Eric Hielscher’s research while affiliated with The Graduate Center, CUNY and other places

Productivity languages such as NumPy and Matlab make it much easier to implement data-intensive numerical algorithms. However, these languages can be intolerably slow for programs that don't map well to their built-in primitives. In this paper, we discuss locality optimizations for our system Parakeet, a just-in-time compiler and runtime system for an array-oriented subset of Python. Parakeet dynamically compiles whole user functions to high performance multi-threaded native code. Parakeet makes extensive use of the classic data parallel operators Map, Reduce, and Scan. We introduce a new set of data parallel operators,TiledMap, TiledReduce, and TiledScan, that break up their computations into local pieces of bounded size so as better to make use of small fast memories. We introduce a novel tiling transformation to generate tiled operators automatically. Applying this transformation once tiles the program for cache, and applying it again enables tiling for registers. The sizes for cache tiles are left unspecified until runtime, when an autotuning search is performed. Finally, we evaluate our optimizations on benchmarks and show significant speedups on programs that exhibit data locality.

High level productivity languages such as Python or Matlab enable the use of computational resources by non-expert programmers. However, these languages often sac-rifice program speed for ease of use. This paper proposes Parakeet, a library which provides a just-in-time (JIT) parallel accelerator for Python. Parakeet bridges the gap between the usability of Python and the speed of code written in efficiency languages such as C++ or CUDA. Parakeet accelerates data-parallel sections of Python that use the standard NumPy scientific comput-ing library. Parakeet JIT compiles efficient versions of Python functions and automatically manages their execu-tion on both GPUs and CPUs. We assess Parakeet on a pair of benchmarks and achieve significant speedups.