Graduate students, post-docs and professionals from academia, government, and industry are invited to sign up now for two summer school courses offered by the Virtual School of Computational Science and Engineering.
• Data Intensive Summer School (July 8-10, 2013)
• Proven Algorithmic Techniques for Many-core Processors (July 29-Aug. 2, 2013)
These Virtual School courses will be delivered to sites nationwide using high-definition videoconferencing technologies, allowing students to participate at a number of convenient locations where they will be able to work with a cohort of fellow computational scientists, have access to local experts, and interact in real time with course instructors.
The Data Intensive Summer School focuses on the skills needed to manage, process, and gain insight from large amounts of data. It targets researchers from the physical, biological, economic, and social sciences who need to deal with large collections of data. The course will cover the nuts and bolts of data-intensive computing, common tools and software, predictive analytics algorithms, data management, and non-relational database models.
Participating sites are: University of California Los Angeles; Louisiana State University; Marshall University; Michigan State University; Northwestern University and the University of Chicago; Princeton University; Purdue University; University of California San Diego; University of Delaware; the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign; University of Oklahoma; University of Tennessee Knoxville; University of Texas at Brownsville; University of Texas at El Paso; and University of Wisconsin Milwaukee.
For more information about the Data-Intensive Summer School, including pre-requisites and course topics, visithttp://www.vscse.org/summerschool/wp-content/uploads/2013/bigdata.html.
The Proven Algorithmic Techniques for Many-core Processors summer school will present students with the seven most common and crucial algorithm and data optimization techniques to support successful use of GPUs for scientific computing.
Studying many current GPU computing applications, the course instructors have learned that the limits of an application’s scalability are often related to some combination of memory bandwidth saturation, memory contention, imbalanced data distribution, or data structure/algorithm interactions. Successful GPU application developers often adjust their data structures and problem formulation specifically for massive threading and executed their threads leveraging shared on-chip memory resources for bigger impact. The techniques presented in the course can improve performance of applicable kernels by 2-10X in current processors while improving future scalability.
Participating sites for this course are: University of California Los Angeles; Clemson University; Louisiana State University; Marshall University; Michigan State University; Princeton University; Purdue University; University of Delaware; the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign; University of Tennessee Knoxville; University of Texas at Brownsville; University of Texas at El Paso; and University of Utah.
For more information about the Proven Algorithmic Techniques for Many-core Processors course, including pre-requisites and course topics, visit http://www.vscse.org/summerschool/wp-content/uploads/2013/manycore.html.
Registration fees for each course are $100, with UCLA-IDRE waiving the fee for UCLA researchers. To register, first visit the user portal for the Extreme Science and Engineering Discovery Environment (XSEDE):https://portal.xsede.org/. If this is your first use of the XSEDE portal, follow the guidelines to create a free portal account. Once you have an XSEDE portal account, you may sign up for the Virtual School courses through the XSEDE course calendar: https://portal.xsede.org/course-calendar.
For more information about the Virtual School, go to http://www.vscse.org. Questions about the summer school can be sent to email@example.com.