Scientific applications are increasingly adopting Artificial Intelligence (AI) techniques to advance science. Specialized hardware accelerators are designed and built to efficiently run AI applications. With the diverse hardware and software stacks of these systems, it is challenging to comprehend their capabilities, programming approaches, and measure performance.
We present an overview of novel AI accelerators, namely Cerebras, SambaNova, Graphcore, and Groq. We provide steps on how to program these systems by porting deep learning models with any refactoring of codes implemented in standard DL framework implementations, compiling, and running on the accelerator hardware. The hands-on session enables attendees to understand the key capabilities of emerging AI accelerators and their performance implications for scientific applications.
- Time: October 30, 2024
- 1:30 - 5:15 PM: ALCF AI Testbeds Deep Dive and Hands-On
- Location: TCS 1416
- Slack Channel: #ai-testbeds: Use to get help, post questions, error messages, etc.
| Time (CT) | Topic |
|---|---|
| 1:30 - 1:45 | Overview of AI Testbeds at ALCF [Slides] |
| 1:45 - 2:30 | Cerebras Hands-On |
| 2:30 - 3:30 | Graphcore Hands-On |
| 3:45 - 4:00 | Break |
| 4:00 - 4:40 | Sambanova Hands-On |
| 4:40 - 5:15 | Groq Hands-On |
- ALCF Hands-on HPC Workshop 2024 Agenda
- Overview of AI Testbeds at ALCF
- ALCF AI Testbed Documentation
- Director’s Discretionary Allocation Program
Contributors: Siddhisanket (Sid) Raskar, Varuni Sastry, Bill Arnold, Krishna Teja Chitty-Venkata, and Murali Emani.
This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.