Data and program restructuring of irregular applications for cache-coherent multiprocessor
ICS '94 Proceedings of the 8th international conference on Supercomputing
Journal of Parallel and Distributed Computing - Special issue on scalability of parallel algorithms and architectures
Object-oriented parallel computation for plasma simulation
Communications of the ACM - Special issue on object-oriented experiences and future trends
Advanced Computer Architecture: Parallelism,Scalability,Programmability
Advanced Computer Architecture: Parallelism,Scalability,Programmability
Plasma Physics Via Computer
Distributed Memory Compiler Design For Sparse Problems
IEEE Transactions on Computers
Runtime Support and Compilation Methods for User-Specified Irregular Data Distributions
IEEE Transactions on Parallel and Distributed Systems
Run-Time Optimization of Sparse Matrix-Vector Multiplication on SIMD Machines
PARLE '94 Proceedings of the 6th International PARLE Conference on Parallel Architectures and Languages Europe
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We introduce a method for improving the cache performance of irregular computations in which data are referenced through run-time defined indirection arrays. Such computations often arise in scientific problems. The presented method, called Run-Time Reference Clustering (RTRC), is a run-time analog of a compile-time blocking used for dense matrix problems. RTRC uses the data partitioning and re-mapping techniques that are a part of distributed memory multi-processor codes designed to minimize interprocessor communication. Re-mapping each set of local data decreases cache-misses the same way re-mapping the global data decreases off-processor references. We demonstrate the applicability and performance of the RTRC technique on several prevalent applications: Sparse Matrix-Vector Multiply, Particle-In-Cell, and CHARMM-like codes. Performance results on SPARC-20, SP-2, and T3-D processors show that single node execution performance can be improved by as much as 35%.