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Evaluation of Dynamic Data Distributions on NUMA Shared Memory Multiprocessors
 

Summary: Evaluation of Dynamic Data Distributions on
NUMA Shared Memory Multiprocessors
Tarek S. Abdelrahman and Kenneth L. Ma
Department of Electrical and Computer Engineering
The University of Toronto
Toronto, Ontario, Canada M5S 3G4
Abstract
Dynamic data distributions offer a number of performance benefits, but require more
sophisticated compiler support and incur run-time overhead. We investigate attainable
benefits using a compiler system we developed for the Hector NUMA multiprocessor.
We show that the benefits depend on a number of factors, includingdata size relative to
the cache size, data access patterns, the degree of "NUMAness" of the multiprocessor
system, and the extent to which data is reused. Programmers and compiler designers
must take these factors into consideration.
1 Introduction
Non-Uniform Memory Access (NUMA) multiprocessors have become widely available in the last few
years; examples include the KSR1/2 [8], the CRAY T3D [6], the Convex Exemplar [5], and Toronto's
Hector [9]. NUMA multiprocessors can scale to large numbers of processors while supporting shared
memory programming. Nonetheless, the non-uniform nature of memory accesses requires careful
management of data in shared memory to enhance data locality. Recent work [1, 2] has shown that

  

Source: Abdelrahman, Tarek S. - Department of Electrical and Computer Engineering, University of Toronto

 

Collections: Computer Technologies and Information Sciences