Hu YC, Cox A, Zwaenepoel W. Improving fine-grained irregular shared-memory benchmarks by data reordering. Proceedings of the IEEE/ACM Supercomputing Conference (SC), Dallas, TX, November 2000. IEEE Computer Society Press: Los Alamitos, CA, 2000. Home/Search Document Details and Download
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ARS: an adaptive runtime system for locality optimization - Tao, Schulz, Karl (2003) (Correct)
....work Data locality on NUMA machines has been addressed over the last years. A significant amount of approaches has therefore been proposed for improving data locality through reducing accesses to remote memories. In addition to those schemes focusing on static data reordering based on compilers [3,6,11,18], several approaches based on page migration have been implemented. This section briefly describes a few of such approaches. In addition, a few approaches for thread migration have to be mentioned, as their techniques can in principle be applied to page migration as well. Verghese et al. 22] ....
Y.C. Hu, A. Cox, W. Zwaenepoel, Improving fine-grained irregular shared-memory benchmarks by data reordering, in: Proceedings of the SC2000.
Exploiting Locality in the Run-Time Parallelization.. - Martín, Singh.. (2002) (Correct)
....the parallel execution of a loop with no output dependences. Their strategy is based on array reordering to improve spatial locality. A recent proposal also based on data reordering using space filling curves to enhance spatial locality of irregular codes on shared memory systems can be found in [8]. Our LCYT proposals, in contrast, are based on loop restructuring and their primary objective is to exploit both spatial and temporal locality, as well as avoid false sharing of data. Moreover, our strategies can be applied to any loop that follows the general pattern represented in Figure 1. 6. ....
Y. C. Hu, A. L. Cox, and W. Zwaenepoel. Improving FineGrained Irregular Shared-Memory Benchmarks by Data Reordering. In Supercomputing Conference, Dallas, TX, 2000.
Improving Memory Hierarchy Performance for Irregular .. - Mellor-Crummey.. (2001) (6 citations) (Correct)
....techniques are more broadly applicable. Our colleagues have recently also applied space filling curve based reorderings to improve the parallel efficiency of shared memory and software distributed shared memory computations by improving data locality, which reduces communication and false sharing [36, 37]. Our experiences show that good data and computation orders can be achieved for irregular problems using dynamic reorderings, and that the gain in locality from using good data and computation orders can be dramatic. 7. Acknowledgements Discussions with Vikram Adve and Rob Fowler helped shape ....
Y. C. Hu, A. Cox, and W. Zwaenepoel, "Improving Fine-Grained Irregular Shared-Memory Benchmarks by Data Reordering," Proceedings Supercomputing 2000.
Locality Optimizations For Adaptive Irregular Scientific Codes - Han, Tseng (2000) (2 citations) (Correct)
....reapply transformations for adaptive codes. Mellor Crummey et al. manually applied a geometric partitioning algorithm based on space filling curves to map multidimensional data to memory, showing large improvements in performance [14] Hu et al. show similar benefits for parallel irregular codes [10]. In comparison, we automate our optimizations in a compiler and run time library, and also use graph based partitioning techniques. Mitchell et al. improved locality using bucket sorting to reorder loop iterations in irregular computations [15] They improved the performance of two NAS ....
Y. Hu, A. Cox, and W. Zwaenepoel. Improving fine-grained irregular shared-memory benchmarks by data reordering. In Proceedings of SC'00, Dallas, TX, November 2000.
Locality Optimizations For Adaptive Irregular Scientific Codes - Han, Tseng (2000) (2 citations) (Correct)
....reapply transformations for adaptive codes. Mellor Crummey et al. manually applied a geometric partitioning algorithm based on space filling curves to map multidimensional data to memory, showing large improvements in performance [14] Hu et al. show similar benefits for parallel irregular codes [10]. In comparison, we automate our optimizations in a compiler and run time library, and also use graph based partitioning techniques. Mitchell et al. improved locality using bucket sorting to reorder loop iterations in irregular computations [15] They improved the performance of two NAS ....
Y. Hu, A. Cox, and W. Zwaenepoel. Improving fine-grained irregular shared-memory benchmarks by data reordering. In Proceedings of SC'00, Dallas, TX, November 2000.
Loosely Coordinated Coscheduling In The Context Of . . . - Sodan (2005) (Correct)
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Hu YC, Cox A, Zwaenepoel W. Improving fine-grained irregular shared-memory benchmarks by data reordering. Proceedings of the IEEE/ACM Supercomputing Conference (SC), Dallas, TX, November 2000. IEEE Computer Society Press: Los Alamitos, CA, 2000.
Scientific Computing Research Environments for the.. - Heinkenschloss.. (2001) (Correct)
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Y. C. Hu, A. Cox, and W. Zwaenepoel. Improving fine-grained irregular shared-memory benchmarks by data reordering. In Proceedings of SC 2000.
Data Locality Optimization of Shared Memory Programs on NUMA.. - Tao (Correct)
No context found.
Y. C. Hu, A. Cox, and W. Zwaenepoel. Improving Fine-Grained Irregular SharedMemory Benchmarks by Data Reordering. In Proceedings of SC2000.
Optimization Techniques for Parallel Codes of Irregular.. - Guo, Chang, Pan (2003) (Correct)
No context found.
) Hu, Y., Cox, A. and Zwaenepoel, W.: Improving fine-grained irregular shared-memory benchmarks by data reordering, Proc. SC'00, Dallas, TX (Nov. 2000).
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