Large scale modeling and simulation problems, from nanoscale materials to universe-scale cosmology, have in the past used the massive computing resources of High-Performance Computing (HPC) systems. Over the last decade, cloud computing has gained popularity for business applications and increasingly for computationally intensive machine learning problems. Despite the prolific literature, the question remains open whether cloud computing can provide HPC-competitive performance for a wide range of scientific applications. The answer to this question is crucial in guiding the design of future systems and providing access to high-performance resources to a broadened community. Here we present a multi-level approach to identifying the performance gap between HPC and cloud computing and to isolate several variables that contribute to this gap by dividing our experiments into (i) hardware and system microbenchmarks and (ii) user applications. Our results show that today's high-end cloud computing can deliver HPC-like performance - at least at modest scales - not only for computationally intensive applications, but also for memory- and communication-intensive applications, thanks to the high-speed memory systems and interconnects and dedicated batch scheduling now available on some cloud platforms.

中文翻译：

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10 年后：云计算正在缩小性能差距

大规模建模和模拟问题，从纳米级材料到宇宙级宇宙学，过去都使用了高性能计算 (HPC) 系统的大量计算资源。在过去的十年中，云计算在商业应用程序中越来越受欢迎，并且越来越多地用于计算密集型机器学习问题。尽管有大量文献，但云计算是否可以为广泛的科学应用提供具有 HPC 竞争力的性能仍然是一个悬而未决的问题。这个问题的答案对于指导未来系统的设计和向更广泛的社区提供对高性能资源的访问至关重要。在这里，我们提出了一种多层次的方法来识别 HPC 和云计算之间的性能差距，并通过将我们的实验分为 (i) 硬件和系统微基准测试以及 (ii) 用户应用程序来隔离导致这种差距的几个变量。我们的结果表明，当今的高端云计算可以提供类似 HPC 的性能——至少在适度的规模上——不仅适用于计算密集型应用程序，而且适用于内存和通信密集型应用程序，这要归功于高速内存系统和互连和专用批处理调度现在可在某些云平台上使用。