Real-Time Dedispersion for Fast Radio Transient Surveys, using Auto Tuning on Many-Core Accelerators

TL;DR

This paper explores optimizing real-time dedispersion in radio transient surveys using auto-tuning on various many-core accelerators, addressing performance challenges due to memory-bound nature.

Contribution

It introduces an auto-tuning approach for dedispersion algorithms tailored to different hardware and observational setups, enhancing efficiency in radio transient surveys.

Findings

Dedispersion is inherently memory-bound, limiting performance.

Auto-tuning significantly improves dedispersion performance across hardware.

Optimal settings vary between observational configurations.

Abstract

Dedispersion, the removal of deleterious smearing of impulsive signals by the interstellar matter, is one of the most intensive processing steps in any radio survey for pulsars and fast transients. We here present a study of the parallelization of this algorithm on many-core accelerators, including GPUs from AMD and NVIDIA, and the Intel Xeon Phi. We find that dedispersion is inherently memory-bound. Even in a perfect scenario, hardware limitations keep the arithmetic intensity low, thus limiting performance. We next exploit auto-tuning to adapt dedispersion to different accelerators, observations, and even telescopes. We demonstrate that the optimal settings differ between observational setups, and that auto-tuning significantly improves performance. This impacts time-domain surveys from Apertif to SKA.