Estimating the Potential Speedup of Computer Vision Applications on Embedded Multiprocessors

TL;DR

This paper introduces a fast, trace-driven simulation method for early performance prediction of OpenMP-based computer vision applications on embedded multiprocessors, aiding developers in optimizing parallelization strategies.

Contribution

It presents Parana, a new simulation tool that accurately estimates parallel performance from sequential traces with significantly reduced modeling effort and faster execution.

Findings

Achieves less than 10% error margin compared to detailed simulators.

Runs up to 20 times faster than reference cycle-approximate simulators.

Effectively guides parallelization decisions for embedded computer vision applications.

Abstract

Computer vision applications constitute one of the key drivers for embedded multicore architectures. Although the number of available cores is increasing in new architectures, designing an application to maximize the utilization of the platform is still a challenge. In this sense, parallel performance prediction tools can aid developers in understanding the characteristics of an application and finding the most adequate parallelization strategy. In this work, we present a method for early parallel performance estimation on embedded multiprocessors from sequential application traces. We describe its implementation in Parana, a fast trace-driven simulator targeting OpenMP applications on the STMicroelectronics' STxP70 Application-Specific Multiprocessor (ASMP). Results for the FAST key point detector application show an error margin of less than 10% compared to the reference cycle-approximate simulator, with lower modeling effort and up to 20x faster execution time.