24 research outputs found

    Large-Scale Structured Sparsity via Parallel Fused Lasso on Multiple GPUs

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    <p>We present a massively parallel algorithm for the fused lasso, powered by a multiple number of graphics processing units (GPUs). Our method is suitable for a class of large-scale sparse regression problems on which a two-dimensional lattice structure among the coefficients is imposed. This structure is important in many statistical applications, including image-based regression in which a set of images are used to locate image regions predictive of a response variable such as human behavior. Such large datasets are increasingly common. In our study, we employ the split Bregman method and the fast Fourier transform, which jointly have a high data-level parallelism that is distinct in a two-dimensional setting. Our multi-GPU parallelization achieves remarkably improved speed. Specifically, we obtained as much as 433 times improved speed over that of the reference CPU implementation. We demonstrate the speed and scalability of the algorithm using several datasets, including 8100 samples of 512 Ă— 512 images. Compared to the single GPU counterpart, our method also showed improved computing speed as well as high scalability. We describe the various elements of our study as well as our experience with the subtleties in selecting an existing algorithm for parallelization. It is critical that memory bandwidth be carefully considered for multi-GPU algorithms. Supplementary material for this article is available online.</p

    High-Dimensional Fused Lasso Regression Using Majorization–Minimization and Parallel Processing

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    <div><p>In this article, we propose a majorization–minimization (MM) algorithm for high-dimensional fused lasso regression (FLR) suitable for parallelization using graphics processing units (GPUs). The MM algorithm is stable and flexible as it can solve the FLR problems with various types of design matrices and penalty structures within a few tens of iterations. We also show that the convergence of the proposed algorithm is guaranteed. We conduct numerical studies to compare our algorithm with other existing algorithms, demonstrating that the proposed MM algorithm is competitive in many settings including the two-dimensional FLR with arbitrary design matrices. The merit of GPU parallelization is also exhibited. Supplementary materials are available online.</p></div

    Influence of Initial Treatment Modality on Long-Term Control of Chronic Idiopathic Urticaria

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    <div><p>Background</p><p>Chronic idiopathic urticaria (CIU) is a common cutaneous disorder but the influence of initial treatment modality on long-term control is not known. The aim of this study was to evaluate clinical features, and the influence of initial treatment modality on long-term control.</p><p>Methods and Results</p><p>641 CIU patients were enrolled from the allergy clinic in a tertiary referral hospital. Disease duration, aggravating factors and treatment modality at each visit were evaluated. Times required to reach a controlled state were analyzed according to initial treatment modality, using Kaplan-Meier survival curves, the Cox proportional-hazards model, and propensity scores. Female to male ratio was 1.7: 1; mean age at onset was 40.5 years. The most common aggravating factors were food (33.5%), stress (31.5%) and fatigue (21.6%). Most patients (82.2%) used H1-antihistamines alone as initial treatment while 17% used a combination treatment with oral corticosteroids. There was no significant difference in the time taken to reach a controlled state between patients treated with single vs multiple H1-antihistamines or between those who received H1-antihistamine monotherapy vs. a combination therapy with oral corticosteroids.</p><p>Conclusion</p><p>The time required to control CIU is not reduced by use of multiple H1-antihistamines or oral corticosteroids in the initial treatment.</p></div
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