As the computing industry transitions to massive parallelism, education must transition just as dramatically to ensure that processor designers, programmers, and end users are able to leverage the benefits of these new architectures.
For this reason, education is among the primary missions of the Parallel Computing Institute. With offerings ranging from complete curricula in parallel computing through the departments of Electrical and Computer Engineering and in Computer Science, to just-in-time workshops and seminars, PCI offers a broad selection of options for students and professionals and collaborates with organizations such as the GPU Center of Excellence, the Universal Parallel Computing Research Center, the Cloud Computing Testbed, and NCSA, among others, to make their offerings widely available.
Please check back to this site regularly for information on educational resources and programs on and related to parallel computing, including listings of internships, scholarships, and professional positions; and major meetings. The list below will grow as we receive postings.
Short Courses and Workshops Offered Annually:
- “Passionate on Parallel,” an NSF-sponsored summer Research Experience for Undergraduates (REU)
- Virtual School of Computational Science and Engineering (VSCSE) Summer School, sponsored by NCSA
- Symposium on Application Acceleration Accelerators in High-Performance Computing (SAAHPC), sponsored by NCSA
- "Programming and Tuning Massively Parallel Systems,” (PUMPS), sponsored by the Barcelona Supercomputing Center
- Training programs and resources through the Parnership for Advanced Computing in Europe (PRACE)
- Innovative Parallel Computing Conference (InPar), offered annually in San Jose, CA, sponsored by the CUDA Center:
- GPU Technology Conference, offered annually in San Jose, CA, sponsored by NVIDIA
- www.gpucomputing.net,sponsored by the GPU Center
- GPU Computing Research Forum, sponsored by the GPU Center
- Programming Massively Parallel Processors: A Hands-On Approach, by David Kirk and Wen-mei Hwu. Morgan Kaufmann/Elsevier, Boston MA, 2011.