Extracting scientific insight from large scale computational and experimental scientific facilities is of critical importance in numerous fields including material science, astrophysics, nuclear physics, high-energy physics and biosciences. With the rapid increases in the scale of computation together with the increased resolution and fidelity of experiments, the data produced has increased in size, complexity, and in richness by orders of magnitude. The trend will continue as users of scientific facilities struggle with the daunting task of analyzing their datasets for knowledge discovery. In this talk, we will elucidate the data and system challenges faced by applications at scale on leadership supercomputers and present various promising approaches. We will give an overview our work in GLEAN - A flexible data-driven framework for analysis and I/O at extreme scale.

Venkatram Vishwanath is an assistant computer scientist at Argonne National Laboratory. His interests are in the areas of large-scale scientific data analysis and visualization, scalable data movement and I/O middleware,  hardware architectures, and collaboration systems.  He is currently working on scalable solutions to enable scientists to glean insights from their large scientific data. His work has lead to publications in ACM/IEEE Supercomputing, IEEE Cluster Computing, IEEE Visualization, among others.

While there will be light refreshments available, feel free to "brown bag" it and bring in food from the outside to eat during the social hour.

Reservations:
Click here to Reserve for Wednesday, March 14
or send an e-mail to greg@neumarke.net