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.

中文翻译：

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针对多核系统优化UPC程序

统一并行C（UPC）的分区全局地址空间（PGAS）模型可以帮助用户在非统一内存访问（NUMA）多核共享内存系统上表达和管理应用程序数据局部性，以获得良好的性能。首先，我们通过示例和定量性能结果描述了几种对在NUMA多核计算机上实现良好性能至关重要的UPC程序优化技术。其次，我们使用两个数值计算内核，即并行矩阵-矩阵乘法和并行3-D FFT，来演示UPC应用程序的端到端开发和优化。我们的结果表明，优化的UPC程序在当前的多核系统上可以实现非常好的可扩展性能，甚至在某些情况下甚至可以胜过供应商优化的库。