Accelerating incoherent dedispersion

TL;DR

This paper analyzes and implements GPU-accelerated incoherent dedispersion algorithms, achieving significant speed-ups over CPU versions, enabling real-time processing for pulsar and transient astronomy surveys.

Contribution

It provides a detailed analysis and GPU implementations of direct, tree, and sub-band dedispersion algorithms, demonstrating their efficiency and practical applicability.

Findings

GPU implementation achieves 9x speed-up over CPU for direct algorithm.

Real-time processing is feasible with current hardware for typical survey parameters.

Tree and sub-band algorithms offer additional speed-ups with trade-offs.

Abstract

Incoherent dedispersion is a computationally intensive problem that appears frequently in pulsar and transient astronomy. For current and future transient pipelines, dedispersion can dominate the total execution time, meaning its computational speed acts as a constraint on the quality and quantity of science results. It is thus critical that the algorithm be able to take advantage of trends in commodity computing hardware. With this goal in mind, we present analysis of the 'direct', 'tree' and 'sub-band' dedispersion algorithms with respect to their potential for efficient execution on modern graphics processing units (GPUs). We find all three to be excellent candidates, and proceed to describe implementations in C for CUDA using insight gained from the analysis. Using recent CPU and GPU hardware, the transition to the GPU provides a speed-up of 9x for the direct algorithm when compared to an optimised quad-core CPU code. For realistic recent survey parameters, these speeds are high enough that further optimisation is unnecessary to achieve real-time processing. Where further speed-ups are desirable, we find that the tree and sub-band algorithms are able to provide 3-7x better performance at the cost of certain smearing, memory consumption and development time trade-offs. We finish with a discussion of the implications of these results for future transient surveys. Our GPU dedispersion code is publicly available as a C library at: http://dedisp.googlecode.com/