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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.