Jean Luca Bez

Jean Luca is a Carrer-Track Researcher in the Scientific Data Management Group at Lawrence Berkeley National Laboratory (LBNL), USA. He is passionate about High-Performance I/O, Parallel I/O, Education, and Competitive Programming. His research focuses on optimizing the I/O performance of scientific applications at the middleware level by exploring I/O Forwarding, I/O Scheduling, and Automatic Tuning and Reconfiguration using Machine Learning techniques.
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Conference Papers
Bin Dong, Jean Luca Bez, Suren Byna, "AIIO: Using Artificial Intelligence for Job-Level and Automatic I/O Performance Bottleneck Diagnosis.", In Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing (HPDC ’23), June 16, 2023,
- Download File: IODiagnose-final.pdf (pdf: 1.9 MB)
Hammad Ather, Jean Luca Bez, Boyana Norris, Suren Byna, "Illuminating the I/O Optimization Path of Scientific Applications", High Performance Computing: 38th International Conference, ISC High Performance 2023, Hamburg, Germany, May 21–25, 2023, Proceedings, Hamburg, Germany, Springer-Verlag, May 21, 2023, 22–41, doi: https://doi.org/10.1007/978-3-031-32041-5_2
The existing parallel I/O stack is complex and difficult to tune due to the interdependencies among multiple factors that impact the performance of data movement between storage and compute systems. When performance is slower than expected, end-users, developers, and system administrators rely on I/O profiling and tracing information to pinpoint the root causes of inefficiencies. Despite having numerous tools that collect I/O metrics on production systems, it is not obvious where the I/O bottlenecks are (unless one is an I/O expert), their root causes, and what to do to solve them. Hence, there is a gap between the currently available metrics, the issues they represent, and the application of optimizations that would mitigate performance slowdowns. An I/O specialist often checks for common problems before diving into the specifics of each application and workload. Streamlining such analysis, investigation, and recommendations could close this gap without requiring a specialist to intervene in every case. In this paper, we propose a novel interactive, user-oriented visualization, and analysis framework, called Drishti. This framework helps users to pinpoint various root causes of I/O performance problems and to provide a set of actionable recommendations for improving performance based on the observed characteristics of an application. We evaluate the applicability and correctness of Drishti using four use cases from distinct science domains and demonstrate its value to end-users, developers, and system administrators when seeking to improve an application’s I/O performance.