Berkeley Lab and ESnet Document Flow, Performance of 56 Terabytes Climate Data Transfer

Visualization by Prabhat (Berkeley Lab).
The simulated storms seen in this visualization are generated from the finite volume version of NCAR’s Community Atmosphere Model. Visualization by Prabhat (Berkeley Lab).

In a recent paper entitled “An Assessment of Data Transfer Performance for Large‐Scale Climate Data Analysis and Recommendations for the Data Infrastructure for CMIP6,” experts from Lawrence Berkeley National Laboratory (Berkeley Lab) and ESnet (the Energy Sciences Network, document the data transfer workflow, data performance, and other aspects of transferring approximately 56 terabytes of climate model output data for further analysis.

The data, required for tracking and characterizing extratropical storms, needed to be moved from the distributed Coupled Model Intercomparison Project (CMIP5) archive to the National Energy Research Supercomputing Center (NERSC) at Berkeley Lab.

The authors found that there is significant room for improvement in the data transfer capabilities currently in place for CMIP5, both in terms of workflow mechanics and in data transfer performance. In particular, the paper notes that performance improvements of at least an order of magnitude are within technical reach using current best practices.

To illustrate this, the authors used Globus to transfer the same raw data set between NERSC and Argonne Leadership Computing Facility (ALCF) at Argonne National Lab.

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University of Virginia, ESnet Team Up to Win Best Paper

Networking has several definitions, including making key connections and moving information from one point to another. And a paper co-authored by engineers at ESnet and the University of Virginia proves the value of both — it was named a Best Paper at the Sixth International Conference on Communication Theory, Reliability, and Quality of Service, held April 21-26 in Venice, Italy.

The paper, “On How to Provision Quality of Service (QoS) for Large Dataset Transfers,” was written by Zhenzhen Yan, a Ph.D. student at the University of Virginia (UVA), UVA Professor Malathi Veeraraghavan, and Chris Tracy and Chin Guok of ESnet.

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