BIGDATA: Small: DCM: DA: Collaborative Research: SMASH -- Scalable Multimedia content AnalysiS in a High-level language

这个大数据项目开发工具，以支持研究人员和开发人员在大规模的原型多媒体内容分析算法的任务。通常，科学家和工程师更喜欢使用高级编程语言（如Python或MATLAB）进行实验，因为它们可以快速实现新的想法。然而，对大数据的实验通常是计算密集型的，因此最终必须由专业程序员重新编码成低级语言，以实现足够的性能，从而在生产力和性能之间产生差距。此外，根据输入数据大小和硬件参数，可能存在将问题映射到并行硬件上的多种策略，从而进一步加剧了问题。以多媒体内容分析的应用领域为例（由于消费者制作的视频的稳定上传，该领域拥有最大和增长最快的数据量），该项目对面向模式的特定于应用程序的专业化框架进行研究，该框架使用分层方法进行并行编程。最终目标是在高级语言的生产力水平上提供各种并行处理的可扩展性。社交媒体视频越来越多地用于科学研究，因为它们允许我们观察和建模许多研究现象，例如，社会科学，经济学，气象学和医学。更具可扩展性的内容分析会影响任何使用社交媒体视频的领域。此外，社交媒体视频是许多人生活的日常部分。使多媒体内容分析更具可扩展性，可以让更多的学生和研究人员开发出更好的算法，从而影响许多人的生活。该框架可在项目网站（http：//www.example.com）上查阅。smash.icsi.berkeley.edu

This big data project develops tools to support researchers and developers in the task of prototyping multimedia content analysis algorithms in a large scale. Typically, scientists and engineers prefer to use high-level programming languages such as Python or MATLAB to conduct experiments, as they allow for a quick implementation of a novel idea. Experiments on big data, however, are often computationally-intensive and therefore must eventually be recoded into a low-level language by expert programmers in order to achieve sufficient performance, creating a gap between productivity and performance. In addition, multiple strategies may exist for mapping a problem onto parallel hardware depending on the input data size and the hardware parameters, further exacerbating the problem. Using the application area of multimedia content analysis as an example (an area with one of the largest and the fastest growing amounts of data due to the steady upload of consumer produced videos), this project performs research on a pattern-oriented, application-specific specialization framework that uses a tiered approach to parallel programming. The ultimate aim is to provide the scalability of diverse parallel processing at the productivity level of high-level languages.Social media videos are increasingly being used for scientific research, as they allow us to observe and model many phenomena studied, for example, in social sciences, economics, meteorology and medicine. More scalable content analysis impacts any field that uses social media videos. Moreover, social media videos are an everyday part of many people's lives. Making multimedia content analysis more scalable allows for better algorithms to be developed by more students and researchers, and therefore impacts many people's lives. The framework is made available on the project website (http://smash.icsi.berkeley.edu).