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Scalable Multimedia Content Analysis on Parallel Platforms Using Python by Ekaterina Gonina; Gerald Friedland; Eric Battenberg; Penporn Koanantakool; Michael Driscoll; Evangelos Georganas; Kurt Keutzer is a Computer Science article available to read on EtoBox.

What is Scalable Multimedia Content Analysis on Parallel Platforms Using Python about?

In this new era dominated by consumer-produced media there is a high demand for web-scalable solutions to multimedia content analysis. A compelling approach to making applications scalable is to explicitly map their computation onto parallel platforms. However, developing efficient parallel implementations and fully utilizing the available resources remains a challenge due to the increased code complexity, limited portability and required low-level knowledge of the underlying hardware. In this article, we present PyCASP, a Python-based framework that automatically maps computation onto parallel platforms from Python application code to a variety of parallel platforms. PyCASP is designed using a systematic, pattern-oriented approach to offer a single software development environment for multimedia content analysis applications. Using PyCASP, applications can be prototyped in a couple hundred lines of Python code and automatically scale to modern parallel processors. Applications written with PyCASP are portable to a variety of parallel platforms and efficiently scale from a single desktop Graphics Processing Unit (GPU) to an entire cluster with a small change to application code. To

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Author

Ekaterina Gonina; Gerald Friedland; Eric Battenberg; Penporn Koanantakool; Michael Driscoll; Evangelos Georganas; Kurt Keutzer

Publisher

ACM

Published

2014

Language

EN

Field

Computer Science (Physical Sciences)