Accelerating astronomical applications 2
FPGAs excel in performing simple operations on high-speed streaming data, at high (energy) efficiency. However, so far, their difficult programming model and poor floating-point support prevented a wideadoption for typical HPC applications. This is changing, due to recent and near-future FPGA technology developments: support for the high-level OpenCL programming language, hard floating-point units, andtight integration with (Xeon) CPU cores. Combined, these are game changers: they dramatically reduce development times and allow using FPGAs for applications that were previously deemed too complex.
Another technology advance, 3D XPoint memory, allows new ways to deal with large amounts of data. Together, these developments will have disruptive impact on tomorrow’s data centers, and blur bordersbetween embedded computing and HPC.
With support from Intel, we will explore these disruptive technologies in critical parts of radioastronomical processing pipelines, so that they can be applied in future and upgraded telescopes. Theseshould lead to shorter development times, more performance, higher energy efficiency, lower costs, lower risks, and eventually more astronomical science.
Reducing Energy Consumption in Radio-astronomical and Ultrasound Imaging Tools
Approaches for radio telescope system health management
An architecture for real Time big data processing for AMBER
Using point clouds to their full potential
An eScience infrastructure for Bayesian inverse modeling
Kernel Tuner greatly simplifies the development of highly-optimized and auto-tuned CUDA, OpenCL, and C code, supporting many advanced use-cases and optimization strategies that speed up the auto-tuning process.