Learning representations is critical for machine learning and very computation-intensive process. There are many opportunities to introduce efficiencies through parallel and distributedcomputing. The success of machi...
Learning representations is critical for machine learning and very computation-intensive process. There are many opportunities to introduce efficiencies through parallel and distributedcomputing. The success of machine learning algorithms depends on data representation. Different representations can expose or hide different features of the data. As we think about the future of learning representations and its impact on machine learning it is important to consider the state of the art of machine learning today, the challenges and opportunities for addressing the computation issues around representation learning, and how to get to deeper understanding and more capabilities in machine learned models. In this talk I will describe 4 ideas related to computational issues in representation learning: reducing uncertainty, developing compact representations, debasing the training data, and developing privacy-preserving representations.
Summary form only given. This paper describes the use of the "Plan 9 from Bell Labs " distributed operating system as a grid computing infrastructure. In particular it compares solutions using the de facto s...
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Summary form only given. This paper describes the use of the "Plan 9 from Bell Labs " distributed operating system as a grid computing infrastructure. In particular it compares solutions using the de facto standard middleware toolkit for grids, Globus, to an environment constructed using computers running the Plan 9 operating system. These environments are compared based on the features they offer in the context of grid computing: authentication, security, data management, and resource discovery.
Two recent curriculum studies, the ACM/IEEE Curricula 2013 Report and the NSF/IEEE-TCPP Curriculum Initiative on parallel and distributedcomputing, argue that every undergraduate computer science program should inclu...
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ISBN:
(纸本)9781479941155
Two recent curriculum studies, the ACM/IEEE Curricula 2013 Report and the NSF/IEEE-TCPP Curriculum Initiative on parallel and distributedcomputing, argue that every undergraduate computer science program should include topics in parallel and distributedcomputing (PDC). Although not within the scope of these reports, there is also a need for students in computing related general education courses to be aware of the role that parallel and distributedcomputing technologies play in the computing landscape. One approach to integrating these topics into existing curricula is to spread them across several courses. However, this approach requires development of multiple instructional modules targeted to introduce PDC concepts at specific points in the curriculum. Such modules need to mesh with the goals of the courses for which they are designed in such a way that minimal material has to be removed from existing topics. At the same time the modules should provide students with an understanding of and experience employing fundamental PDC concepts. In this paper we report on our experience developing and deploying such modules.
A novel approach is presented to teach the parallel and distributedcomputing concepts of synchronization and remote memory access. The single program multiple data (SPMD) partitioned global address space (PGAS) model...
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A novel approach is presented to teach the parallel and distributedcomputing concepts of synchronization and remote memory access. The single program multiple data (SPMD) partitioned global address space (PGAS) model presented in this paper uses a procedural programming language appealing to undergraduate students. We propose that the amusing nature of the approach may engender creativity and interest using these concepts later in more sober environments. Specifically, we implement parallel extensions to LOLCODE within a source-to-source compiler sufficient for the development of parallel and distributed algorithms normally implemented using conventional high-performance computing languages and APIs.
Merge sort is useful in sorting a great number of data progressively, especially when they can be partitioned and easily collected to a few processors. Merge sort can be parallelized, however, conventional algorithms ...
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The ability to design effective solutions using parallel processing should be a required competency for every computing student. However, teaching parallel concepts is sometimes challenging and costly, specially at ea...
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ISBN:
(纸本)9781509036837
The ability to design effective solutions using parallel processing should be a required competency for every computing student. However, teaching parallel concepts is sometimes challenging and costly, specially at early stages of a computer science degree. For such reasons we present a set of modules to teach parallelcomputing paradigms using as examples problems that are computationally intensive, but easy to understand and can be easily implemented using the Python parallelization libraries MPI for Python and Disco.
We present performance results on a Windows cluster with up to 768 cores using MPI and two variants of threading - CCR and TPL. CCR (Concurrency and Coordination Runtime) presents a message based interface while TPL (...
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