Symmetry and performance in consistency protocols
ICS '99 Proceedings of the 13th international conference on Supercomputing
Application scaling under shared virtual memory on a cluster of SMPs
ICS '99 Proceedings of the 13th international conference on Supercomputing
A high-level abstraction of shared accesses
ACM Transactions on Computer Systems (TOCS)
Improving fine-grained irregular shared-memory benchmarks by data reordering
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Optimizing Home-Based Software DSM Protocols
Cluster Computing
An Effective Logical Cache for a Clustered LRC-Based DSM System
Cluster Computing
Journal of Parallel and Distributed Computing
Homeless and home-based Lazy Release Consistency protocols on Distributed Shared Memory
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
A segment-based DSM supporting large shared object space
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
A locality-aware home migration for software distributed shared memory
Proceedings of the 2013 Research in Adaptive and Convergent Systems
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In this paper, we compare the performance of two multiple-writer protocols based on lazy release consistency. In particular, we compare the performance of Princeton's home-based protocol and TreadMarks' protocol on a 32-processor platform. We found that the performance difference between the two protocols was less than 4% for four out of seven applications. For the three applications on which performance differed by more than 4%, the TreadMarks protocol performed better for two because most of their data were migratory, while the home-based protocol performed better for one. For this one application, the explicit control over the location of data provided by the home-based protocol resulted in a better distribution of communication load across the processors. These results differ from those of a previous comparison of the two protocols. We attribute this difference to (1) a different ratio of memory to network bandwidth on our platform and (2) lazy diffing and request overlapping, two optimizations used by TreadMarks that were not used in the previous study.