A Lightweight Approach to Performance Portability with targetDP

Abstract
Leading HPC systems achieve their status through use of highly parallel devices such as NVIDIA GPUs or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. I will describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. I will demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus a separate lattice QCD particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with MPI to allow use on systems containing multiple nodes: I will show scaling results on traditional and GPU-accelerated large scale supercomputers. I will also present preliminary performance results on new-generation NVIDIA Pascal and Intel KNL architectures.

About the speaker
Alan’s research career began in the area of theoretical physics: his Ph.D. thesis was awarded the UK-wide Ogden Prize in 2004 for the best thesis in particle physics phenomenology. He continued this work under a University Fellowship at The Ohio State University, before moving to EPCC in 2005. His current research focuses on the exploitation of GPUs to the benefit of real scientific and industrial applications: he has a particular interest in the programming of large-scale GPU-accelerated supercomputers, and also in the area of performance portability. He was awarded the status of CUDA Fellow in 2014. Alan leads EPCC’s GPU related activities, and is involved in management, teaching and supervision for the EPCC MSc in High Performance Computing.

www.oerc.ox.ac.uk/events/targetdp
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