cphVB: A System for Automated Runtime Optimization and Parallelization of Vectorized Applications

TL;DR

cphVB introduces an abstract machine framework that translates high-level vector operations into bytecode, enabling efficient execution on diverse modern architectures while maintaining abstraction from low-level details.

Contribution

The paper presents cphVB, a novel system that bridges high-level vector languages and hardware-optimized execution through an intermediate bytecode approach.

Findings

Good performance on multi-core CPU architectures

Effective mapping of high-level vector operations to hardware

Maintains abstraction while optimizing execution

Abstract

Modern processor architectures, in addition to having still more cores, also require still more consideration to memory-layout in order to run at full capacity. The usefulness of most languages is deprecating as their abstractions, structures or objects are hard to map onto modern processor architectures efficiently. The work in this paper introduces a new abstract machine framework, cphVB, that enables vector oriented high-level programming languages to map onto a broad range of architectures efficiently. The idea is to close the gap between high-level languages and hardware optimized low-level implementations. By translating high-level vector operations into an intermediate vector bytecode, cphVB enables specialized vector engines to efficiently execute the vector operations. The primary success parameters are to maintain a complete abstraction from low-level details and to provide efficient code execution across different, modern, processors. We evaluate the presented design through a setup that targets multi-core CPU architectures. We evaluate the performance of the implementation using Python implementations of well-known algorithms: a jacobi solver, a kNN search, a shallow water simulation and a synthetic stencil simulation. All demonstrate good performance.