Adaptive granularity: Transparent integration of fine- and coarse-grain communication |
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Authors: | Daeyeon Park Rafael H Saavedra Sungdo Moon |
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Affiliation: | (1) Department of Control and Instrumentation Engineering, Hankuk University of Foreign Studies, Yongin, 449-791 Kyoungkido, Republic of Korea;(2) Department of Computer Science, SAL-300, University of Southern California, 90089-0781 Los Angeles, California |
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Abstract: | The granularity of shared data is one of the key factors affecting the performance of distributed shared memory machines (DSM).
Given that programs exhibit quite different sharing patterns, providing only one or two fixed granularities cannot result
in an efficient use of resources. On the other hand, supporting arbitrarily granularity sizes significantly increases not
only hardware complexity but software overhead as well. Furthermore. the efficient use of arbitrarily granularities put the
burden on users to provide information about program behavior to compilers and/or runtime systems. These kind of requirements
tend to restrict the programmability of the shared memory model. In this paper, we present a new communication scheme, calledAdaptive Granularity (AG). Adaptive Granularity makes it possible to transparently integrate bulk transfer into the shared memory model by supporting
variable-size granularity and memory replication. It consists of two protocols: one for small data and another for large data.
For small size data, the standard hardware DSM protocol is used and the granularity is fixed to the size of a cache line.
For large array data, the protocol for bulk data is used instead, and the granularity varies depending on the runtime sharing
behavior of the applications. Simulation results show that AG improves performance up to 43% over the hardware implementation
of DSM (e.g., DASH, Alewife). Compared with an equivalent architecture that supports fine-grain memory replication at the
fixed granularity of a cache line (e.g., Typhoon), AG reduces execution time up to 35%.
This research was supported in part by NSF under grant CCR-9308981 by ARPA under Rome Laboratories Contract F30602-91-C-0146,
and by the USC Zumberge Fund. Computing resources were provided in part by NSF infrastructure grant CDA-9216321. |
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Keywords: | Adaptive granularity distributed shared-memory multiprocessor bulk data transfer memory replication |
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