The COVID-19 High-Performance Computing Consortium.

In March of 2020, recognizing the potential of High Performance Computing (HPC) to accelerate understanding and the pace of scientific discovery in the fight to stop COVID-19, the HPC community assembled the largest collection of worldwide HPC resources to enable COVID-19 researchers worldwide to advance their critical efforts. Amazingly, the COVID-19 HPC Consortium was formed within one week through the joint effort of the Office of Science and Technology Policy (OSTP), the U.S.

Content-based histopathology image retrieval using CometCloud.

Background: The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible.

The analysis of image feature robustness using cometcloud.

The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above.

Investigating the Use of Cloudbursts for High-Throughput Medical Image Registration.

This paper investigates the use of clouds and autonomic cloudbursting to support a medical image registration. The goal is to enable a virtual computational cloud that integrates local computational environments and public cloud services on-the-fly, and support image registration requests from different distributed researcher groups with varied computational requirements and QoS constraints. The virtual cloud essentially implements shared and coordinated task-spaces, which coordinates the scheduling of jobs submitted by a dynamic set of research groups to their local job queues.

Asynchronous replica exchange for molecular simulations.

An asynchronous implementation of the replica exchange method that addresses some of the limitations of conventional synchronous replica exchange implementations is presented. In asynchronous replica exchange pairs of processors initiate and perform temperature replica exchanges independently from the other processors, thereby removing the need for processor synchronization found in conventional synchronous implementations. Illustrative calculations on a molecular system are presented that show that asynchronous replica exchange, contrary to the synchronous implementation, is able to utilize at nearly top efficiency loosely coupled pools of processors with heterogeneous speeds, such as those found in computational grids and CPU scavenging environments.

Decentralized data sharing of tissue microarrays for investigative research in oncology.

Tissue microarray technology (TMA) is a relatively new approach for efficiently and economically assessing protein and gene expression across large ensembles of tissue specimens. Tissue microarray technology holds great potential for reducing the time and cost associated with conducting research in tissue banking, proteomics, and outcome studies. However, the sheer volume of images and other data generated from even limited studies involving tissue microarrays quickly approach the processing capacity and resources of a division or department.