A Novel Approach for Online-Monitoring for High Data-Rate Image-Based Instrumentation Suren Chilingaryan, Andreas Kopmann Forschungszentrum Karlsruhe, Germany Goal: Processing of large data sets Data set 10-50GB Speed up hours → < 1 minute Solution: Standard graphic adapter Universal programming (CUDA, OpenCL) Teraflop performance High bandwidth Benefit: Improve experiment quality Experiment control Tomographic reconstruction 3D Imaging applications produce large data sets. Several pictures from different angles or during manipulation are taken and the 3D object needs to be reconstructed from the image sequence. The size of the data sets is typically in the order of 10-50GB. Regular PCs need for this task several hours up to one day of computation time – this is too long for direct feed back (quality of the probe, control of the experiment). Even the transport of the data set takes too long too come to close real time results. Utilizing standard graphics adapters can provide a solution. Actual graphic adapters provide a multiple of the computation power of actual desktop CPUs. The adapter is connect with high bandwidth (PCI Express). And all manufactures offer general purpose libraries that allow to use the graphics adapters also for non graphics purposes. In order to have a general programming interface with OpenCL a new standard for GPU, DSP and CPU systems is developed. The first results for image processing applications show that quite large performance increase can be reached. It seems to be possible to come in the desired regions of realtime or near-realtime monitoring. Of course depending on the application and the resolution of the images. Further planned applications are online-simulation for partical physics. Here simulations are more and more needed in parallel with the experiments. Examples: Fieldline simulations for KATRIN neutrino experiment or cosmic ray simulations. + Graphic co-processors offer multi teraflops in a single PC KIT – The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe (TH) 1 1

Application: Strain Measurement Data Set: Number of Images: 11 (521) Resolution: 2208x3000 Total size: 200 MB (10GB) Results: CPU reconstruction: ~10 hours (16 cores) GPU reconstruction: 30 min Image corr / All Unoptimized 173 212 Software Optimization 97 140 CUDA/FFT plugin 60 104 FULL CUDA support 9 45 Parallel Memory Transfers 6 42 Parallel Computations 6 25 Nvidia Tesla S1070 2 12

Application: X-ray Tomography PyHST - High Speed Tomography Reconstruction ESRF (European Synchrotron Radiation Facility) Polygone Scientifique Louis Néel, 6 rue Jules Horowitz, 38000 GRENOBLE Data Set: Number of Images: 2000 Resolution: 1776x1707 Total size: 24 GB Results: CPU reconstruction: ~10 hours (16 cores) GPU reconstruction: 30 min Porose polyethylene grains in a conical plastic holder 3 | Dr. Andreas Kopmann | PNI Bonus Programm 27.3.2009