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Computer Languages & Systems Software

Julian Bellavita

Julian Bellavita
Computer Languages and Systems Software Group
Phone: 541-285-3790
Lawrence Berkeley National Laboratory

Julian is a senior at UC Berkeley studying Computer Science. His primary research interests are parallel programming tools for scientific computing, data management techniques for large scientific datasets, and dense linear algebra. His current work at LBL focuses on symPACK, a UPC++ based solver for sparse symmetric matrices.

Conference Papers

J. Bellavita, A. Sim, K. Wu, I. Monga, C. Guok, F. Würthwein, D. Davila, "Studying Scientific Data Lifecycle in On-demand Distributed Storage Caches", 5th ACM International Workshop on ​System and Network Telemetry and Analysis (SNTA) 2022, in conjunction with The 31st ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2022, doi: 10.1145/3526064.3534111


Julian Bellavita, Alex Sim (advisor), John Wu (advisor), "Predicting Scientific Dataset Popularity Using dCache Logs", ACM/IEEE The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’22), ACM Student Research Competition (SRC), November 2022,

Poster (PDF)

The dCache installation is a storage management system that acts as a disk cache for high-energy physics (HEP) data. Storagespace on dCache is limited relative to persistent storage devices, therefore, a heuristic is needed to determine what data should be kept in the cache. A good cache policy would keep frequently accessed data in the cache, but this requires knowledge of future dataset popularity. We present methods for forecasting the number of times a dataset stored on dCache will be accessed in the future. We present a deep neural network that can predict future dataset accesses accurately, reporting a final normalized loss of 4.6e-8. We present a set of algorithms that can forecast future dataset accesses given an access sequence. Included are two novel algorithms, Backup Predictor and Last N Successors, that outperform other file prediction algorithms. Findings suggest that it is possible to anticipate dataset popularity in advance.