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This paper presents {scshape PaSh}, a system for parallelizing POSIX shell scripts. Given a script, {scshape PaSh} converts it to a dataflow graph, performs a series of semantics-preserving program transformations that expose parallelism, and then converts the dataflow graph back into a script -- one that adds POSIX constructs to explicitly guide parallelism coupled with {scshape PaSh}-provided {scshape Unix}-aware runtime primitives for addressing performance- and correctness-related issues. A lightweight annotation language allows command developers to express key parallelizability properties about their commands. An accompanying parallelizability study of POSIX and GNU commands -- two large and commonly used groups -- guides the annotation language and optimized aggregator library that {scshape PaSh} uses. Finally, {scshape PaSh}s {scshape PaSh}s extensive evaluation over 44 unmodified {scshape Unix} scripts shows significant speedups ($0.89$--$61.1times$, avg: $6.7times$) stemming from the combination of its program transformations and runtime primitives.
High-level programming languages such as Python are increasingly used to provide intuitive interfaces to libraries written in lower-level languages and for assembling applications from various components. This migration towards orchestration rather t
In recent years the computing landscape has seen an in- creasing shift towards specialized accelerators. Field pro- grammable gate arrays (FPGAs) are particularly promising as they offer significant performance and energy improvements compared to CPU
Constraint Handling Rules (CHR) is a declarative rule-based formalism and language. Concurrency is inherent as rules can be applied to subsets of constraints in parallel. Parallel implementations of CHR, be it in software, be it in hardware, use diff
Data-intensive applications are becoming commonplace in all science disciplines. They are comprised of a rich set of sub-domains such as data engineering, deep learning, and machine learning. These applications are built around efficient data abstrac
This paper presents the design, implementation, and evaluation of the PyTorch distributed data parallel module. PyTorch is a widely-adopted scientific computing package used in deep learning research and applications. Recent advances in deep learning