Data Provenance and Management in Radio Astronomy: A Stream Computing Approach

TL;DR

This paper explores a stream-computing approach using IBM InfoSphere Streams and hardware accelerators to improve data provenance and management in large-scale radio astronomy projects like the Square Kilometer Array.

Contribution

It demonstrates the viability of stream computing and accelerators for managing large, real-time radio astronomy data pipelines, with a case study on autocorrelating spectrometers.

Findings

Effective real-time data management with InfoSphere Streams

Advantages of stream computing over traditional methods

Successful implementation of an autocorrelating spectrometer

Abstract

New approaches for data provenance and data management (DPDM) are required for mega science projects like the Square Kilometer Array, characterized by extremely large data volume and intense data rates, therefore demanding innovative and highly efficient computational paradigms. In this context, we explore a stream-computing approach with the emphasis on the use of accelerators. In particular, we make use of a new generation of high performance stream-based parallelization middleware known as InfoSphere Streams. Its viability for managing and ensuring interoperability and integrity of signal processing data pipelines is demonstrated in radio astronomy. IBM InfoSphere Streams embraces the stream-computing paradigm. It is a shift from conventional data mining techniques (involving analysis of existing data from databases) towards real-time analytic processing. We discuss using InfoSphere Streams for effective DPDM in radio astronomy and propose a way in which InfoSphere Streams can be utilized for large antennae arrays. We present a case-study: the InfoSphere Streams implementation of an autocorrelating spectrometer, and using this example we discuss the advantages of the stream-computing approach and the utilization of hardware accelerators.