Units:

12

Description:

The fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture. This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance software development using important functionality such as linear algebra kernels, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and how to write parallel code for multicore, manycore, and cluster platforms, based on state-of-the-art research. Further, a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course. Finally, the course will introduce the students to the recent field of automatic performance tuning. Prerequiste: Senior ECE or CS undergraduate student or higher, solid C programming skills.

In Spring 2019 this course is broadcast from the Silicon Valley campus. ECE Pittsburgh students attend classes synchronously with students in Silicon Valley.

Last Modified: 2019-05-29 4:11PM

Semesters offered:

• Spring 2019
• Spring 2015
• Fall 2013
• Fall 2012
• Fall 2011