Rob V van Nieuwpoort | Universiteit Leiden (original) (raw)

Rob V van Nieuwpoort

Uploads

Papers by Rob V van Nieuwpoort

We describe the design and implementation of an extremely scalable real-time RFI mitigation metho... more We describe the design and implementation of an extremely scalable real-time RFI mitigation method, based on the of-fline AOFlagger. All algorithms scale linearly in the number of samples. We describe how we implemented the flagger in the LOFAR real-time pipeline, on both CPUs and GPUs. Additionally , we introduce a novel simple history-based flagger that helps reduce the impact of our small window on the data. By examining an observation of a known pulsar, we demonstrate that our flagger can achieve much higher quality than a simple thresholder, even when running in real time, on a distributed system. The flagger works on visibility data, but also on raw voltages, and beam formed data. The algorithms are scale-invariant, and work on microsecond to second time scales. We are currently implementing a prototype for the time domain pipeline of the SKA central signal processor.

Astronomy & Astrophysics, 2013

ABSTRACT

Concurrency: Practice and Experience, 2000

Proceedings of the seventh ACM SIGPLAN symposium on Principles and practice of parallel programming - PPoPP '99, 1999

Safety and Security Engineering V, 2013

Concurrency and Computation: Practice and Experience, 2015

ABSTRACT Many-core hardware is targeted specifically at obtaining high performance, but reaching ... more ABSTRACT Many-core hardware is targeted specifically at obtaining high performance, but reaching high performance is often challenging because hardware-specific details have to be taken into account. Although there are many programming systems that try to alleviate many-core programming, some providing a high-level language, others providing a low-level language for control, none of these systems have a clear and systematic methodology as a foundation. In this article, we propose stepwise-refinement for performance: a novel, clear, and structured methodology for obtaining high performance on many-cores. We present a system that supports this methodology, offers multiple levels of abstraction to provide programmers a trade-off between high-level and low-level programming, and provides programmers detailed performance feedback. We evaluate our methodology with several widely varying compute kernels on two different many-core architectures: a Graphical Processing Unit (GPU) and the Xeon Phi. We show that our methodology gives insight in the performance, and that in almost all cases, we gain a substantial performance improvement using our methodology. Copyright © 2015 John Wiley & Sons, Ltd.