Course Information

Course Name	Turkish	CUDA ile GPU’lar ic¸in Paralel Hesaplama
	English	Paral.Comp.for GPUs Using CUDA
Course Code	BLG 562E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	-	3	3	-	-
Course Language		English
Course Coordinator		Ayşe Yılmazer Metin
Course Objectives		Understanding the CUDA programming model and GPU micro-architecture. Studying and understanding the performance of CUDA programs and learning to develop efficient parallel programs using CUDA.
Course Description		History of GPUs. Principles of parallel algorithm design. CUDA programming model. GPU micro- architecture. Occupancy, GPU performance and performance analysis and debugging tools. Branch divergence and control flow optimizations. GPU memory system. Memory system optimizations. Unified Memory and CPU-GPU cooperative computing. Synchronization, atomics, cache coherence, memory consistency. Parallel patterns, Histogram, Prefix-sum, etc. Dynamic parallelism, streams, multi-GPU programming. CUDA libraries, CuBlas, CuDNN, NPP, NvGraph, cuFFT, NCCL, cuSPARSE, etc. Other GPU programming environments, OpenCL and HSA.
Course Outcomes		1. Listing and applying fundamental principles of parallel algorithm design. 2. Describe common GPU architectures and programming models. 3. Explaining the factors that affect the performance of GPU programs. 4. Describing and applying the fundamental control flow optimizations to GPU programs. 5. Describing and applying the fundamental memory optimizations to GPU programs. 6. Explaining the techniques for communication and synchronization in GPU and understanding the overhead of communication on GPUs. 7. Using parallel patterns with CUDA.
Pre-requisite(s)
Required Facilities		C/C++ programming
Other
Textbook		David B. Kirk and Wen-mei Hwu, 2012, Programming Massively Parallel Processors: A Hands-on Approach, (2nd Edition), Morgan Kaufmann, ISBN 0- 12-415992-3. • Grama, A. Gupta, G. Karypis, and V. Kumar, Introduction to Parallel Computing, 2003, (2nd Ed.), Addison-Wesley.
Other References		NVidia, CUDA Programming Guide

Courses

Help

About