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Start Over Author "Alekseev, A." Publication Type Conference Materials
1,155 results on '"Alekseev, A."'

49. Utilizing Distributed Heterogeneous Computing with PanDA in ATLAS.

Author

Maeno, Tadashi, Alekseev, Aleksandr, Barreiro Megino, Fernando Harald, De, Kaushik, Guan, Wen, Karavakis, Edward, Klimentov, Alexei, Korchuganova, Tatiana, Lin, FaHui, Nilsson, Paul, Wenaus, Torre, Yang, Zhaoyu, and Zhao, Xin

Subjects
DISTRIBUTED computing, WORKFLOW, MANAGEMENT, CLOUD computing, REMOTE computing
Abstract

In recent years, advanced and complex analysis workflows have gained increasing importance in the ATLAS experiment at CERN, one of the large scientific experiments at LHC. Support for such workflows has allowed users to exploit remote computing resources and service providers distributed worldwide, overcoming limitations on local resources and services. The spectrum of computing options keeps increasing across the Worldwide LHC Computing Grid (WLCG), volunteer computing, high-performance computing, commercial clouds, and emerging service levels like Platform-as-a-Service (PaaS), Container-as-a-Service (CaaS) and Function-as-a-Service (FaaS), each one providing new advantages and constraints. Users can significantly benefit from these providers, but at the same time, it is cumbersome to deal with multiple providers, even in a single analysis workflow with fine-grained requirements coming from their applications' nature and characteristics. In this paper, we will first highlight issues in geographically-distributed heterogeneous computing, such as the insulation of users from the complexities of dealing with remote providers, smart workload routing, complex resource provisioning, seamless execution of advanced workflows, workflow description, pseudointeractive analysis, and integration of PaaS, CaaS, and FaaS providers. We will also outline solutions developed in ATLAS with the Production and Distributed Analysis (PanDA) system and future challenges for LHC Run4. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Distributed Machine Learning Workflow with PanDA and iDDS in LHC ATLAS.

Author

Guan, Wen, Maeno, Tadashi, Zhang, Rui, Weber, Christian, Wenaus, Torre, Alekseev, Aleksandr, Barreiro Megino, Fernando Harald, De, Kaushik, Karavakis, Edward, Klimentov, Alexei, Korchuganova, Tatiana, Lin, FaHui, Nilsson, Paul, Yang, Zhaoyu, and Zhao, Xin

Subjects
MACHINE learning, WORKFLOW, PARTICLE physics, COMPUTATION laboratories, MONTE Carlo method
Abstract

Machine Learning (ML) has become one of the important tools for High Energy Physics analysis. 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 exploit the physics potentials, more and more computing resources are required for processing these ML tasks. In addition, complex advanced ML workflows are developed 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 become a popular research area. In this paper, we present our efforts enabling the execution of distributed ML workflows on the Production and Distributed Analysis (PanDA) system and intelligent Data Delivery Service (iDDS). First, we describe how PanDA and iDDS deal with large-scale ML workflows, including the implementation to process workloads on diverse and geographically distributed computing resources. Next, we report real-world use cases, such as HyperParameter Optimization, Monte Carlo Toy confidence limits calculation, and Active Learning. Finally, we conclude with future plans. [ABSTRACT FROM AUTHOR]

Published
2024
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