar
  10. 10.
    Sabot, G. W. The Paralation Model: Architecture Independent SIMD Programming. MIT Press, Cambridge, MA, 1988.Google Scholar
  11. 11.
    Steele, G. L., Jr., and Hillis, W. D. Connection Machine Lisp: Fine-Grained Parallel Symbolic Processing, pages 279–297. ACM Conference on Lisp and Functional Programming, 1986.Google Scholar
  12. 12.
    Steele, G. L., Jr., and Wholey, S. Connection Machine Lisp: A Dialect of Common Lisp for Data Parallel Programming. International Conference on Super Computing, 1987. TMC Tech. Report PL87-6.Google Scholar
  13. 13.
    Thinking Machines Corporation. *Lisp Reference Manual, 1988.Google Scholar
  14. 14.
    Tomboulian, S. and Pappas, M. Indirect Addressing and Load Balancing for Faster Solution to Mandelbrot Set on SIMD architectures. MasPar Corporation Tech. Report, October 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Simon C. Merrall
    • 1
  • Julian A. Padget
    • 1
  1. 1.School of Mathematical SciencesBath UniversityBathUK

Personalised recommendations