Abstract
Tracking detectors in high-energy physics experiments produce hundreds of megabytes of data at a rate of several hundred Hz. Processing this data at a bandwidth of 10-20 Gbyte/sec requires parallel computing. Reducing the huge data rate to a manageable amount by realtime data compression and pattern recognition techniques is the prime task. Clustered SMP (Symmetric Multi-Processor) nodes, based on off-the-shelf PCs and connected by a high bandwidth, low latency network, provide the necessary computing power. Such a system can easily be interfaced to the front-end electronics of the detectors via the internal PCI-bus. Data compression techniques like vector quantization and data modeling and fast transformations like conformal mapping or the adaptive, generalized Hough-transform for feature extraction are the methods of choice.
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Adler, C. et al. (2001). From the Big Bang to Massive Data Flow: Parallel Computing in High Energy Physics Experiments. In: Sørevik, T., Manne, F., Gebremedhin, A.H., Moe, R. (eds) Applied Parallel Computing. New Paradigms for HPC in Industry and Academia. PARA 2000. Lecture Notes in Computer Science, vol 1947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-70734-4_39
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DOI: https://doi.org/10.1007/3-540-70734-4_39
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