Flow-Based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance
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Co-processors or accelerators like GPUs and FPGAs are becoming more and more popular. CMS' data processing framework (CMSSW) implements multi-threading using Intel TBB utilizing tasks as concurrent units of work. We have developed generic mechanisms within the CMSSW framework to interact effectively with non-CPU resources and configure CPU and non-CPU algorithms in a unified way. As a first step to gain experience, we have explored mechanisms for how algorithms could offload work to NVIDIA GPUs with CUDA.
Co-processors or accelerators like GPUs and FPGAs are becoming more and more popular. CMS’ data processing framework (CMSSW) implements multi-threading using Intel TBB utilizing tasks as concurrent units of work. We have developed generic mechanisms within the CMSSW framework to interact effectively with non-CPU resources and configure CPU and non-CPU algorithms in a unified way. As a first step to gain experience, we have explored mechanisms for how algorithms could offload work to NVIDIA GPUs with CUDA.
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The GstLAL library, derived from Gstreamer and the LIGO Algorithm Library, supports a stream-based approach to gravitational-wave data processing. Although GstLAL was primarily designed to search for gravitational-wave signatures of merging black holes and neutron stars, it has also contributed to other gravitational-wave searches, data calibration, and detector-characterization efforts. GstLAL has played an integral role in all of the LIGO-Virgo collaboration detections, and its low-latency configuration has enabled rapid electromagnetic follow-up for dozens of compact binary candidates.
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