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Sifting Through a Trillion Electrons


SDM's Surendra Byna and colleagues from Berkeley Lab’s Computational Research Division teamed up with researchers to develop novel software strategies for storing, mining, and analyzing massive datasets generated by a state-of-the-art plasma physics code called VPIC. » Read More

Catching Turbulence in the Solar Wind


Massive datasets plus modelling, visualization and analytics allow researchers to "see" the unseen: the turbulence in solar winds. » Read More

Arie Shoshani Earns Lifetime Achievement Award

Arie award

More than 25 years ago, Arie Shoshani realized that researchers were facing significant challenges in organizing, managing and analyzing their scientific data. He set out to develop computer applications to help them better meet the challenges and created the Scientific Data Management Group in the process. » Read More

The Scientific Data Management (SDM) group develops technologies and tools for efficient data access and storage management of massive scientific datasets. We are currently developing storage resource management tools, data querying technologies, in situ feature extraction algorithms, along with software platforms for exascale data. The group also works closely with application scientists to address their data processing challenges. These tools and application development activities are backed by active research efforts on novel algorithms for emerging hardware platforms.

Group Leader: John Wu

»Visit the Scientific Data Management (SDM) site.

SDM Publications

SDN for End-to-end Networked Science at the Exascale

December 1, 2020

Towards HPC I/O performance prediction through large-scale log analysis

June 24, 2020

A Deep Deterministic Policy Gradient Based Network Scheduler For Deadline-Driven Data Transfers

June 22, 2020

Predicting Resource Requirement in Intermediate Palomar Transient Factory Workflow

May 11, 2020

BBOS: Efficient HPC Storage Management via Burst Buffer Over-Subscription

May 11, 2020

Life Course as a Contextual System to Investigate the Effects of Life Events, Gender, and Generation on Travel Mode Use

January 12, 2020

Analysis in the Data Path of an Object-centric Data Management System

December 18, 2019

Tuning Object-centric Data Management Systems for Large Scale Scientific Applications

December 18, 2019

Exploring Metadata Search Essentials for Scientific Data Management

December 17, 2019

A Reinforcement Learning Based Network Scheduler For Deadline-Driven Data Transfers

December 10, 2019

Analysis and Prediction of Data Transfer Throughput for Data-Intensive Workloads

December 10, 2019

Spatiotemporal Real-Time Anomaly Detection for Supercomputing Systems

December 10, 2019

Identifying Time Series Similarity in Large-Scale Earth System Datasets

December 10, 2019

Machine Learning for Prediction of Mid to LongTerm Habitual Transportation Mode Use

December 10, 2019

Federated Wireless Network Intrusion Detection

December 10, 2019

DeepRoute: Herding Elephant and Mice Flows with Reinforcement Learning

December 2, 2019

Revisiting I/O Behavior in Large-Scale Storage Systems: The Expected and the Unexpected

November 24, 2019

Comparison of Array Management Library Performance - A Neuroscience Use Case

November 20, 2019

Active Learning-based Automatic Tuning and Prediction of Parallel I/O Performance

November 19, 2019

Understanding Data Motion in the Modern HPC Data Center

November 19, 2019

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