《朱莉娅》中的沟通密集型表演
A Look at Communication-Intensive Performance in Julia
摘要：
Julia程序设计语言因其潜在的程序员生产力和在科学代码上令人印象深刻的性能而继续受到欢迎。因此，它具有大规模HPC的潜力，但我们尚未看到这种潜力完全实现。虽然Julia确实拥有大规模运行的机器，而其他人也已经为非常并行的工作负载这样做了，但我们还没有看到Julia在HPC领域中非常常见的通信密集型代码上的性能分析。在本文中，我们调查朱莉娅的性能在这方面，首先与一套微基准内，没有节点，然后使用第一个朱莉娅端口的标准，HPC基准代码，高性能共轭梯度（HPCG）。我们表明，如果程序员适当平衡的计算通信比，朱莉娅实际上可以在集群计算环境中优于C/MPI。
英文摘要：
The Julia programming language continues to gain popularity both for its potential for programmer productivity and for its impressive performance on scientific code. It thus holds potential for large-scale HPC, but we have not yet seen this potential fully realized. While Julia certainly has the machinery to run at scale, and while others have done so for embarrassingly parallel workloads, we have yet to see an analysis of Julia's performance on communication-intensive codes that are so common in the HPC domain. In this paper we investigate Julia's performance in this light, first with a suite of microbenchmarks within and without the node, and then using the first Julia port of a standard, HPC benchmarking code, high-performance conjugate gradient (HPCG). We show that if programmers properly balance the computation to communication ratio, Julia can actually outperform C/MPI in a cluster computing environment.
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