- Title: “Adaptation of the Fast Fourier Transform processing on hybride integrated CPU/GPU architecture”
- When: October 2, 2015 — 10:30
- Where: Inria Sophia Antipolis, salle Euler Violet
- Olivier Sentieys, IRISA, Rennes, France
- Albert Cohen (Referee), Inria / ENS, Paris, France
- Jean-François Méhaut (Referee) UJF / Inria, Grenoble, France
- Robert De Simone (co-supervisor), AOSTE, Inria/I3S-UNS, France
- Serge Tissot (co-supervisor), KONTRON, Toulon, France
- Michel Syska(co-supervisor), COATI, Inria/I3S-UNS, France
Abstract: Multicore architectures Intel Core (IvyBridge, Haswell, etc.) contain both general purpose CPU cores (4) and dedicated GPU cores embedded on the same chip (16 and 40 respectively). As part of the activity of Kontron (the company partially funding this CIFRE scholarship), an important objective is to efficiently compute arrays and sequences of fast Fourier transforms (FFT) such as one finds in radar applications, on this architecture. While native (but proprietary) libraries exist for Intel CPU, nothing is currently available for the GPU part.
The aim of the thesis was to define the efficient placement of FFT modules, and to study theoretically the optimal form for grouping computing stages of such FFT according to data locality on a single computing core. This choice should allow processing efficiency, by adjusting the memory size available to the required application data size.
Then the multiplicity of cores is exploitable to compute several FFT in parallel, without interference (except for possible bus contention between the CPU and the GPU). We have achieved significant results, both in the implementation of an FFT (1024 points) on a SIMD CPU core, expressed in C, and in the implementation of a FFT of the same size on a GPU SIMT core, then expressed in OpenCL.
In addition, our results allow to define rules to automatically synthesize such solutions, based solely on the size of the FFT (more specifically its number of stages), and the size of the local memory for a given computing core. The performances obtained are better than the native Intel library for CPU, and demonstrate a significant gain in consumption on GPU. All these points are detailed in the thesis document.
These results should lead to exploitation of the code as library by the Kontron company.
Keywords: FFT, GPU, multicores.