For a fee of $100, students can participate at state-of-the-art classrooms across the country. The distributed sites are linked by high-definition videoconferencing, enabling synchronous audio/video Q&A. Students can benefit from hands-on lab activities with on-site support from skilled teaching assistants. All students will have access to high-performance computing resources during the course and will receive certificates of completion.
Snacks and an evening reception will be provided for on-site students, but participants are responsible for their own travel and lodging costs (low-cost dorm accommodations will be provided where possible). Students are required to provide their own laptops.
For no additional cost, on-site participants can take an online short course on CUDA that are designed to help them meet course prerequisites. These courses are available only to on-site participants.
NOTE: On-site participation is "first come, first served" and seating at each site is limited. Register early to reserve a seat at your preferred site!
Students who register in April will be notified of their site assignment by May 7. Those who register in May or after will be notified within two weeks of registration.
Once you are notified of your site assignment, you have two weeks to pay the course fee and complete the registration process before you forfeit your spot.
Center for Computation & Technology, Louisiana State University, Baton Rouge
Institute for Data and High Performance Computing, Georgia Institute of Technology, Atlanta
Institute for Digital Research and Education, University of California, Los Angeles
National Center for Supercomputing Applications, Urbana, Illinois
Northwestern University, Evanston, Illinois
Ohio Supercomputer Center, Ohio State University, Columbus
RENCI, Chapel Hill, North Carolina
University of Iowa, Iowa City
University of Michigan, Ann Arbor
University of Tennessee, Knoxville
Experience working in a Unix environment
Experience developing and running scientific codes written in C or C++
Basic knowledge of CUDA (A short online course, Introduction to CUDA, is available to registered on-site students who need assistance in meeting this prerequisite)
Students who took the course Many-core Processors in 2009 are encouraged to take this follow-on course, which includes new topics and lab exercises.
Wen-Mei W. Hwu, professor of electrical and computer engineering and principal investigator of the CUDA Center of Excellence, University of Illinois at Urbana-Champaign
David Kirk, NVIDIA fellow
why problem formulation and algorithm design choices can have dramatic effect on performance
common algorithmic strategies for high performance
Increasing locality in dense arrays
tiling of data access and layout
Improving efficiency and vectorization in dense arrays
Reducing output interference
conversion from scatter to gather
parallelizing reductions and histograms
Dealing with non-uniform data
data sorting and binning
Dealing with sparse data
sorting and packing
Dealing with dynamic data
parallel queue-based algorithms
Improving data efficiency in large data traversal
stencil and other grid-based computation
Extending beyond many-core processors
Overview of use of techniques in application domains
computational fluid dynamics
molecular dynamics (NAMD/VMD, MPI, use of algorithm strategies)
gene sequencing, financial analysis, etc.
NOTE: Students are required to provide their own laptops.
Event Date & Time:
Monday, August 15, 2011 - 00:00 to Friday, August 19, 2011 - 00:00