GPU Acceleration : OpenACC for Radar Processing Simulation

International audienceThis article gives a methodological approach to accelerating an environment of a RADAR (RAdio Detecting And Ranging) simulation, from a single-core CPU implementation to a multi-core GPU implementation. We focus our attention on the most common tools for GPU programming like CUDA [2], but more specifically on OpenACC [6], a directive based parallel programming language. One of its promises is, with minimal modifications, to transform a CPU code to take advantage of many-core architectures, CPUs or GPUs alike. Radar systems rely on many layers of testing, one of them being software validation. As technology moves forward, systems become increasingly complex, thus increasing the required processing power to simulate those systems. With CPU performance stalling, it is imperative to switch to alternative architectures. Our contribution is providing key steps for accelerating a software simulation of a radar algorithm on a GPU, with a particular focus on performance but also on the ease of programming. Maximum achieved execution time speedup on GPU architecture for our typical use case of radar processing is of 8.2 for CUDA and of 4.56 for OpenACC compared to the reference implementation on CPU