This course aims to explore the evolution of NVIDIA GPU Computing from its origins in PC gaming to its current role in deep learning applications. The learning outcomes include understanding the journey of GPUs, their role in various industries, and the power they provide for training deep neural networks. The course covers topics such as classic GPU architectures, rendering, CUDA, and the evolution of NVIDIA GPUs from G80 to Volta GV100. The teaching method involves a seminar-style presentation by an industry expert. This course is intended for individuals interested in computer systems, GPU computing, deep learning, and the applications of GPUs in various fields.
Overview
Syllabus
Introduction
Background about Nvidia
Gaming
Console Gaming
Cloud Gaming
Supercomputing
Tesla V100
How do we get here
The intent
Classic GPUs
Rendering
Numeric representations
Vertex fetch engine
Unified shaders
G80
throughput vs latency
CUDA
Fermi Architecture
Kepler Architecture
Pascal Architecture
GTX 1080 TI
Volta GV100
Tensor Core
Interconnect
Titan
Deep Neural Network
ImageNet
Models are Complex
Training
Tensor RT
Image Per Second
Automotive
SOCs
Drive PX2
Taught by
Stanford Online
