Towards developing robust algorithms for solving partial differential equations on MIMD machines

Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system

Performance tradeoffs in static and dynamic load balancing strategies

The problem of uniformly distributing the load of a parallel program over a multiprocessor system was considered. A program was analyzed whose structure permits the computation of the optimal static solution. Then four strategies for load balancing were described and their performance compared. The strategies are: (1) the optimal static assignment algorithm which is guaranteed to yield the best static solution, (2) the static binary dissection method which is very fast but sub-optimal, (3) the greedy algorithm, a static fully polynomial time approximation scheme, which estimates the optimal solution to arbitrary accuracy, and (4) the predictive dynamic load balancing heuristic which uses information on the precedence relationships within the program and outperforms any of the static methods. It is also shown that the overhead incurred by the dynamic heuristic is reduced considerably if it is started off with a static assignment provided by either of the other three strategies

Implementation of a parallel unstructured Euler solver on shared and distributed memory architectures

An efficient three dimensional unstructured Euler solver is parallelized on a Cray Y-MP C90 shared memory computer and on an Intel Touchstone Delta distributed memory computer. This paper relates the experiences gained and describes the software tools and hardware used in this study. Performance comparisons between two differing architectures are made

caGrid-Enabled caBIGTM Silver Level Compatible Head and Neck Cancer Tissue Database System

There are huge amounts of biomedical data generated by research labs in each cancer institution. The data are stored in various formats and accessed through numerous interfaces. It is very difficult to exchange and integrate the data among different cancer institutions, even among different research labs within the same institution, in order to discover useful biomedical knowledge for the healthcare community. In this paper, we present the design and implementation of a caGrid-enabled caBIGTM silver level compatible head and neck cancer tissue database system. The system is implemented using a set of open source software and tools developed by the NCI, such as the caCORE SDK and caGrid. The head and neck cancer tissue database system has four interfaces: Web-based, Java API, XML utility, and Web service. The system has been shown to provide robust and programmatically accessible biomedical information services that syntactically and semantically interoperate with other resources

Эффективность капецитабина по сравнению с 5-фторурацилом при раке толстой кишки и желудка: обновленный метаанализ выживаемости в шести клинических исследованиях

Оральный фторпиримидин — капецитабин — широко изучен в сравнительных исследованиях с вводимым внутривенно 5-фторурацилом как монотерапевтическое средство или в комплексном приме- нении при метастатическом колоректальном раке (МКРР) и метастатическом раке желудка (МРЖ). По рекомендации Европейских органов здравоохранения выполнен метаанализ эффективности применения капецитабина по сравнению с 5-фторурацилом при МКРР и МРЖ

Demystifying Data Science Projects: A Look on the People and Process of Data Science Today

Peer reviewe

GridIMAGE: A Novel Use of Grid Computing to Support Interactive Human and Computer-Assisted Detection Decision Support

This paper describes a Grid-aware image reviewing system (GridIMAGE) that allows practitioners to (a) select images from multiple geographically distributed digital imaging and communication in medicine (DICOM) servers, (b) send those images to a specified group of human readers and computer-assisted detection (CAD) algorithms, and (c) obtain and compare interpretations from human readers and CAD algorithms. The currently implemented system was developed using the National Cancer Institute caGrid infrastructure and is designed to support the identification of lung nodules on thoracic computed tomography. However, the infrastructure is general and can support any type of distributed review. caGrid data and analytical services are used to link DICOM image databases and CAD systems and to interact with human readers. Moreover, the service-oriented and distributed structure of the GridIMAGE framework enables a flexible system, which can be deployed in an institution (linking multiple DICOM servers and CAD algorithms) and in a Grid environment (linking the resources of collaborating research groups). GridIMAGE provides a framework that allows practitioners to obtain interpretations from one or more human readers or CAD algorithms. It also provides a mechanism to allow cooperative imaging groups to systematically perform image interpretation tasks associated with research protocols