In recent years a new category of data analysis applications have evolved, known as data pipelining tools, which enable even nonexperts to perform complex analysis tasks on potentially huge amounts of data. Due to the complex and computing intensive analysis processes and methods used, it is often neither sufficient nor possible to simply rely on the increase of performance of single processors. Promising solutions to this problem are parallel and distributed approaches that can accelerate the analysis process. In this paper we discuss the parallel and distribution potential of pipelining tools by demonstrating several parallel and distributed implementations in the open source pipelining platform KNIME. We verify the practical applicabilit...
This paper considers an analytic data distribution for improving the performance of host-client type...
DIY2 is a programming model and runtime for block-parallel analytics on distributed-memory machines....
Abstract. Large-scale parallel data analysis, where global information from a variety of problem dom...
In recent years a new category of data analysis applications have evolved, known as data pipelining ...
[[abstract]]The basic concept of piplined data-parallel algorithms is introduced by contrasting the ...
Due to the character of the original source materials and the nature of batch digitization, quality ...
Over the last decades a large number of performance tools has been developed to analyze and optimize...
Parallel computers can provide impressive speedups, but unfortunately such speedups are difficult to...
Multi-core processors are now ubiquitous and are widely seen as the most viable means of delivering ...
This dissertation addresses creating portable and efficient parallel programs for scientific computi...
Generalizable approaches, models, and frameworks for irregular application scalability is an old yet...
The demand for ever-growing computing capabilities in scientific computing and simulation has led to...
[[abstract]]A systematic procedure for designing pipelined data-parallel algorithms that are suitabl...
It has become common knowledge that parallel programming is needed for scientific applications, part...
This thesis presents a parallel implementation of data streaming algorithms for multiple streams. Th...
This paper considers an analytic data distribution for improving the performance of host-client type...
DIY2 is a programming model and runtime for block-parallel analytics on distributed-memory machines....
Abstract. Large-scale parallel data analysis, where global information from a variety of problem dom...
In recent years a new category of data analysis applications have evolved, known as data pipelining ...
[[abstract]]The basic concept of piplined data-parallel algorithms is introduced by contrasting the ...
Due to the character of the original source materials and the nature of batch digitization, quality ...
Over the last decades a large number of performance tools has been developed to analyze and optimize...
Parallel computers can provide impressive speedups, but unfortunately such speedups are difficult to...
Multi-core processors are now ubiquitous and are widely seen as the most viable means of delivering ...
This dissertation addresses creating portable and efficient parallel programs for scientific computi...
Generalizable approaches, models, and frameworks for irregular application scalability is an old yet...
The demand for ever-growing computing capabilities in scientific computing and simulation has led to...
[[abstract]]A systematic procedure for designing pipelined data-parallel algorithms that are suitabl...
It has become common knowledge that parallel programming is needed for scientific applications, part...
This thesis presents a parallel implementation of data streaming algorithms for multiple streams. Th...
This paper considers an analytic data distribution for improving the performance of host-client type...
DIY2 is a programming model and runtime for block-parallel analytics on distributed-memory machines....
Abstract. Large-scale parallel data analysis, where global information from a variety of problem dom...