Supercomputing and Computation

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Data Tools

SDAV (Data Management/Scientific Software Tools)


Scalable Data Management, Analysis, and Visualization (SDAV) provides comprehensive expertise in scientific data management, analysis, and visualization aimed at transferring state of the art techniques into operational use by application scientists on leadership-class computing facilities. Our team works directly with application scientists to assist them by applying the best tools and technologies at our disposal, and learning from the scientists where our tools fall short. Technical solutions to any shortcomings are implemented to ensure that our tools overcome mission-critical challenges in the scientific discovery process. These tools are further developed and improved as these computing platforms change.

Data Management: Experts in our field expect that, as concurrency grows, there will be a widening gap between computational and I/O capacity, and this will be further stressed by energy demands. Our approach is to perform as much work as possible while the data is still resident in application memory, a use model often referred to as “in-situ.”

Simulations are generating an unprecedented amount of data, facilitated by the rapidly increasing computational capabilities of leading compute resources. This presents significant challenges. One challenge lies in hardware trends: the enormous increases in compute power are not being matched by corresponding increases in bandwidth to storage. Cost and power constrain the feasibility of dramatically larger storage deployments. A second challenge lies in extracting knowledge from these volumes of data. Research in data management infrastructure has created capabilities that can assist in this process, but the available tools are not widely used and deployed. These are not just future challenges, but rather, they are already causing bottlenecks that substantially impact the quality and productivity of scientific research performed with HPC machines.

Scientific Software Tools: A sustainable software infrastructure requires quality assurance, regression testing, distribution, and tracking feedback from the users. Our intent is to deliver a software infrastructure to the scientific community that couples the best practices from both research and development.

Source: http://sdav-scidac.org/

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