http://app.cul.columbia.edu:8080/ac/handle/10022/AC:P:29135 Search the Channel search http://app.cul.columbia.edu:8080/ac/simple-search http://app.cul.columbia.edu:8080/ac/handle/10022/AC:P:29619 Title: Example application under PRET environment -- Programming a MultiMediaCard <br/> <br/>Author(s): Dedhia, Devesh <br/> <br/>Abstract: PRET philosophy proposes the temporal characteristics to be made predictable. However for various applications the PRET processor will have to interact with a non predictable environment. In this paper an example of one such environment, an MultiMediaCard (MMC) is considered. This paper illustrates a method to make the response of the MMC predictable. Wed, 21 Jan 2009 22:58:59 GMT http://app.cul.columbia.edu:8080/ac/handle/10022/AC:P:29618 Title: Improving the Quality of Computational Science Software by Using Metamorphic Relations to Test Machine Learning Applications <br/> <br/>Author(s): Xie, Xiaoyuan; Ho, Joshua; Murphy, Christian; Kaiser, Gail E.; Xu, Baowen; Chen, T.Y. <br/> <br/>Abstract: Many applications in the field of scientific computing - such as computational biology, computational linguistics, and others - depend on Machine Learning algorithms to provide important core functionality to support solutions in the particular problem domains. However, it is difficult to test such applications because often there is no 'test oracle' to indicate what the correct output should be for arbitrary input. To help address the quality of scientific computing software, in this paper we present a technique for testing the implementations of machine learning classification algorithms on which such scientific computing software depends. Our technique is based on an approach called 'metamorphic testing', which has been shown to be effective in such cases. In addition to presenting our technique, we describe a case study we performed on a real-world machine learning application framework, and discuss how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also discuss how our findings can be of use to other areas of computational science and engineering. Sun, 18 Jan 2009 22:58:59 GMT http://app.cul.columbia.edu:8080/ac/handle/10022/AC:P:29617 Title: Retrocomputing on an FPGA: Reconstructing an 80's-Era Home Computer with Programmable Logic <br/> <br/>Author(s): Edwards, Stephen A. <br/> <br/>Abstract: The author reconstructs a computer of his childhood, an Apple II+. Sun, 11 Jan 2009 22:58:59 GMT http://app.cul.columbia.edu:8080/ac/handle/10022/AC:P:29616 Title: Rapid Parallelization by Collaboration <br/> <br/>Author(s): Sethumadhavan, Simha; Kaiser, Gail E. <br/> <br/>Abstract: The widespread adoption of Chip Multiprocessors has renewed the emphasis on the use of parallelism to improve performance. The present and growing diversity in hardware architectures and software environments, however, continues to pose difficulties in the effective use of parallelism thus delaying a quick and smooth transition to the concurrency era. In this document, we describe the research being conducted at the Computer Science Department at Columbia University on a system called COMPASS that aims to simplify this transition by providing advice to programmers considering parallelizing their code. The advice proffered to the programmer is based on the wisdom collected from programmers who have already parallelized some code. The utility of COMPASS rests, not only on its ability to collect the wisdom unintrusively but also on its ablility to *automatically* seek, find and synthesize this wisdom into advice that is tailored to the code the user is considering parallelizing and to the environment in which the optimized program will execute in. COMPASS provides a platform and an extensible framework for sharing human expertise about code parallelization -- widely and on diverse hardware and software. By leveraging the ``Wisdom of Crowds' model which has been conjunctured to scale exponentially and which has successfully worked for Wikis, COMPASS aims to enable *rapid* parallelization of code and thus continue to extend the benefits for Moore's law scaling to science and society. Wed, 31 Dec 2008 22:58:59 GMT