Heterogeneous Parallel Programming
持续时间: 9 weeks
Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.
课程评论: 4 个评论
All computing systems, from mobile to supercomputers, are becoming heterogeneous parallel computers using both multi-core CPUs and many-thread GPUs for higher power efficiency and computation throughput. While the computing community is racing to build tools and libraries to ease the use of these heterogeneous parallel computing systems, effective and confident use of these systems will always require knowledge about the low-level programming interfaces in these systems. This course is designed for students in all disciplines to learn the essence of these programming interfaces (CUDA/OpenCL, OpenMP, and MPI) and how they should orchestrate the use of these interfaces to achieve application goals.
The course is unique in that it is application oriented and only introduces the necessary underlying computer science and computer engineering knowledge needed for understanding. It covers data parallel execution model, memory models for locality, parallel algorithm patterns, overlapping computation with communication, and scalable programming using joint MPI-CUDA in large scale computing clusters. It has been offered as a one-week intensive summer school for the past four years. In the past two years, there have been ten video-linked academic sides with a total of more than two hundred students each year.
- Week One: Introduction to Heterogeneous Computing and a Quick Overview of CUDA C and MPI, with lab setup and programming assignment of vector addition in CUDA C
- Week Two: Kernel-Based Data Parallel Programming and Memory Model for Locality, with programming assignment of simple and tiled matrix multiplication.
- Week Three: Performance Considerations and Task Parallelism Model, with programming assignment in performance tuning.
- Week Four: Parallel Algorithm Patterns – Reduction/Scan, stencil computation and Sparse computation, with programming assignment of reduction tree.
- Week Five: MPI in a Heterogeneous Computing Cluster: domain partitioning, data distribution, data exchange, and using heterogeneous computing nodes, with programming assignment of a MPI-CUDA application.
- Week Six: Related Programming Models – OpenACC, CUDA FORTRAN, C++AMP, Thrust, and important trends in heterogeneous parallel computing, with final exam.
0 票支持; 0 票反对
0 票支持; 0 票反对
1 票支持; 0 票反对