Virtual memory on data diffusion architectures

  • Authors:

  • Jorge Buenabad-Chávez;Henk L. Muller;Paul W. A. Stallard;David H. D. Warren

  • Affiliations:

  • Sección de Computación, CINVESTAV, Ap. Postal 14-740, México DF 07360, Mexico;Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK;Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK;Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK

  • Venue:
  • Parallel Computing
  • Year:
  • 2003

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Abstract

Data diffusion architectures (also known as cache only memory architectures) provide, a shared address space on top of distributed memory. Their distinctive feature is that data diffuses, or migrates and replicates, in main memory according to whichever processors are using the data. This requires an associative organisation of main memory, which decouples each address and its data item from any physical location. A data item can thus be placed and replicated where it is needed. Also, the physical address space does not have to be fixed and contiguous. It can be any set of addresses within the address range of the processors, possibly varying over time, provided it is smaller than the size of main memory. This flexibility is similar to that of a virtual address space, and offers new possibilities to organise a virtual memory system.We present an analysis of possible organisations of virtual memory on such architectures, and propose two main alternatives: traditional virtual memory (TVM) is organised around a fixed and contiguous physical address space using a traditional mapping; associative memory virtual memory (AMVM) is organised around a variable and non-contiguous physical address space using a simpler mapping.To evaluate TVM and AMVM, we extended a multiprocessor emulation of a data diffusion architecture to include part of the Mach operating system virtual memory. This extension implements TVM; a slightly modified version implements AMVM. On applications tested, AMVM shows a marginal performance gain over TVM. We argue that AMVM will offer greater advantages with higher degrees of parallelism or larger data sets.