SMASH comprises several components that automatically map computations that typically occur in content analysis to parallel platforms from Python code.

Audio and Multimedia researchers at ICSI are starting to work on a new big data project that aims to provide the scalability of diverse parallel processing at the productivity level of high-level languages.

The new SMASH project (Scalable Multimedia content AnalysiS in a High-level language) is a collaboration between researchers at ICSI and UCB and is funded by a grant from the National Science Foundation (NSF).

Using multimedia as an example, the researchers are developing tools for high-level analysis of large amounts of data. "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."