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Simple Summary: The study adapted an existing Web-based intervention, the Family Gene Toolkit, for Swiss and Korean families that harbor the genetic changes associated with hereditary breast and ovarian cancer syndrome. The Family Gene Toolkit encourages family communication of genetic testing results and cascade genetic testing among at-risk relatives. Feedback from 68 women with genetic changes and 31 clinicians informed the culturally sensitive adaptation of the content. The Information Technology team developed the web application that will host the intervention. Finally, a new sample of 18 women from families with hereditary breast and ovarian cancer reviewed and evaluated the adapted content and the functions of the web application. Findings support that overall, the adapted Family Gene Toolkit is well-designed, has useful information for these families, and provides interactive content and illustrative stories. The research team will test if it can increase rates of cascade testing among at-risk relatives in a subsequent randomized trial. The study adapted the Family Gene Toolkit and developed a customized web application for Swiss and Korean families harboring BRCA1 or BRCA2 pathogenic variants to support family communication of genetic testing results and promote cascade genetic testing among at-risk relatives. In the first step, narrative data from 68 women with BRCA1/BRCA2 pathogenic variants and clinician feedback informed a culturally sensitive adaptation of the content consistent with current risk management guidelines. In the second step, the Information Technology team developed the functions and the interface of the web application that will host the intervention. In the third step, a new sample of 18 women from families harboring BRCA1/BRCA2 pathogenic variants tested the acceptability and usability of the intervention using "think-aloud" interviews and a questionnaire. Participants expressed high levels of satisfaction with the intervention. They provided positive feedback for the information regarding active coping, strategies to enhance family communication, interactive elements, and illustrative stories. They reported that the information was useful and the web application was easy to navigate. Findings suggest that the Family Gene Toolkit is well-designed and can increase rates of cascade testing among at-risk relatives. Its efficacy will be tested in a subsequent randomized trial. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2007.11.715 Byline: Theodore Andronikos (a), Florina M. Ciorba (b), Panayiotis Theodoropoulos (b), Dimitrios Kamenopoulos (b), George Papakonstantinou (b) Keywords: General loops; Dynamic scheduling; Code generation; Shared and distributed memory architectures Abstract: This paper describes Cronus, a platform for parallelizing general nested loops. General nested loops contain complex loop bodies (assignments, conditionals, repetitions) and exhibit uniform loop-carried dependencies. The novelty of Cronus is twofold: (1) it determines the optimal scheduling hyperplane using the QuickHull algorithm, which is more efficient than previously used methods, and (2) it implements a simple and efficient dynamic rule (successive dynamic scheduling) for the runtime scheduling of the loop iterations along the optimal hyperplane. This scheduling policy enhances data locality and improves the makespan. Cronus provides an efficient runtime library, specifically designed for communication minimization, that performs better than more generic systems, such as Berkeley UPC. Its performance was evaluated through extensive testing. Three representative case studies are examined: the Floyd-Steinberg dithering algorithm, the Transitive Closure algorithm, and the FSBM motion estimation algorithm. The experimental results corroborate the efficiency of the parallel code. The tests show speedup ranging from 1.18 (out of the ideal 4) to 12.29 (out of the ideal 16) on distributed-systems and 3.60 (out of 4) to 15.79 (out of 16) on shared-memory systems. Cronus outperforms UPC by 5-95% depending on the test case. Author Affiliation: (a) Department of Informatics, Ionian University, 7, Tsirigoti Square, 49100 Corfu, Greece (b) Computing Systems Laboratory, Department of Electrical and Computer Engineering, National Technical University of Athens, Zografou Campus, 15773 Athens, Greece Article History: Received 6 August 2007; Revised 31 October 2007; Accepted 2 November 2007

This work is based on the seminar titled 'Resiliency in Numerical Algorithm Design for Extreme Scale Simulations' held March 1–6, 2020, at Schloss Dagstuhl, that was attended by all the authors. Advanced supercomputing is characterized by very high computation speeds at the cost of involving an enormous amount of resources and costs. A typical large-scale computation running for 48 h on a system consuming 20 MW, as predicted for exascale systems, would consume a million kWh, corresponding to about 100k Euro in energy cost for executing 1023 floating-point operations. It is clearly unacceptable to lose the whole computation if any of the several million parallel processes fails during the execution. Moreover, if a single operation suffers from a bit-flip error, should the whole computation be declared invalid? What about the notion of reproducibility itself: should this core paradigm of science be revised and refined for results that are obtained by large-scale simulation? Naive versions of conventional resilience techniques will not scale to the exascale regime: with a main memory footprint of tens of Petabytes, synchronously writing checkpoint data all the way to background storage at frequent intervals will create intolerable overheads in runtime and energy consumption. Forecasts show that the mean time between failures could be lower than the time to recover from such a checkpoint, so that large calculations at scale might not make any progress if robust alternatives are not investigated. More advanced resilience techniques must be devised. The key may lie in exploiting both advanced system features as well as specific application knowledge. Research will face two essential questions: (1) what are the reliability requirements for a particular computation and (2) how do we best design the algorithms and software to meet these requirements? While the analysis of use cases can help understand the particular reliability requirements, the construction of remedies is currently wide open. One avenue would be to refine and improve on system- or application-level checkpointing and rollback strategies in the case an error is detected. Developers might use fault notification interfaces and flexible runtime systems to respond to node failures in an application-dependent fashion. Novel numerical algorithms or more stochastic computational approaches may be required to meet accuracy requirements in the face of undetectable soft errors. These ideas constituted an essential topic of the seminar. The goal of this Dagstuhl Seminar was to bring together a diverse group of scientists with expertise in exascale computing to discuss novel ways to make applications resilient against detected and undetected faults. In particular, participants explored the role that algorithms and applications play in the holistic approach needed to tackle this challenge. This article gathers a broad range of perspectives on the role of algorithms, applications and systems in achieving resilience for extreme scale simulations. The ultimate goal is to spark novel ideas and encourage the development of concrete solutions for achieving such resilience holistically. [ABSTRACT FROM AUTHOR]