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Computation and Data Partitioning on Scalable Shared Memory Multiprocessors
 

Summary: Computation and Data Partitioning on Scalable
Shared Memory Multiprocessors
Sudarsan Tandri and Tarek S. Abdelrahman
Department of Electrical and Computer Engineering
The University of Toronto
Toronto, Ontario, Canada, M5S 1A4
e-mail: ftandri,tsag@eecg.toronto.edu
Abstract
In this paper we identify the factors that affect the derivation of com-
putation and data partitions on scalable shared memory multiprocessors
(SSMMs). We show that these factors necessitate an SSMM-conscious
approach. In addition to remote memory access, which is the sole factor
on distributed memory multiprocessors, cache affinity, memory con-
tention and false sharing are important factors that must be considered.
Experimental evidence is presented to demonstrate the impact of these
factors on performance using three applications on the KSR1 and the
Hector multiprocessors.
1 Introduction
Scalable shared memory multiprocessors (SSMMs) are becoming increasingly popular and a
viable alternative to distributed memory multiprocessors (DMMs). The Stanford DASH [20],

  

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

 

Collections: Computer Technologies and Information Sciences