Designing Scientific Applications on GPUs

Designing Scientific Applications on GPUs

Project reference: 2130

In recent years there has been a massive development in the high-performance computing architecture. More precisely, almost all the supercomputers are having the accelerators such as GPUs. On the other hand, there is a big question rising if we use the available HPC resource in scientific computing. Therefore, it is necessary to use the heterogeneous machine for scientific applications in the scientific research community. When porting the code to the new architecture, many things need to be considered, especially the performance analysis and correctness.

Project Mentor: Dr. Ezhilmathi Krishnasamy

Project Co-mentor: Dr. Sebastien Varrette

Site Co-ordinator: Dr. Ezhilmathi Krishnasamy

Learning Outcomes:
* Porting scientific algorithm into GPUs.
* More understanding of GPU architecture and its limitations.
* Performance analysis and code optimization.
* Numerical analysis and its application.

Student Prerequisites (compulsory):
* Programming skills in C/C++ and CUDA programming

Student Prerequisites (desirable):
* Basic knowledge in parallel programming model or parallel computer architecture and applied mathematics.

Training Materials:


Week 1/: Training week
Week 2/:  Literature Review Preliminary Report (Plan writing)
Week 3 – 7/: Project Development
Week 8/: Final Report write-up

Final Product Description:
* Porting scientific C/C++ code into GPU and check correctness.

Adapting the Project: Increasing the Difficulty:
* Using multiple GPUs might increase the difficulty of the project.

Adapting the Project: Decreasing the Difficulty:
*  CUDA implementation for the few tasks.

* Opensource software framework will be considered.
* Student will get a desktop computer and HPC account at Iris supercomputer of the University of Luxembourg.


ULux-University of Luxembourg

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