Through continuous monitoring and acceleration, AI Booster accelerates AI training and
inference,
improving resource utilization and productivity.
Regularly sample system metrics to infer AI performance bottlenecks
Leverages monitoring feedback for continuous acceleration improvements
Continued use enables even greater acceleration over time
Identify relationships between program execution and hardware resource usage based on monitoring results.
Monitor hardware load
How much of each resource is being used by each process?
Identify resource-intensive processes
Which program components are heavily utilizing the CPU and GPU?
Detect Inefficient Code
Which processes have high resource consumption but low impact on the results?
Explore improvements and accelerate performance by identifying bottlenecks from monitoring results.
Replacing components with appropriate software libraries
Adjusting parameters effectively
Applying parallelization methods
By using monitoring results after the speed improvements as feedback, we can continuously iterate on the acceleration cycle.
Case Study: Accelerating Megatron-LM, a Continuous Pre-training Library for Enhancing LLMs with Specialized Knowledge
If you’d like to know more about its features and value
Download the Brochure
