What’s New in HPC Research: Transfer Learning, Radio Astronomy, FPGAs and More

In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here.

Autotuning exascale applications with multitask and transfer learning

Autotuning (where operators automatically find optimal performance parameters for an application) is critical as computing scales up. In this paper, the researchers discuss the use of machine learning – specifically, multitask and transfer learning – to enhance this process. They compare their results to state-of-the-art autotuning methods, finding a 1.5x improvement compared to some popular approaches. The researchers highlight how their approach might be preferable for applications generally and for exascale applications in particular.

Authors: Wissam M. Sid-Lakhdar, Mohsen Mahmoudi Aznaveh, Xiaoye S. Li and James W. Demmel

Comparing FPGAs and GPUs for radio-astronomical imaging

FPGAs have been gaining traction due to their benefits for energy efficiency in simple operations with high-speed data. These researchers – a duo from the Netherlands Institute for Radio Astronomy – demonstrate implementation and optimization of a radio-astronomical imaging application on an Arria 10 FPGA. Comparing their results to GPU and CPU implementations, they find that optimizing for a high clock speed on the FPGA is difficult, but argue that recent developments in FPGAs have made them viable accelerators for complex HPC applications.

Authors: Bram Veenboer and John W. Romein

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