Efficient channelization on a Graphics Processing Unit

We present an implementation of a channelizer (F-engine) running on a Graphics Processing Unit (GPU). While not the first GPU implementation of a channelizer, we have put significant effort into optimizing the implementation. We are able to process four antennas each with 2 Gsample/s, 10-bit dual-polarized input and 8-bit output, on a single commodity GPU. This fully utilizes the available PCIe bandwidth of the GPU. The system is not as optimized for a single high-bandwidth antenna, but handles 6.2 Gsample/s, limited by single-core CPU performance.Comment: Submitted to The Journal of Astronomical Telescopes, Instruments, and System

The AARTFAAC All-Sky Monitor: System Design and Implementation

The Amsterdam-ASTRON Radio Transients Facility And Analysis Center (AARTFAAC) all sky monitor is a sensitive, real time transient detector based on the Low Frequency Array (LOFAR). It generates images of the low frequency radio sky with spatial resolution of 10s of arcmin, MHz bandwidths, and a time cadence of a few seconds, while simultaneously but independently observing with LOFAR. The image timeseries is then monitored for short and bright radio transients. On detection of a transient, a low latency trigger will be generated for LOFAR, which can interrupt its schedule to carry out follow-up observations of the trigger location at high sensitivity and resolutions. In this paper, we describe our heterogeneous, hierarchical design to manage the 240 Gbps raw data rate, and large scale computing to produce real-time images with minimum latency. We discuss the implementation of the instrumentation, its performance, and scalability.Comment: Submitted to Journal of Astronomical Instrumentation, Special issue on 'Digital Signal Processing (DSP) in Radio Astronomy

Radio-Astronomical Imaging on Accelerators

Imaging is considered the most compute-intensive and therefore most challenging part of a radio-astronomical data-processing pipeline. To reach the high dynamic ranges imposed by the high sensitivity and large field of view of the new generation of radio telescopes such as the Square Kilometre Array (SKA), we need to be able to correct for direction-independent effects (DIEs) such as the curvature of the earth as well as for direction-dependent time-varying effects (DDEs) such as those caused by the ionosphere during imaging. The novel Image-Domain gridding (IDG) algorithm was designed to avoid the performance bottlenecks of traditional imaging algorithms. We implement, optimize, and analyze the performance and energy efficiency of IDG on a variety of hardware platforms to find which platform is the best for IDG. We analyze traditional CPUs, as well as several accelerators architectures. IDG alleviates the limitations of traditional imaging algorithms while it enables the advantages of GPU acceleration: better performance at lower power consumption. The hardware-software co-design has resulted in a highly efficient imager. This makes IDG on GPUs an ideal candidate for meeting the computational and energy efficiency constraints of the SKA. IDG has been integrated with a widely-used astronomical imager (WSClean) and is now being used in production by a variety of different radio observatories such as LOFAR and the MWA. It is not only faster and more energy-efficient than its competitors, but it also produces better quality images