Shared memory programming for large scale machines
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
How to Make a Multiprocessor Computer That Correctly Executes Multiprocess Programs
IEEE Transactions on Computers
Scaling communication-intensive applications on BlueGene/P using one-sided communication and overlap
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
A Parallel Numerical Library for UPC
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
Optimizing collective communication on multicores
HotPar'09 Proceedings of the First USENIX conference on Hot topics in parallelism
Automatically tuning collective communication for one-sided programming models
Automatically tuning collective communication for one-sided programming models
Hybrid PGAS runtime support for multicore nodes
Proceedings of the Fourth Conference on Partitioned Global Address Space Programming Model
UPCBLAS: a library for parallel matrix computations in Unified Parallel C
Concurrency and Computation: Practice & Experience
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The Partitioned Global Address Space (PGAS) model of Unified Parallel C (UPC) can help users express and manage application data locality on non-uniform memory access (NUMA) multi-core shared-memory systems to get good performance. First, we describe several UPC program optimization techniques that are important to achieving good performance on NUMA multi-core computers with examples and quantitative performance results. Second, we use two numerical computing kernels, parallel matrix-matrix multiplication and parallel 3-D FFT, to demonstrate the end-to-end development and optimization for UPC applications. Our results show that the optimized UPC programs achieve very good and scalable performance on current multi-core systems and can even outperform vendor-optimized libraries in some cases.