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There is a growing utilization gap between modern hardware and modern programming languages for data analysis.Due to power and other constraints, recent processor design has sought improved performance through increased SIMD and multi-core parallelism. At the same time, high-level, dynamically-typed languages for data analysis have become popular. These languages emphasize ease of use and high productivity, but have, in general, low performance and limited support for exploiting hardware parallelism.

In this paper, we describe Riposte, a new runtime for the R language, which bridges this gap. Riposte uses tracing, a technique commonly used to accelerate scalar code, to dynamically discover and extract sequences of vector operations from arbitrary R code. Once extracted, we can fuse traces to eliminate unnecessary memory traffic, compile them to use hardware SIMD units, and schedule them …