A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
Lecture Notes in Computer Science
In this paper, we describe the performance of the parallel GROMOS87 code, developed under the ESPRIT EUROPORT-2/PACC project, and indicate its potential impact in industry. An outline of the parallel code structure is given, followed by a discussion of the results of some industrially-relevant testcases. Conclusions are drawn as to the overall success of the project, and lessons learned from the porting and benchmarking activities, which show that the parallel code can enable more ambitious use of molecular simulation in industry.doi:10.1007/3-540-61142-8_544 fatcat:ayjpjdoakrf6bjcy3rvvxybfmi
In this work, we demonstrate on-chip OPO covering > 130 THz of the visible spectrum, including red, orange, yellow, and green wavelengths. ... In particular, the top line (green) shows the case where the OPO is octave-spanning, with idler and signal bridging infrared and green wavelengths. d, Top view microscope images of devices exhibiting ... The overall frequency span covers red, orange, yellow, and green signal wavelengths. ...arXiv:2003.12177v1 fatcat:sxko7mpqm5hhvoyacuv5kzc2ia
Benefits/dis-benefits are characterised as: Red = increased cost, Green = reduced cost. BPC = Better Patient Care, EGT = Efficiency Gain in Time, RC = where benefit is purely as Reduced Cost. ...doi:10.1109/hicss.2008.490 dblp:conf/hicss/GreenY08 fatcat:3e5ebrtrlfbvhmvptiv6rcdra4
Microsoft Research is now in its fourth year of awarding Windows Azure cloud resources to the academic community. As of April 2014, over 200 research projects have started. In this paper we review the results of this effort to date. We also characterize the computational paradigms that work well in public cloud environments and those that are usually disappointing. We also discuss many of the barriers to successfully using commercial cloud platforms in research and ways these problems can bedoi:10.1145/2608029.2608030 dblp:conf/hpdc/GannonFGTY14 fatcat:oqyh3g66njamvdbwltdgf3lxim
more »... rcome. Cloud computing, map reduce, scalable systems, platform as a service, infrastructure as a service, cloud programming models.
This article presents ALOJA-Machine Learning (ALOJA-ML) an extension to the ALOJA project that uses machine learning techniques to interpret Hadoop benchmark performance data and performance tuning; here we detail the approach, efficacy of the model and initial results. Hadoop presents a complex execution environment, where costs and performance depends on a large number of software (SW) configurations and on multiple hardware (HW) deployment choices. These results are accompanied by a test beddoi:10.1145/2783258.2788600 dblp:conf/kdd/BerralPCCRG15 fatcat:3y7pnkbwxvbzjodjfwhm4ckjla
more »... and tools to deploy and evaluate the cost-effectiveness of the different hardware configurations, parameter tunings, and Cloud services. Despite early success within ALOJA from expert-guided benchmarking, it became clear that a genuinely comprehensive study requires automation of modeling procedures to allow a systematic analysis of large and resource-constrained search spaces. ALOJA-ML provides such an automated system allowing knowledge discovery by modeling Hadoop executions from observed benchmarks across a broad set of configuration parameters. The resulting performance models can be used to forecast execution behavior of various workloads; they allow 'a-priori' prediction of the execution times for new configurations and HW choices and they offer a route to model-based anomaly detection. In addition, these models can guide the benchmarking exploration efficiently, by automatically prioritizing candidate future benchmark tests. Insights from ALOJA-ML's models can be used to reduce the operational time on clusters, speed-up the data acquisition and knowledge discovery process, and importantly, reduce running costs. In addition to learning from the methodology presented in this work, the community can benefit in general from ALOJA data-sets, framework, and derived insights to improve the design and deployment of Big Data applications.
Green and Vavreck (2008) randomly assign nonpartisan television commercials to cable TV markets and study their effects on voter turnout. ... Gerber and Green (1998) generalized this model to account for the possibility that the underlying value of a given candidate changes over time. ...doi:10.1017/s000305541000047x fatcat:hrapecfgcbcjhho6pjafrl4k3u
This article presents the ALOJA project and its analytics tools, which leverages machine learning to interpret Big Data benchmark performance data and tuning. ALOJA is part of a long-term collaboration between BSC and Microsoft to automate the characterization of cost-effectiveness on Big Data deployments, currently focusing on Hadoop. Hadoop presents a complex run-time environment, where costs and performance depend on a large number of configuration choices. The ALOJA project has created andoi:10.1109/tetc.2015.2496504 fatcat:7kpa5wvwfzfs3jtd6aqjfbq5du
more »... en, vendor-neutral repository, featuring over 40,000 Hadoop job executions and their performance details. The repository is accompanied by a test-bed and tools to deploy and evaluate the cost-effectiveness of different hardware configurations, parameters and Cloud services. Despite early success within ALOJA, a comprehensive study requires automation of modeling procedures to allow an analysis of large and resource-constrained search spaces. The predictive analytics extension, ALOJA-ML, provides an automated system allowing knowledge discovery by modeling environments from observed executions. The resulting models can forecast execution behaviors, predicting execution times for new configurations and hardware choices. That also enables model-based anomaly detection or efficient benchmark guidance by prioritizing executions. In addition, the community can benefit from ALOJA data-sets and framework to improve the design and deployment of Big Data applications.
Separate data are shown for arteriolosclerosis (blue), extravascular pigmented macrophages (red), and expanded Virchow-Robin spaces (green). ...doi:10.1016/j.neurobiolaging.2015.10.009 pmid:26597697 pmcid:PMC4688098 fatcat:yocuakywcjep3gqamrdlwj6e3q
This article presents the ALOJA project, an initiative to produce mechanisms for an automated characterization of cost-effectiveness of Hadoop deployments and reports its initial results. ALOJA is the latest phase of a long-term collaborative engagement between BSC and Microsoft which, over the past 6 years has explored a range of different aspects of computing systems, software technologies and performance profiling. While during the last 5 years, Hadoop has become the de-facto platform fordoi:10.1109/bigdata.2014.7004322 dblp:conf/bigdataconf/PoggiCCMBTAGLRVGB14 fatcat:mwznaa3urrcplbsmssebw2ppii
more »... Data deployments, still little is understood of how the different layers of the software and hardware deployment options affects its performance. Early ALOJA results show that Hadoop's runtime performance, and therefore its price, are critically affected by relatively simple software and hardware configuration choices e.g., number of mappers, compression, or volume configuration. Project ALOJA presents a vendor-neutral repository featuring over 5000 Hadoop runs, a test bed, and tools to evaluate the cost-effectiveness of different hardware, parameter tuning, and Cloud services for Hadoop. As few organizations have the time or performance profiling expertise, we expect our growing repository will benefit Hadoop customers to meet their Big Data application needs. ALOJA seeks to provide both knowledge and an online service to with which users make better informed configuration choices for their Hadoop compute infrastructure whether this be on-premise or cloud-based. The initial version of ALOJA's Web application and sources are available at http://hadoop.bsc.es
Structure is isotypic with a-ThSi 2 , the unit cell is outlined.RE positions shown in green, Ga/Si positions shown in orange. RE trigonal prismbased framework shown with black bonds. ... RE positions shown in green, Ga1/Si1 positions shown in orange, Si2 positions shown in blue/white checkers. The framework based on trigonal prisms of RE-atoms is shown with black bonds. ...doi:10.1016/j.jssc.2013.02.029 fatcat:6td7eutugvdtfmlvo6znnj47ne
British Journal of Clinical Pharmacology
G. & Green, G. J. (1981). Dangers of amiodarone and anticoagulant treat- ment. Br. med. J., 283, 58. Staiger, Ch., Jauernig, R., De Vries, J. & Weber, E. (1984). ... A high per- formance liquid chromatographic assay of amio- darone and desethylamiodarone in plasma. Br. J. clin. Pharmac., 20, 299P. McKenna, W. J., Rowland, E. & Krikler, D. M. (1983). ...
British Journal of Clinical Pharmacology
Relation of amio- darone hepatic and pulmonary toxicity to serum drug Short report 127 correlation between increasing creatinine and amio- darone concentrations would have been approximately 1.0. ... Echt DS, Liebson PR, Mitchell LB, Peters RW, Obias- Manno D, Barker AH, Arensberg D, Baker A, Friedman L, Greene HL, Huther ML, Richardson DW, The CAST Investigators. ...
Daron, G. H.: Am. J. Anat. 58: 349. 1936. FarrE, ARTHUR: Uterus and its appendages; the cyclopedia of Anatomy and Physiol- ogy, ed. Robert B. ... Todd, Longman, Brown, Green, Longmans and Roberts, Lon- don. June 1858. Hunter, Wivi1AM: Anatomia uteri humani gravidi tabulis illustrata. 1774. REYNOLDS, 8S. R. M.: Am. J. ...
« Previous Showing results 1 — 15 out of 1,048 results