• Scientists and engineers who design and build unique scientific research facilities face similar challenges. • These include managing massive data rates that exceed current computational infrastructure capacity to extract scientific insights and driving the experiments in real time. • These challenges are obstacles to maximizing the impact of scientific discoveries and significantly slow the pace of knowledge growth. • Scientists and engineers at NVIDIA work with these facilities to develop new solutions built on parallel and distributed computation that remove these blockers. • This post will walk through two notable examples of formalizing complex physics problems into tractable mathematical puzzles that benefit greatly from GPU-accelerated scientific computing, involving the U.S. • Department of Energy: NSF-DOE Vera C.
Article Summaries:
- NVIDIA’s accelerated‑computing team has partnered with two major U.S. research facilities- the Vera C. Rubin Observatory and SLAC’s Linac Coherent Light Source II (LCLS‑II)-to deliver real‑time experiment steering. By applying GPU‑accelerated Python libraries CuPy and cuPyNumeric, NVIDIA’s Accelerated Space and Time Image Analysis (ASTIA) and X‑ray Analysis for Nanoscale Imaging (XANI) pipelines process the observatory’s 20 TB nightly sky surveys and LCLS‑II’s up to 1 million X‑ray bursts per second. The new approach cuts data‑analysis time from months to hours, enabling live feedback that was previously impossible and accelerating discoveries in astrophysics and ultrafast X‑ray science.
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