CAREER: A Runtime for Fast Data Analysis on Modern Hardware

Stanford University, Stanford CA

Investigators

Abstract

The computer revolution that continuously transformed our society throughout the past 60 years happened because every year computer processors reliably became faster. Unfortunately, this trend has stopped. New processors can no longer easily be made faster. Instead, new computer hardware uses parallelism or specialized components to achieve performance, which has made it much harder to build high-performance applications since most existing data processing systems run 10-100x slower than they could even on current processors and will have even more trouble on emerging hardware. To drive advances in information processing, computer systems that automatically map applications to emerging hardware are needed. This is a challenging intellectual problem. This project proposes "Weld", a run-time for data-intensive parallel computation on modern hardware. The project includes 2 main research thrusts: *An intermediate language (IL) for data-intensive computation that can capture common data-intensive applications but is easy to optimize for parallel hardware. This language enables mapping workloads to diverse hardware like CPUs and GPUs. *A runtime API that lets Weld dynamically optimize across different libraries used in the same program. This API will allow Weld to perform complex optimizations like loop blocking across parallel libraries, unlocking speedups not yet possible. Success of this project will result in the creation of software that automatically maps existing key data intensive applications (e.g., data analytics, machine learning and search) to emerging hardware devices and achieves a 10-100x speedup over current applications. Beyond producing new technology, this project will train the next generation of engineers in high performance processing, online teaching resources, and research mentoring for undergraduate and graduate students. Together, education and new technology may make industrial, scientific, and government users of big data 10-100x more productive and enable the next generation of knowledge-driven systems.