Driven by strong interests from our open source community, the Alluxio core engineering team re-designed things to come up with a more efficient and transparent way for users to leverage data orchestration through the POSIX interface. This enables much better performance for ML workloads where data is accessed via the POSIX interface.
In this 20 minute community session, you’ll hear from Lu Qiu, one of Alluxio’s lead engineers on the POSIX implementation project.
In this session, you’ll learn:
- How Alluxio’s new JNI-based FUSE implementation supports more efficient POSIX data access
- How improvements to multiple data operations, including distributedLoad, optimizations on listing or calculating directories with a massive amounts of files, etc., improve performance. In model training
- How these latest enhancements improve performance on TensorFlow and PyTorch training workloads, even with GPU-based training and compute
Lu Qiu has been involved in open source software for many years and is currently a software engineer at Alluxio. Lu develops easier ways for Alluxio integration in the public cloud environment. Lu is mainly responsible for leader election, journal management, metrics management, and big data preparation for machine learning workloads. Lu receives an M.S. degree from George Washington University in Data Science.