NVIDIA believes in extending our University investment to include support of teaching, research, and advanced education.  The GPU Education Center Program is designed to support and encourage teaching establishments to include GPU Computing using CUDA C/C++ as part of their course offerings.  To help facilitate this teaching effort, NVIDIA may grant equipment, funding and course material assistance.

Benefits

  • Desinate as GPU Education Center.  The University and each course including GPU Computing using CUDA C/C++ will be identified on NVIDIA’s website to help students find Universities which provide training for GPU Computing using CUDA C/C++.
  • Donate CUDA enabled GPUs to be installed in teaching lab computers making it possible for students and other members of the University community to have direct access to CUDA enabled systems for hands-on experience of CUDA C/C++ development, debugging and experimentation.   This initial teaching kit consists of (1) high-end GPU for timing and benchmarking and (2) CUDA-capable GPUs for teaching. These GPUs will be of our choosing, but are currently (1) Tesla K40 (active) and (2) Titan X GPUs.
  • Access to 100 GPU programming labs available at nvidia.qwiklab.com for free, of which you can divide amongst your students.  As these labs are self-paced and hosted in the cloud, a student only needs a web-browser and internet access to participate. Available labs can be viewed here under Labs tab.
  • Webinars for CUDA related programming from experts in various aspects of optimizing performance.
  • Teaching support materials and courseware in the form of podcasts, doc files, power points, lectures and problem assignments sets. 
  • Optional book donation of (10) copies of the Programming Massively Parallel Processors book authored by David B. Kirk & Wen-mei W. Hwu. Must be requested in the proposal to be considered.
  • Optionally award teaching assistant funds.  These funds will be awarded as 50% matching funds and must be requested in the proposal to be considered. Not all requests for TA funds will be approved.
  • Automatically include the center in the Centers Reward Program, providing special pricing on NVIDIA Tesla GPUs (limits and exclusions apply). Check with an NVIDIA OEM and Tesla Reseller for the special pricing.

Expectations
Universities selected as GPU Education Centers are expected to:

  • Include GPU computing and/or CUDA C/C++ as a substantial portion of the curriculum in any graduate or undergraduate level recurring course on parallel programming.  Seminars do not qualify for this program.
  • Provide occasional GPU comoputing training available to other members of their University community.  (Example:  graduate students and professors in other disciplines or departments).
  • Encourage the active use of the GPU education lab so that it is continuously booked.
  • Keep NVIDIA informed of all major scheduling and course changes relating to the GPU education courses.
  • Provide a letter of commitment from the dean or department head (on official letterhead, please) stating the university's intention to include GPU computing and/or CUDA C/C++ as a substantial portion of a recurring course.
  • Provide a course summary on what worked, what didn’t, recommendation for improved course, course materials, etc. to help improve the state of parallel computing education.
  • No overhead or fees attached to any equipment or TA Matching fund awards incurred.

Application Process
Please submit a 1-3 page PDF or word proposal to GECproposal@nvidia.com.  Proposals will be reviewed and announced on a quarterly basis and should include the following information:

  • Full Contact information including mailing address, phone number and PI contact name. Proposals missing contact information will be automatically rejected.
  • Details of courses which will include GPU computing and/or CUDA C/C++ (course name, number, URL, approximate number of lectures/assignments/projects/etc through which students are exposed to GPU computing and/or CUDA C/C++, and any additional relevant information).
  • Description & Specifications of PC’s in proposed GPU education lab.
  • Matching funds proposal and budget outline for teaching assistant, graduate student or equivalent or any books requested (if applicable).
  • A letter from the Dean or Dept Head stating in good faith that the course will include CUDA C/C++ and be a recurring part of the curriculum.

The next review will be mid-October, application deadline is October 1, 2015.

See our current list of Worlwide GPU Education Centers

If you have any questions about the program or the status of your application, please email us at GECproposal@nvidia.com.