The Square Kilometre Array (SKA) radio telescope will be a massive scientific instrument entering service in the late 2020’s. The conversion of its antenna signals to images and the detection of transient phenomena is a massive computational undertaking, requiring 200PB/sec of memory bandwidth, all dedicated to array processing. In this talk we will give an overview of the data processing in the telescope and the process that has been followed to design suitable algorithms and systems. We will highlight parts of the challenge that have interesting relationships to computer science, and then transition to review recent technological developments such as memory, machine learning accelerators, and new floating point formats that may prove helpful.

Bio: Peter Braam is a scientist and entrepreneur focused on problems in large scale computing. Originally trained as a mathematician, he has worked at several academic institutions including Oxford, CMU and Cambridge. One of his startup companies developed the widely used Lustre file system. During the last few years he has focused on computing for the SKA telescope and on research in data intensive computing.