systems, from mobile to supercomputers, are becoming heterogeneous, massively
parallel computers for higher power efficiency and computation
throughput. While the computing community is racing to build tools and
libraries to ease the use of these systems, effective and confident
use of these systems will always require knowledge about low-level
programming in these systems. This course is designed for students to
learn the essence of low-level programming interfaces and how to use these
interfaces to achieve application goals. CUDA C, with its good balance between
user control and verboseness, will serve as the teaching vehicle for the first
half of the course. Students will then extend their learning into closely
related programming interfaces such as OpenCL, OpenACC, and C++AMP.
The course is unique in that it is application oriented and only introduces the
necessary underlying computer science and computer engineering knowledge for
understanding. It covers the concept of data parallel execution models,
memory models for managing locality, tiling techniques for reducing bandwidth
consumption, parallel algorithm patterns, overlapping computation with
communication, and a variety of
heterogeneous parallel programming interfaces. The concepts learned in this
course form a strong foundation for learning other types of parallel
Week One: Introduction to Heterogeneous
Computing, Overview of CUDA C, and Kernel-Based Parallel Programming, with lab tour
and programming assignment of vector addition in CUDA C.
Week Two: Memory Model for Locality, Tiling
for Conserving Memory Bandwidth, Handling Boundary Conditions, and Performance
Considerations, with programming assignment of simple matrix-matrix multiplication
in CUDA C.
Week Three: Parallel Convolution Pattern, with
programming assignment of tiled matrix-matrix multiplication in CUDA C.
Week Four: Parallel Scan Pattern, with
programming assignment of parallel convolution in CUDA C.
Week Five: Parallel Histogram Pattern and
Atomic Operations, with programming assignment of parallel scan in CUDA C.
Week Six: Data Transfer and Task
Parallelism, with programming assignment of parallel histogram in CUDA C.
Week Seven: Introduction to OpenCL,
Introduction to C++AMP, Introduction to OpenACC, with programming assignment of
vector addition using streams in CUDA C.
Week Eight: Course Summary,
Other Related Programming Models –Thrust, Bolt, and CUDA FORTRAN, with
programming assignment of simple matrix-matrix multiplication in choice of
OpenCL, C++AMP, or OpenACC.
Week Nine: complete any
remaining lab assignments, with optional, bonus programming assignments in choice
of OpenCL, C++AMP, or OpenACC.
Kristina Šekrst completed this course and found the course difficulty to be hard.
I've enjoyed this course, and the programming assignments were fun (on a separate platform though, so this complicated things a bit). Caveat: this is far away from an introductory course, and there's a lot of Googleing forthcoming if you're a newbie like me, since it's easy to get lost.