Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures

This article describes advances in statistical computation for large-scale data analysis in structured Bayesian mixture models via graphics processing unit (GPU) programming. The developments are partly motivated by computational challenges arising in fitting models of increasing heterogeneity to increasingly large datasets. An example context concerns common biological studies using high-throughput technologies generating many, very large datasets and requiring increasingly high-dimensional mixture models with large numbers of mixture components.We outline important strategies and processes for GPU computation in Bayesian simulation and optimization approaches, give examples of the benefits of GPU implementations in terms of processing speed and scale-up in ability to analyze large datasets, and provide a detailed, tutorial-style exposition that will benefit readers interested in developing GPU-based approaches in other statistical models. Novel, GPU-oriented approaches to modifying existing algorithms software design can lead to vast speed-up and, critically, enable statistical analyses that presently will not be performed due to compute time limitations in traditional computational environments. Supplementalmaterials are provided with all source code, example data, and details that will enable readers to implement and explore the GPU approach in this mixture modeling context. © 2010 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Reason, risk, and reward: Models for libraries and other stakeholders in an evolving scholarly publishing ecosystem

Scholarly publishing, and scholarly communication more generally, are based on patterns established over many decades and even centuries. Some of these patterns are clearly valuable and intimately related to core values of the academy, but others were based on the exigencies of the past, and new opportunities have brought into question whether it makes sense to persist in supporting old models. New technologies and new publishing models raise the question of how we should fund and operate scholarly publishing and scholarly communication in the future, moving away from a scarcity model based on the exchange of physical goods that restricts access to scholarly literature unless a market-based exchange takes place. This essay describes emerging models that attempt to shift scholarly communication to a more open-access and mission-based approach and that try to retain control of scholarship by academics and the institutions and scholarly societies that support them. It explores changing practices for funding scholarly journals and changing services provided by academic libraries, changes instituted with the end goal of providing more access to more readers, stimulating new scholarship, and removing inefficiencies from a system ready for change. © 2014 by the American Anthropological Association.

Contrast in intracardiac acoustic radiation force impulse images of radiofrequency ablation lesions.

We have previously shown that intracardiac acoustic radiation force impulse (ARFI) imaging visualizes tissue stiffness changes caused by radiofrequency ablation (RFA). The objectives of this in vivo study were to (1) quantify measured ARFI-induced displacements in RFA lesion and unablated myocardium and (2) calculate the lesion contrast (C) and contrast-to-noise ratio (CNR) in two-dimensional ARFI and conventional intracardiac echo images. In eight canine subjects, an ARFI imaging-electroanatomical mapping system was used to map right atrial ablation lesion sites and guide the acquisition of ARFI images at these sites before and after ablation. Readers of the ARFI images identified lesion sites with high sensitivity (90.2%) and specificity (94.3%) and the average measured ARFI-induced displacements were higher at unablated sites (11.23 ± 1.71 µm) than at ablated sites (6.06 ± 0.94 µm). The average lesion C (0.29 ± 0.33) and CNR (1.83 ± 1.75) were significantly higher for ARFI images than for spatially registered conventional B-mode images (C = -0.03 ± 0.28, CNR = 0.74 ± 0.68).