Over the past decade, the bulk synchronous processing (BSP) model has been 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 systems which provides both task-parallel and actor-like 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 the early experience with using Ray to implement a variety of applications.
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