We survey parallel programming models and languages using six criteria to assess their suitability for realistic portable parallel programming. We argue that an ideal model should by easy to program, should have a software development methodology, should be architecture-independent, should be easy to understand, should guarantee performance, and should provide accurate information about the cost of programs. These criteria reflect our belief that developments in parallelism must be driven by a parallel software industry based on portability and efficiency. We consider programming models in six categories, depending on the level of abstraction they provide. Those that are very abstract conceal even the presence of parallelism at the software level. Such models make software easy to build and port, but efficient and predictable performance is usually hard to achieve. At the other end of the spectrum, low-level models make all of the messy issues of parallel programming explicit (how many threads, how to place them, how to express communication, and how to schedule communication), so that software is hard to build and not very portable, but is usually efficient. Most recent models are near the center of this spectrum, exploring the best tradeoffs between expressiveness and performance. A few models have achieved both abstractness and efficiency. Both kinds of models raise the possibility of parallelism as part of the mainstream of computing.

No abstract

Designing, building, and implementing an architecture for distributed knowledge discovery.

No abstract

Extracting useful knowledge from huge digital datasets requires smart and scalable analytics services, programming tools, and applications.

No abstract

Recently, we have been witnessing huge advancements in the scale of data we routinely generate and collect in pretty much everything we do, as well as our ability to exploit modern technologies to process, analyze and understand this data. The intersection of these trends is what is called, nowadays, as Big Data Science. Cloud computing represents a practical and cost-effective solution for supporting Big Data storage, processing and for sophisticated analytics applications. We analyze in details the building blocks of the software stack for supporting big data science as a commodity service for data scientists. We provide various insights about the latest ongoing developments and open challenges in this domain. 1 https://prestodb.io/ 2

Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called Knowledge Grid. This paper describes the Knowledge Grid framework and presents the toolset provided by the Knowledge Grid for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the Knowledge Grid tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.