The impact of global communication latency at extreme scales on Krylov methods

Krylov Subspace Methods (KSMs) are popular numerical tools for solving large linear systems of equations. We consider their role in solving sparse systems on future massively parallel distributed memory machines, by estimating future performance of their constituent operations. To this end we construct a model that is simple, but which takes topology and network acceleration into account as they are important considerations. We show that, as the number of nodes of a parallel machine increases to very large numbers, the increasing latency cost of reductions may well become a problematic bottleneck for traditional formulations of these methods. Finally, we discuss how pipelined KSMs can be used to tackle the potential problem, and appropriate pipeline depths

HyperLoom possibilities for executing scientific workflows on the cloud

We have developed HyperLoom - a platform for defining and executing scientific workflows in large-scale HPC systems. The computational tasks in such workflows often have non-trivial dependency patterns, unknown execution time and unknown sizes of generated outputs. HyperLoom enables to efficiently execute the workflows respecting task requirements and cluster resources agnostically to the shape or size of the workflow. Although HPC infrastructures provide an unbeatable performance, they may be unavailable or too expensive especially for small to medium workloads. Moreover, for some workloads, due to HPCs not very flexible resource allocation policy, the system energy efficiency may not be optimal at some stages of the execution. In contrast, current public cloud providers such as Amazon, Google or Exoscale allow users a comfortable and elastic way of deploying, scaling and disposing a virtualized cluster of almost any size. In this paper, we describe HyperLoom virtualization and evaluate its performance in a virtualized environment using workflows of various shapes and sizes. Finally, we discuss the Hyperloom potential for its expansion to cloud environments.61140639

HyperLoom: A platform for defining and executing scientific pipelines in distributed environments

Real-world scientific applications often encompass end-to-end data processing pipelines composed of a large number of interconnected computational tasks of various granularity. We introduce HyperLoom, an open source platform for defining and executing such pipelines in distributed environments and providing a Python interface for defining tasks. HyperLoom is a self-contained system that does not use an external scheduler for the actual execution of the task. We have successfully employed HyperLoom for executing chemogenomics pipelines used in pharmaceutic industry for novel drug discovery.6

CMS Software Distribution on the LCG and OSG Grids

The efficient exploitation of worldwide distributed storage and computing resources available in the grids require a robust, transparent and fast deployment of experiment specific software. The approach followed by the CMS experiment at CERN in order to enable Monte-Carlo simulations, data analysis and software development in an international collaboration is presented. The current status and future improvement plans are described.Comment: 4 pages, 1 figure, latex with hyperref

Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2

Prospectus, January 16, 1973

CHAMPAIGN-URBANA PEACE MARCH SCHEDULED; Anti-war resolution proposed; IOC meeting; Parkland trail riders; Please send a picture; Health program enrollment; Financial Board opening; All club treasurers; Can you help?; U.S. Gov\u27t Speaker; Debaters compete at ISU; Cruisin\u27 \u2773; True happenings; Ken\u27s munchy cereal; Equal tyme; Getting ignored by the biggies; PC lady hits the big time; Writer\u27s view questioned; Wanna graduate?; Big Kid\u27s Day?; little fat kid; Population, resources, environment; Mass demonstrations in D.C., Inauguration Day; Chi Gamma Iota; New campus regs; Freed injured; Movie Review: The Getaway; Prof Spectushttps://spark.parkland.edu/prospectus_1973/1014/thumbnail.jp

Effect of early vasopressin vs norepinephrine on kidney failure in patients with septic shock. The VANISH Randomized Clinical Trial

IMPORTANCE: Norepinephrine is currently recommended as the first-line vasopressor in septic shock; however, early vasopressin use has been proposed as an alternative. OBJECTIVE: To compare the effect of early vasopressin vs norepinephrine on kidney failure in patients with septic shock. DESIGN, SETTING, AND PARTICIPANTS: A factorial (2×2), double-blind, randomized clinical trial conducted in 18 general adult intensive care units in the United Kingdom between February 2013 and May 2015, enrolling adult patients who had septic shock requiring vasopressors despite fluid resuscitation within a maximum of 6 hours after the onset of shock. INTERVENTIONS: Patients were randomly allocated to vasopressin (titrated up to 0.06 U/min) and hydrocortisone (n = 101), vasopressin and placebo (n = 104), norepinephrine and hydrocortisone (n = 101), or norepinephrine and placebo (n = 103). MAIN OUTCOMES AND MEASURES: The primary outcome was kidney failure-free days during the 28-day period after randomization, measured as (1) the proportion of patients who never developed kidney failure and (2) median number of days alive and free of kidney failure for patients who did not survive, who experienced kidney failure, or both. Rates of renal replacement therapy, mortality, and serious adverse events were secondary outcomes. RESULTS: A total of 409 patients (median age, 66 years; men, 58.2%) were included in the study, with a median time to study drug administration of 3.5 hours after diagnosis of shock. The number of survivors who never developed kidney failure was 94 of 165 patients (57.0%) in the vasopressin group and 93 of 157 patients (59.2%) in the norepinephrine group (difference, -2.3% [95% CI, -13.0% to 8.5%]). The median number of kidney failure-free days for patients who did not survive, who experienced kidney failure, or both was 9 days (interquartile range [IQR], 1 to -24) in the vasopressin group and 13 days (IQR, 1 to -25) in the norepinephrine group (difference, -4 days [95% CI, -11 to 5]). There was less use of renal replacement therapy in the vasopressin group than in the norepinephrine group (25.4% for vasopressin vs 35.3% for norepinephrine; difference, -9.9% [95% CI, -19.3% to -0.6%]). There was no significant difference in mortality rates between groups. In total, 22 of 205 patients (10.7%) had a serious adverse event in the vasopressin group vs 17 of 204 patients (8.3%) in the norepinephrine group (difference, 2.5% [95% CI, -3.3% to 8.2%]). CONCLUSIONS AND RELEVANCE: Among adults with septic shock, the early use of vasopressin compared with norepinephrine did not improve the number of kidney failure-free days. Although these findings do not support the use of vasopressin to replace norepinephrine as initial treatment in this situation, the confidence interval included a potential clinically important benefit for vasopressin, and larger trials may be warranted to assess this further. TRIAL REGISTRATION: clinicaltrials.gov Identifier: ISRCTN 20769191

Upward spirals of positive emotion and coping: Replication, extension, and initial exploration of neurochemical substrates

The broaden-and-build theory (Fredrickson, 1998, 2001) predicts that positive emotions broaden the scopes of attention and cognition, thereby facilitating the building of personal resources and initiating upward spirals toward increasing emotional well-being. This study attempts to replicate and extend previous empirical support for this model. Using a sample of 185 undergraduates, we assessed whether positive affect and broad-minded coping, interpersonal trust, and social support reciprocally and prospectively predict one another over a two-month period, and whether this upward spiral might be partially based in changes in dopaminergic functioning. As hypothesized, PA and positive coping did mutually build on one another, as did PA and interpersonal trust. Contrary to expectation, PA did not demonstrate an upward spiral relation with social support. Results suggest further study of the relationship between PA and changes in dopamine metabolite levels over time is warranted