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大规模制作多媒体内容分析算法原型的任务。通常，科学家和工程师更喜欢使用高级编程语言进行实验，因为它们可以快速实现一个新的想法。然而，大数据上的实验往往是计算密集型的，因此最终必须由专家程序员重新编写成一种低级语言，以实现足够的性能，从而在生产率和性能之间造成差距。此外，可能存在多种策略，用于根据输入数据大小和硬件参数将问题映射到并行硬件上，从而进一步加剧问题。以多媒体内容分析应用领域为例(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之一)，该项目对使用分层并行编程方法的面向模式的、特定于应用的专业化框架进行研究。最终目的是在高级语言的生产力水平上提供多样化并行处理的可扩展性。社交媒体视频越来越多地被用于科学研究，因为它们允许我们观察和模拟许多研究的现象，例如在社会科学、经济学、气象学和医学中。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日常生活的一部分。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://smash.icsi.berkeley.edu).)上查阅

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).