GPU Example: Tesla V100
Steve Lantz
Cornell Center for Advanced Computing
Revisions: 5/2023, 12/2021, 5/2021 (original)
It's fine to have a general understanding of what graphics processing units can be used for, and to know conceptually how they work. But at the actual hardware level, what does a particular GPU consist of, if one peeks "under the hood"? Sometimes the best way to learn about a certain type of device is to consider one or two concrete examples. First we'll take detailed look at the Tesla V100, one of the NVIDIA models that has been favored for HPC applications. In a subsequent topic, we do a similar deep dive into the Quadro RTX 5000, a GPU which is found in TACC's Frontera.
Objectives
After you complete this topic, you should be able to:
- List the main architectural features of GPUs and explain how they differ from comparable features of CPUs
- Discuss the implications for how programs are constructed for General-Purpose computing on GPUs (or GPGPU), and what kinds of software ought to work well on these devices
- Describe the names, sizes, and speeds of the computational and memory components of specific models of NVIDIA GPU devices
Prerequisites
- Familiarity with High Performance Computing (HPC) concepts could be helpful, but most terms are explained in context.
- Parallel Programming Concepts and High-Performance Computing could be considered as a possible companion to this topic, for those who seek to expand their knowledge of parallel computing in general, as well as on GPUs.