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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)
—Two of the most commonly used hashing strategies—linear probing and hashing with chaining—are adapted for efficient execution on a Cray XMT. These strategies are designed to minimize memory contention. Datasets that follow a power law distribution cause significant performance challenges to shared memory parallel hashing implementations. Experimental(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)