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Large-scale 1-regularized loss minimization problems arise in high-dimensional applications such as compressed sensing and high-dimensional supervised learning , including classification and regression problems. High-performance algorithms and implementations are critical to efficiently solving these problems. Building upon previous work on coordinate(More)
To-date, the application of high-performance computing resources to Semantic Web data has largely focused on commodity hardware and distributed memory platforms. In this paper we make the case that more specialized hardware can offer superior scaling and close to an order of magnitude improvement in performance. In particular we examine the Cray XMT. Its(More)
A tournament is a complete directed graph. A convex subset is a vertex subset with the property that every two-path beginning and ending inside the convex subset is contained completely within the subset. This paper shows that every nontrivial convex subset is the closure of a subset of vertices of cardinality two. This result leads to algorithms that nd(More)
We present a generic framework for parallel coordinate descent (CD) algorithms that includes, as special cases, the original sequential algorithms Cyclic CD and Stochastic CD, as well as the recent parallel Shotgun algorithm. We introduce two novel parallel algorithms that are also special cases—Thread-Greedy CD and Coloring-Based CD—and give performance(More)
We present SUDA2, a recursive algorithm for finding minimal sample uniques (MSUs). SUDA2 uses a novel method for representing the search space for MSUs and new observations about the properties of MSUs to prune and traverse this space. Experimental comparisons with previous work demonstrate that SUDA2 is not only several orders of magnitude faster but is(More)