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Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
---|---|---|---|---|---|---|
High Performance Computing With Cuda | BTM611 | Elective | Doctorate degree | 1 | Fall | 8 |
Prof. Dr. Hikmet Hakan GÜREL
Research Assistant Seda BALTA
1) Knows how to develop programming models on GPU.
2) Learns Cuda programming model.
3) Knows integrated memory and CPU-GPU programming.
Program Competencies | ||||||||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | ||
Learning Outcomes | ||||||||||||||
1 | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | |
2 | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | |
3 | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle | Middle |
Face to Face
None
Not Required
Technological development of GPUs. Basic criteria for parallel algorithm design. CUDA programming model. GPU microarchitecture. Occupancy, GPU performance, performance analysis and debugging tools. Optimizations for program control flow. GPU memory structure. Methods to improve memory performance. Integrated memory and CPU-GPU programming. Synchronization, atomic processes, techniques for memory consistency. Dynamic parallelism and multi-GPU programming. CUDA libraries (CuBlas, CuDNN, etc.). Other GPU programming environments, OpenCL, HSA.
Turkish
Required