Performance of Accelerators
Last updated on 2025-09-24 | Edit this page
Estimated time: 10 minutes
Overview
Questions
- What are accelerators?
- How do they affect my jobs performance?
- How can I measure accelerator utilization?
Objectives
After completing this episode, participants should be able to …
- Understand difference of performance measurements on accelerators (GPUs, FPGAs) to CPUs.
- Understand how batch systems and performance measurements tools treat accelerators.
Narrative:
- The deadline is creeping up, only few ways to go!
- Hey, we have a GPU partition! Maybe this will help us speed up the process!
What we’re doing here:
- What changes?
- New metrics
- Transfer to/from accelerator
- Different options/requirements to scheduler & performance measurement tools
Introduction
Run the same example workload on GPU and compare.
Don’t mention FPGAs too much, maybe just a node what accelerators could be, besides GPU. Goal is to keep it simple and accessible, focus on what’s common in most HPC systems these days
Explain how to decide where to run something. CPU vs. small GPU vs. high-end GPUs. Touches on transfer overhead etc.
Summary
Leading question: Performance optimization is a deep topic and we are not done learning. How could I continue exploring the topic?
- Tools to measure GPU/FPGA performance of a job
- Common symptoms of GPU/FPGA problems