Leveraging Parallel Data Processing Frameworks with Verified Lifting

Leveraging Parallel Data Processing Frameworks with Verified Lifting

Many parallel data frameworks have been proposed in recent years that let sequential programs access parallel processing. To capitalize on the benefits of such frameworks, existing code must often be rewritten to the domain-specific languages that each framework supports. This rewriting—tedious and error-prone—also requires developers to choose the framework that best optimizes performance give...

Leveraging Object-Oriented Frameworks

Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications

MapReduce is a popular programming paradigm for running largescale data-intensive computation. Recently, many frameworks that implement that paradigm have been developed. To leverage such frameworks, however, developers need to familiarize with each framework’s API and rewrite their code. We present Casper, a new tool that automatically translates sequential Java programs to the MapReduce parad...

Comparing Data Processing Frameworks for Scalable Clustering

Recent advances in the development of data parallel platforms have provided a significant thrust to the development of scalable data mining algorithms for analyzing massive data sets that are now commonplace. Scalable clustering is a common data mining task that finds consequential applications in astronomy, biology, social network analysis and commercial domains. The variety of platforms avail...

Leveraging Non-Uniform Resources for Parallel Query Processing

Clusters are now composed of non-uniform nodes with different CPUs, disks or network cards so that customers can adapt the cluster configuration to the changing technologies and to their changing needs. Such modular clusters challenge parallel databases. The load balancing techniques used by existing parallel databases partition data across a set of nodes that all run the same relational operat...