[linuxkernelnewbies] Graphic Cards Technical Computing

[Peter Teoh]

http://web.mit.edu/professional/short-programs/courses/graphic_cards_technical_computing.html Graphic Cards for Technical Computing [6.15s] Click here for email updates - stay informed about course availability and registration dates. Date: June 22-26, 2009 | Tuition: $3,000 | Continuing Education Units (CEUs): 3.0 Course Summary Any computer with a modern high-end graphics card contains a high-speed parallel computer that can perform technical computations much faster than the host machine's CPU. This course focuses on using programmable graphics chips (GPUs) to accelerate single and multi-computer technical computing calculations. The techniques covered provide a potentially powerful way to extract much more performance out of a computer system for a given amount of power and for a modest budget. The course includes both practical hands-on activities, exploring the technologies that are available today to program these devices, and a critical overview of relevant parallel programming paradigms. Together these topics provide an in-depth perspective on emerging GPU technology, covering its potential benefits as well as its challenges. The course is suited to both hands-on technical computing practitioners and technically knowledgeable decision makers interested in understanding how desktop, power efficient parallel computing technologies might impact their domain. The course will include multiple worked example application scenarios from numerical computing simulations using partial and ordinary differential equations and employing implicit and explicit methods. Content   Fundamentals: Core concepts, understandings and tools (30%)   Latest Developments: Recent advances and future trends (30%)   Industry Applications: Linking theory and real-world (40%) Delivery Methods   Lecture: Delivery of material in a lecture format (40%)   Discussion or Groupwork: Participatory learning (20%)   Labs: Demonstrations, experiments, simulations (40%) Level   Introductory: Appropriate for a general audience (20%)   Specialized: Assumes experience in practice area or field (70%)   Advanced: In-depth explorations at the graduate level (10%) « Back to Top Learning Objectives Understand the origin of GPU performance. Examine the programming abstractions that GPUs offer. Understand what algorithms are or are not suitable for GPUs. Apply software strategies that make sense. Learn how to use GPUs singly and in parallel clusters. « Back to Top Who Should Attend Participants should have some understanding of computer programming. Typical participants will be interested in learning how parallel processing using graphics cards can accelerate the solution time for technical computing problems in science, mathematics and engineering. « Back to Top Lecturers Chris Hill Principal Research Scientist in the Department of Earth, Atmospheric & Planetary Sciences at MIT.