Composable Interfaces for Parallel Processing in Apache Spark & Weld

The main reason people are productive writing software is composability -- engineers can take libraries and functions written by other developers and easily combine them into a program. However, composability has taken a back seat in early parallel processing APIs. For example, composing MapReduce jobs required writing the output of every job to a file, which is both slow and error-prone. Apache Spark helped simplify cluster programming largely because it enabled efficient composition of parallel functions, leading to a large standard library and high-level APIs in various languages. In this talk, I'll explain how composability has evolved in Spark's newer APIs, and also present a new research project I'm leading at Stanford called Weld to enable much more efficient composition of software on emerging parallel hardware (multicores, GPUs, etc).