Jan Novotny

Jan Novotný received his PhD in Theoretical Physics and Astrophysics in 2017 at the Silesian University in Opava, Czech Republic. Currently, he is an associate professor at the Institute of Physics, Silesian University in Opava. Formerly he worked as a post-doctoral research associate at Oxford e-Research Centre at Department of Engineering Science, University of Oxford. There he worked in a team developing accelerated algorithms on GPUs for the SKA. His research interests include computational algorithms, high-performance computing and spherically symmetric relativistic stars with a polytropic equation of state.


Affiliation – Institute of Physics, Silesian University in Opava

Talks

Implementing CUDA Streams into AstroAccelerate – A Case Study

To be able to run tasks asynchronously on NVIDIA GPUs a programmer must explicitly implement asynchronous execution in their code using the syntax of CUDA streams. Streams allow a programmer to launch independent concurrent execution tasks, providing the ability to utilise different functional units on the GPU asynchronously. For example, it is possible to transfer the results from a previous computation performed on input data n-1, over the PCIe bus whilst computing the result for input data n, by placing different tasks in different CUDA streams. The benefit of such an approach is that the time taken for the data transfer between the host and device can be hidden with computation. This case study deals with the implementation of CUDA streams into AstroAccelerate. AstroAccelerate is a GPU accelerated real-time signal processing pipeline for time-domain radio astronomy.