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.
Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this question remains open . Answers are crucial for designing future systems and democratizing high-performance computing. We present a multi-level approach to investigate the performance gap between HPC and cloud computing , isolating different variables that contribute to this gap . Our experiments are divided into (i) hardware and system microbenchmarks and (ii) user application proxies. The results show that today's high-end cloud computing can deliver HPC-competitive performance not only for computationally intensive applications but also for memory- and communication-intensive applications - at least at modest scales - thanks to the high-speed memory systems and interconnects and dedicated batch scheduling now available on some cloud platforms.
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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
Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this
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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
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