[EuroPython 2011] Yann Le Du - 20 June 2011 in "Track Lasagne"
In Electron Paramagnetic Resonance Imaging, we are faced with a deconvolution problem that has a strong impact on the image actually reconstructed. Faced with the need of mapping the distribution of organic matter in Terrestrial and Martian rock samples for applications in exobiology, we needed to see how to extract a maximum amount of information from our data: our approach uses reservoir computing artificial neural networks coupled to a particle swarm algorithm that evolves the reservoirs’ weights.
The code runs on the Hybrid Processing Units for Science (HPU4Science) cluster located at the Laboratoire de Chimie de la Matière Condensée de Paris (LCMCP). The cluster is composed of a central data storage machine and a heterogeneous ensemble of 6 decentralized nodes. Each node is equipped with a Core2 Quad or i7 CPU and 3-7 NVIDIA Graphical Processing Units (GPUs) including the GF110 series. Each of the 28 GPUs independently explores a different parameter space sphere of the same problem. Our application shows a sustained real performance of 15.6 TFLOPS. The HPU4Science cluster cost 36, 090resultingina432.3MFLOPS ⁄ cost performance.
That talk is meant to demonstrate on a practical case how consumer grade computer hardware coupled to a very popular computer language can be used to tackle a difficult yet very elementary scientific problem: how do you go from formulating the problem, to choosing the right hardware and software, and all the way to programming the algorithms using the appropriate development tools and methodologies (notably Literate Programming). On the math side, the talk requires a basic understanding of matrix algebra and of the discretization process involved when computing integrals.