.png){kind=icon}
.png){kind=icon}
Return
Scalable Multimedia Content Analysis on Parallel Platforms Using Python
.png){kind=icon}
PRE
AI
DOI：10.1145/2517151.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 illustrate our approach, we present three multimedia content analysis applications that use our framework: a state-of-the-art speaker diarization application, a content-based music recommendation system based on the Million Song Dataset, and a video event detection system for consumer-produced videos. We show that across this wide range of applications, our approach achieves the goal of automatic portability and scalability while at the same time allowing easy prototyping in a high-level language and efficient performance of low-level optimized code.
Keywords:
Experimentation
Performance
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.0K
Citations:
5.4K
