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With the data doubling every year, data intensive applications are increasing as well as the demand of high-end resource capacity to analyse collected data sets. The explosion of analysis applications have become a major driver for revising system architecture and tools leading to the proliferation of software components and frameworks which may require multi-node and multi-core systems to scale-up and provide good performance. In this context, Machine learning and Deep learning are steadily proving to be successful methods for a variety of use cases, and their popularity has resulted in numerous open-source software tools becoming accessible to the public and popular across different scientific disciplines. But with the growth of applications and tools, it is becoming difficult for researchers to estimate how much resource is needed to run their analyses and select appropriate software and hardware components. The goal of this paper is to present the results of a preliminary comparative study of state-of-the-art machine and deep learning tools and benchmark them on Cineca HPC systems. The comparison has been done taking in consideration different factors including the impossibility to benchmark all tools available on the market, the existence of tools supporting hardware accelerators, such as GPU, and the availability of precedent studies [1,2]. Our preliminary results show that tested tools are able to leverage underneath system capabilities to achieve significant performance and that no single software exists that outperforms others opening space to further optimisation.

A Performance Study of Machine and Deep Learning Frameworks on Cineca HPC Systems