QR Factorization of Tall and Skinny Matrices in a Grid Computing Environment

Previous studies have reported that common dense linear algebra operations do not achieve speed up by using multiple geographical sites of a computational grid. Because such operations are the building blocks of most scientific applications, conventional supercomputers are still strongly predominant in high-performance computing and the use of grids for speeding up large-scale scientific problems is limited to applications exhibiting parallelism at a higher level. We have identified two performance bottlenecks in the distributed memory algorithms implemented in ScaLAPACK, a state-of-the-art dense linear algebra library. First, because ScaLAPACK assumes a homogeneous communication network, the implementations of ScaLAPACK algorithms lack locality in their communication pattern. Second, the number of messages sent in the ScaLAPACK algorithms is significantly greater than other algorithms that trade flops for communication. In this paper, we present a new approach for computing a QR factorization -- one of the main dense linear algebra kernels -- of tall and skinny matrices in a grid computing environment that overcomes these two bottlenecks. Our contribution is to articulate a recently proposed algorithm (Communication Avoiding QR) with a topology-aware middleware (QCG-OMPI) in order to confine intensive communications (ScaLAPACK calls) within the different geographical sites. An experimental study conducted on the Grid'5000 platform shows that the resulting performance increases linearly with the number of geographical sites on large-scale problems (and is in particular consistently higher than ScaLAPACK's).Comment: Accepted at IPDPS10. (IEEE International Parallel & Distributed Processing Symposium 2010 in Atlanta, GA, USA.

Interactive Session: Equity, Diversity, and Inclusion in Open Education

Participants in the Equity, Diversity, and Inclusion in Open Education session will gain strategies on how to integrate equitable values into their workflows. This session will cover best practices for centering diverse perspectives. It will also explore design principles which harness the flexibility of OER to include a variety teaching and learning styles

Variables Associated with High School Shot Put Performance

International Journal of Exercise Science 15(6): 1357-1365, 2022. This study determined the relationship between high school athletes\u27 maximal strength, jumping, and sprinting with shot put performance. High school athletes (n = 9; 16.9 ± 1 years; 110.4 ± 10.8 kg; 183.73 ± 9.33 cm) performed the broad jump, 1RM squat, 1RM bench, 40-yard dash with 10-yd split, and shot put. Strong positive correlations between shot put performance and broad jump (r = 0.89), 1RM squat (r = 0.90), and 1RM bench press (r = 0.87), and a strong negative correlation between shot put performance and 40-yd dash (r = -0.86) were observed. No significant correlation was found between shot put performance and 10-yd split times. Results indicate that shot put performance is associated with strength, jumping, and sprinting. It is valuable for coaches to understand relationships between physical fitness measurements, such as strength, power, and speed/acceleration, with shot put performance to predict competition performance, make training adjustments, and develop young throwers appropriately

Influence of Stimulus Intensity on Multimodal Integration in the Startle Escape System of Goldfish

Processing of multimodal information is essential for an organism to respond to environmental events. However, how multimodal integration in neurons translates into behavior is far from clear. Here, we investigate integration of biologically relevant visual and auditory information in the goldfish startle escape system in which paired Mauthner-cells (M-cells) initiate the behavior. Sound pips and visual looms as well as multimodal combinations of these stimuli were tested for their effectiveness of evoking the startle response. Results showed that adding a low intensity sound early during a visual loom (low visual effectiveness) produced a supralinear increase in startle responsiveness as compared to an increase expected from a linear summation of the two unimodal stimuli. In contrast, adding a sound pip late during the loom (high visual effectiveness) increased responsiveness consistent with a linear multimodal integration of the two stimuli. Together the results confirm the Inverse Effectiveness Principle (IEP) of multimodal integration proposed in other species. Given the well-established role of the M-cell as a multimodal integrator, these results suggest that IEP is computed in individual neurons that initiate vital behavioral decisions

The AVA project : empowering young people to address domestic and sexual violence : final evaluation report

The AVA Project: Empowering young people to address domestic and sexual violence (hereafter referred to as ‘the Project’) was developed and led by AVA, a UK charity committed to ending gender based violence and abuse. The overarching aim of the project was: “to deliver therapeutic group-work and leadership development to disadvantaged and marginalised young people to improve their understanding of domestic and sexual violence, to improve their emotional wellbeing and to empower them to influence peers and advocate for the needs of themselves and others within social care and education services”. The project defined itself as underpinned by a number of key values including: youth work (specifically the principle of voluntary engagement); participation; and feminist practice. It was funded for £298,254 over three years by Big Lottery: Reaching Communities Fund, commencing in April 2013 and, with a short project extension continued until July 2016. The project was delivered in five local sites (localities) across England, through two distinct though related models: MODEL 1: ‘Peer Education’ - a therapeutic group-work model across two project sites focused on improving emotional wellbeing and awareness of domestic and sexual violence (DSV). MODEL 2: ‘Youth leadership’ - a youth leadership project to improve young people’s emotional wellbeing, their understanding of domestic and sexual violence (DSV) and that of their peers, whilst increasing opportunities for, and the abilities of, young people to influence services aimed at them in relation to DSV

Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique

Forthcoming surveys such as the Large Synoptic Survey Telescope (LSST) and Euclid necessitate automatic and efficient identification methods of strong lensing systems. We present a strong lensing identification approach that utilizes a feature extraction method from computer vision, the Histogram of Oriented Gradients (HOG), to capture edge patterns of arcs. We train a supervised classifier model on the HOG of mock strong galaxy-galaxy lens images similar to observations from the Hubble Space Telescope (HST) and LSST. We assess model performance with the area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve. Models trained on 10,000 lens and non-lens containing images images exhibit an AUC of 0.975 for an HST-like sample, 0.625 for one exposure of LSST, and 0.809 for 10-year mock LSST observations. Performance appears to continually improve with the training set size. Models trained on fewer images perform better in absence of the lens galaxy light. However, with larger training data sets, information from the lens galaxy actually improves model performance, indicating that HOG captures much of the morphological complexity of the arc finding problem. We test our classifier on data from the Sloan Lens ACS Survey and find that small scale image features reduces the efficiency of our trained model. However, these preliminary tests indicate that some parameterizations of HOG can compensate for differences between observed mock data. One example best-case parameterization results in an AUC of 0.6 in the F814 filter image with other parameterization results equivalent to random performance.Comment: 18 pages, 14 figures, summarizing results in figure

MPI Applications on Grids: A Topology-Aware Approach

Large Grids are build by aggregating smaller parallel machines through a public long-distance interconnection network (such as the Internet). Therefore, their structure is intrinsically hierarchical. Each level of the network hierarchy gives performances which differ from the other levels in terms of latency and bandwidth. MPI is the de facto standard for programming parallel machines, therefore an attractive solution for programming parallel applications on this kind of grids. However, because of the aforementioned differences of communication performances, the application continuously communicates back and forth between clusters, with a significant impact on performances. In this report, we present an extension of the information provided by the run-time environment of an MPI library, a set of efficient collective operations for grids and a methodology to organize communication patterns within applications with respect to the underlying physical topology, and implement it in a geophysics application