General Purpose Computing with GPU

Graphics Processing Units (GPU) for computer graphics and gaming, general parallel computation, assessing the performance of parallel algorithms on GPUs, measuring the speedup over similar CPU algorithms, applications of signal processing, neural networks, etc., programming techniques for GPUs, NVIDIA’s parallel computing language, CUDA programming model and syntax, GPU architecture, high-performance computing on GPUs, parallel algorithms, CUDA libraries, applications of GPU computing, performance optimization and specific GPU applications, e.g., Machine Learning computations.

Hours: XYZ where X = Lecture, Y = Lab, Z = Credit
All hours are per week.
3 Lab hours constitute 1 credit hour
1 credit hour implies 1 lecture of 50mins per academic week. 16 weeks in total.
Pre-Requisite courses are courses required to be completed before this course may be taken
Co-Requisite courses are courses required to be taken along with this course