首页|Reports Summarize Machine Learning Research from Brookhaven National Laboratory (Distributed Machine Learning Workflow with PanDA and iDDS in LHC ATLAS)
Reports Summarize Machine Learning Research from Brookhaven National Laboratory (Distributed Machine Learning Workflow with PanDA and iDDS in LHC ATLAS)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting originating from the Brookhav en National Laboratory by NewsRx correspondents, research stated, “Machine Learn ing (ML) has become one of the important tools for High Energy Physics analysis. ” Our news editors obtained a quote from the research from Brookhaven National Lab oratory: “As the size of the dataset increases at the Large Hadron Collider (LHC ), and at the same time the search spaces become bigger and bigger in order to e xploit the physics potentials, more and more computing resources are required fo r processing these ML tasks. In addition, complex advanced ML workflows are deve loped in which one task may depend on the results of previous tasks. How to make use of vast distributed CPUs/GPUs in WLCG for these big complex ML tasks has be come a popular research area. In this paper, we present our efforts enabling the execution of distributed ML workflows on the Production and Distributed Analysi s (PanDA) system and intelligent Data Delivery Service (iDDS). First, we describ e how PanDA and iDDS deal with large-scale ML workflows, including the implement ation to process workloads on diverse and geographically distributed computing r esources. Next, we report real-world use cases, such as HyperParameter Optimizat ion, Monte Carlo Toy confidence limits calculation, and Active Learning.”
Brookhaven National LaboratoryCyborgsEmerging TechnologiesMachine Learning
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