Enabling a High Throughput Real Time Data Pipeline for a Large Radio Telescope Array with GPUs

TL;DR

This paper presents a scalable GPU-based data processing pipeline enabling real-time analysis of the high-throughput data stream from the Murchison Widefield Array radio telescope, supporting its scientific goals.

Contribution

It introduces a heterogeneous GPU-based architecture for real-time data reduction, achieving high performance and scalability for large radio telescope arrays.

Findings

Achieved real-time processing at 2.5 TFLOP/s performance

Developed a highly parallel, scalable GPU pipeline

Supported continuous 8s data batches without delay

Abstract

The Murchison Widefield Array (MWA) is a next-generation radio telescope currently under construction in the remote Western Australia Outback. Raw data will be generated continuously at 5GiB/s, grouped into 8s cadences. This high throughput motivates the development of on-site, real time processing and reduction in preference to archiving, transport and off-line processing. Each batch of 8s data must be completely reduced before the next batch arrives. Maintaining real time operation will require a sustained performance of around 2.5TFLOP/s (including convolutions, FFTs, interpolations and matrix multiplications). We describe a scalable heterogeneous computing pipeline implementation, exploiting both the high computing density and FLOP-per-Watt ratio of modern GPUs. The architecture is highly parallel within and across nodes, with all major processing elements performed by GPUs. Necessary scatter-gather operations along the pipeline are loosely synchronized between the nodes hosting the GPUs. The MWA will be a frontier scientific instrument and a pathfinder for planned peta- and exascale facilities.