U.C. Berkeley CS267
Applications of Parallel Computers
This course teaches both graduate and advanced undergraduate students from diverse departments how use parallel computers both efficiently
and productively, i.e. how to write programs that run fast while minimizing
programming effort. The latter is increasingly important since essentially all computers are (becoming) parallel, from supercomputers to laptops.
So beyond teaching the basics about parallel computer architectures and programming languages, we emphasize commonly used patterns that appear in essentially all programs that need to run fast. These patterns include both common computations (eg linear algebra, graph algorithms, structured grids,..)
and ways to easily compose these into larger programs.
We show how to recognize these patterns in a variety of practical
problems, efficient (sometimes optimal) algorithms for implementing them,
how to find existing efficient implementations of these patterns when available,
and how to compose these patterns into larger applications.
We do this in the context of the most important parallel programming models today:
shared memory (eg PThreads and OpenMP on your multicore laptop),
distributed memory (eg MPI and UPC on a supercomputer), GPUs (eg CUDA and OpenCL, which could be both in your laptop and supercomputer), and cloud computing (eg MapReduce and Hadoop). We also present a variety of useful
tools for debugging correctness and performance of parallel programs.
Finally, we have a variety of guest lectures by a variety of experts,
including parallel climate modeling, astrophysics, and other topics.
Detailed Schedule of Lectures
(lecturers shown in parentheses)