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Spack, a software package management tool, was recognized with an R&D 100 Award in November. Developed for high-performance computing applications, Spack is used as an end-to-end development tool within the high-energy physics community. Fermilab is a contributor to Spack, whose initial development was led by Lawrence Livermore National Laboratory.
From Inside HPC, Sept. 15, 2019: Argonne and the National Center for Supercomputing Applications use deep learning to analyze Dark Energy Survey data.
From DOE, Sept. 10, 2019: Fermilab scientist Bo Jayatilaka is quoted in this article on ESnet, which has expanded to connect more than 40 major research institutions at speeds 15,000 times faster than a home network.
From MIT News, Aug. 19, 2019: A new prototype machine-learning technology co-developed by Fermilab and MIT scientists speeds Large Hadron Collider data processing by up to 175 times over traditional methods.
A new machine learning technology tested by Fermilab scientists and collaborators can spot specific particle signatures among an ocean of LHC data in the blink of an eye, much faster than standard methods. Sophisticated and swift, its performance gives a glimpse into the game-changing role machine learning will play in making future discoveries in particle physics as data sets get bigger and more complex.
Tran’s $2.5 million award will fund the development of new ways to handle the massive amounts of data that particle physics produces and to expand investigations of Higgs boson physics.
A pioneer in particle physics and high-performance computing, Fermilab has launched HEPCloud, a cloud computing service that will enable the lab’s demanding experiments to make the best, most efficient use of computing resources. This flagship project lets experiments rent computing resources from external sources during peak demand, reducing the costs of providing for local resources while also providing failsafe redundancy.
From Inside HPC, July 3, 2019: Particle physics researchers are using custom integrated circuits called FPGAs in combination with other computing resources to process massive quantities of data at extremely fast rates to find clues to the origins of the universe. This requires filtering sensor data in real time to identify novel particle substructures that could contain evidence of the existence of dark matter and other physical phenomena. A growing team of physicists and engineers from Fermilab, CERN and other institutions, co-led by Fermilab scientist Nhan Tran, wanted to have a flexible way to optimize custom-event filters in the CMS detector they are working on at CERN.
From Exascale Computing Project, May 28, 2019: Fermilab scientist Andreas Kronfeld is featured in this piece on the Excascale Computing Project, quantum chromodynamics and lattice QCD. Kronfeld, the principal investigator of ECP’s LatticeQCD project, explains how exascale computing will be essential to extending the work of precision calculations in particle physics to nuclear physics. The calculations are central for interpreting all experiments in particle physics and nuclear physics.
From HPCWire, May 7, 2019: Fermilab scientist Andreas Kronfeld is quoted in this article that follows up on Oak Ridge National Laboratory’s announcement of plans to build the $600 million, 1.5 exaflops Frontier supercomputer.