
Provide multiple tools that target a wide range of AI partitioners: At Meta, there are engineers with different backgrounds who may need to tune their AI workload performance. Some of them are AI experts while others are general software engineers. Therefore, MAIProf provides a variety of tools for different levels of performance debugging, from high-level automatic trace comprehension to low-level trace analysis.
Support distributed GPU profiling: MAIProf can collect profiling data from multiple hosts, each with multiple GPUs. It then shows a combined view/analysis of the entire system.
Highly scalable: MAIProf is built as a service on top of existing infrastructures in Meta data centers such as a scalable storage system called Manifold. Its profiling capability can be easily scaled by adding more machines in the service pool with the increase of workloads.

2. Case Study: Optimizing a Protection PyTorch Model