6.338J Parallel Computing
Interdisciplinary introduction to parallel computing and modern big data analysis using Julia. Covers scientific computing topics such as dense and sparse linear algebra, N-body problems, and Fourier transforms, and geometric computing topics such as mesh generation and mesh partitioning. Focuses on application of these techniques to machine learning algorithms in big data applications. Provides direct experience with programming traditional-style supercomputing as well as working with modern cloud computing stacks. Designed to separate the realities and myths about the kinds of problems that can be solved on the world's fastest machines.
6.338J will be offered this semester (Fall 2017). It is instructed by A. Edelman.
This class counts for a total of 12 credits. This is a graduate-level class.
You can find more information at the 6.338 Class Site site.
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