© 2020 Strange Loop
Over the past decade, the bulk synchronous processing (BSP) model has proven highly effective for processing large amounts of data. However, today we are witnessing the emergence of a new class of applications, i.e., AI workloads. These applications exhibit new requirements, such as nested parallelism and highly heterogeneous computations. To support such workloads, we have developed Ray, a distributed system which provides both task-parallel and actor abstractions. Ray is highly scalable employing an in-memory storage system and a distributed scheduler. In this talk, I will discuss some of our design decisions and our early experience with using Ray to implement a variety of applications.
Stephanie is a PhD student in computer systems at UC Berkeley. She's interested in building robust and usable distributed systems for the average developer and likes provable guarantees.
Robert Nishihara is a PhD student at UC Berkeley. He works on machine learning, optimization, and distributed systems.