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De Coninck A.,Ghent University | Kourounis D.,Institute of Computational Science | Verbosio F.,Institute of Computational Science | Schenk O.,Institute of Computational Science | And 3 more authors.
Proceedings - 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2015 | Year: 2015

Genomic prediction for plant breeding requires taking into account environmental effects and variations of genetic effects across environments. The latter can be modelled by estimating the effect of each genetic marker in every possible environmental condition, which leads to a huge amount of effects to be estimated. Nonetheless, the information about these effects is only sparsely present, due to the fact that plants are only tested in a limited number of environmental conditions. In contrast, the genotypes of the plants are a dense source of information and thus the estimation of both types of effects in one single step would require as well dense as sparse matrix formalisms. This paper presents a way to efficiently apply a high performance computing infrastructure for dealing with large-scale genomic prediction settings, relying on the coupling of dense and sparse matrix algebra. © 2015 IEEE.

Fang X.,Institute of Computational Science | Liu G.,University of Electronic Science and Technology of China | Huang T.-Z.,Institute of Computational Science
WSEAS Transactions on Systems | Year: 2010

Neural gas network is a single-layered soft competitive neural network, which can be applied to clustering analysis with fast convergent speed comparing to Self-organizing Map (SOM), K-means etc. Combining neural gas with principal component analysis, this paper proposes a new clustering method, namely principal components analysis neural gas (PCA-NG), and the online learning algorithm is also given. The soft competitive learning of PCA-NG is based on local principal subspace, which characterizes the profile of a certain cluster. We utilize the PCA-NG to the domain of intrusion detection. Some experiments are carried out to illustrate the performance of the proposed approach by using a synthetic Gaussian-distributed dataset and the KDD CUP 1999 Intrusion Detection Evaluation dataset.

Strecha C.,Ecole Polytechnique Federale de Lausanne | Bronstein A.M.,Technion - Israel Institute of Technology | Bronstein M.M.,Institute of Computational Science | Fua P.,Ecole Polytechnique Federale de Lausanne
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2012

SIFT-like local feature descriptors are ubiquitously employed in computer vision applications such as content-based retrieval, video analysis, copy detection, object recognition, photo tourism, and 3D reconstruction. Feature descriptors can be designed to be invariant to certain classes of photometric and geometric transformations, in particular, affine and intensity scale transformations. However, real transformations that an image can undergo can only be approximately modeled in this way, and thus most descriptors are only approximately invariant in practice. Second, descriptors are usually high dimensional (e.g., SIFT is represented as a 128-dimensional vector). In large-scale retrieval and matching problems, this can pose challenges in storing and retrieving descriptor data. We map the descriptor vectors into the Hamming space in which the Hamming metric is used to compare the resulting representations. This way, we reduce the size of the descriptors by representing them as short binary strings and learn descriptor invariance from examples. We show extensive experimental validation, demonstrating the advantage of the proposed approach. © 2012 IEEE.

Rietmann M.,Institute of Computational Science | Messmer P.,Nvidia | Nissen-Meyer T.,ETH Zurich | Peter D.,Princeton University | And 6 more authors.
International Conference for High Performance Computing, Networking, Storage and Analysis, SC | Year: 2012

Computational seismology is an area of wide sociological and economic impact, ranging from earthquake risk assessment to subsurface imaging and oil and gas exploration. At the core of these simulations is the modeling of wave propagation in a complex medium. Here we report on the extension of the high-order finite-element seismic wave simulation package SPECFEM3D to support the largest scale hybrid and homogeneous supercomputers. Starting from an existing highly tuned MPI code, we migrated to a CUDA version. In order to be of immediate impact to the science mission of computational seismologists, we had to port the entire production package, rather than just individual kernels. One of the challenges in parallelizing finite element codes is the potential for race conditions during the assembly phase. We therefore investigated different methods such as mesh coloring or atomic updates on the GPU. In order to achieve strong scaling, we needed to ensure good overlap of data motion at all levels, including internode and host-accelerator transfers. Finally we carefully tuned the GPU implementation. The new MPI/CUDA solver exhibits excellent scalability and achieves speedup on a node-to-node basis over the carefully tuned equivalent multi-core MPI solver. To demonstrate the performance of both the forward and adjoint functionality, we present two case studies run on the Cray XE6 CPU and Cray XK6 GPU architectures up to 896 nodes: (1) focusing on most commonly used forward simulations, we simulate seismic wave propagation generated by earthquakes in Turkey, and (2) testing the most complex seismic inversion type of the package, we use ambient seismic noise to image 3-D crust and mantle structure beneath western Europe. © 2012 IEEE.

Maiolo M.,University of Applied Sciences and Arts Southern Switzerland | Maiolo M.,Institute For Angewandte Simulation | Vancheri A.,University of Applied Sciences and Arts Southern Switzerland | Krause R.,Institute of Computational Science | Danani A.,University of Applied Sciences and Arts Southern Switzerland
Journal of Computational Physics | Year: 2015

In this paper, we apply Multiresolution Analysis (MRA) to develop sparse but accurate representations for the Multiscale Coarse-Graining (MSCG) approximation to the many-body potential of mean force. We rigorously framed the MSCG method into MRA so that all the instruments of this theory become available together with a multitude of new basis functions, namely the wavelets. The coarse-grained (CG) force field is hierarchically decomposed at different resolution levels enabling to choose the most appropriate wavelet family for each physical interaction without requiring an a priori knowledge of the details localization. The representation of the CG potential in this new efficient orthonormal basis leads to a compression of the signal information in few large expansion coefficients. The multiresolution property of the wavelet transform allows to isolate and remove the noise from the CG force-field reconstruction by thresholding the basis function coefficients from each frequency band independently. We discuss the implementation of our wavelet-based MSCG approach and demonstrate its accuracy using two different condensed-phase systems, i.e. liquid water and methanol. Simulations of liquid argon have also been performed using a one-to-one mapping between atomistic and CG sites. The latter model allows to verify the accuracy of the method and to test different choices of wavelet families. Furthermore, the results of the computer simulations show that the efficiency and sparsity of the representation of the CG force field can be traced back to the mathematical properties of the chosen family of wavelets. This result is in agreement with what is known from the theory of multiresolution analysis of signals. © 2015.

News Article | January 26, 2016
Site: motherboard.vice.com

I’m being rocked violently by a magnitude nine earthquake along with three other people. We’ve all ducked underneath a table and, squished together, we shield our heads with cushions. As the shaking intensifies, we clutch at the table legs to stop our bodies from sliding out. After a minute, the tremors subside and we crawl out from under our table shelter looking dazed. The tremors we just experienced were similar to those that ravaged Tohoku, a northern province in Japan, in March 2011. But our earthquake experience wasn’t exactly real. The shaking was computer-simulated, triggered by the flick of a switch by our disaster guide, Yoshiko Miyagawa. We were inside one of Tokyo’s three Safety and Disaster Learning Centers (Bousaikan), run by the Fire Department in Ikebukuro, learning how to prepare for the worst. The earthquake simulator looks deceptively like a small dining table propped up on a stage.Image: Emiko Jozuka “We don’t want to just show people visuals of what a big earthquake is like, we really want people to experience a high-magnitude tremor so they know what to expect,” Makoto Goto, a representative of the Ikebukuro Bosaikan, told me. “We really want people to gain the basic safety skills so they can protect themselves in the event of disaster.” Japan lies in what’s dubbed the “Ring of Fire”—a 40,000 km horseshoe-shaped basin made up of a medley of fault lines, oceanic trenches, volcanic belts and arcs. This leaves the country susceptible to natural disasters. It’s hit by as many as 100,000 earthquakes per year, and nobody knows when the next big quake will strike. In April 2012, the Tokyo metropolitan government predicted what would happen to Tokyo if it were hit by a magnitude-7.3 earthquake. They foresaw epic transportation delays, and estimated close to 9,700 deaths—with up to 4,100 caused by wooden houses in the city going up in flames. The Lloyds City Risk Index Report for 2015 to 2025, released in August 2015, suggests that Japan’s proneness to both manmade and natural disasters makes Tokyo the second riskiest city in the world to live in. Fight that virtual fire! A snapshot of another disaster prevention training scene at the Bousaikan. Image: Emiko Jozuka The earthquake simulator, which is basically a moveable stage equipped with a table and five chairs, might sound like something you’d come across in an amusement park. However, at the Ikebukuro Bosaikan—which was established in 1986 and is manned by a crew of retired firefighters—the small simulator allows people who’ve never experienced an earthquake an insight into how terrifying they can be. The disaster guide also takes visitors through what you’re supposed to do when the tremors strike so they’re prepared if ever they come across the real thing. Miyagawa instructed us to quickly duck under the table as soon as we felt shaking and to avoid screaming in case we bit down on our tongues by accident. The Ikebukuro Bousaikan offers the general public a physical experience of a fake quake, but they’re by no means the only ones simulating an earthquake’s deadly effects. While the Bousaikan is all about instructing the public, researchers at the University of Tokyo’s Earthquake Research Institute (ERI) simulate earthquakes on a macro cityscape scale in order to inform disaster response at a national level. As the fake quake hits, hotel employees scramble for safety. Image: Emiko Jozuka They hope that their simulations can assist government decision makers when it comes to preparing city infrastructures against powerful earthquakes in the future. According to Lalith Wijerathne—an associate professor who virtually simulates hypothetical disaster evacuation scenarios in urban environments in the event of a deadly quake—they can’t say when a big quake will strike, but “it’s coming.” “In our center we want to simulate earthquake phenomena from start to finish,” Wijerathne told me. “We want to develop software so we can simulate the effect of an earthquake on a large city, starting with the earthquake motion in the bedrock, then the shaking in the soil, and how this amplification process shakes buildings, and then how people start evacuating.” The researchers can apply data from past quakes and use the high processing power of Japan’s K supercomputer in the RIKEN Advanced Institute of Computational Science in southern Japan to simulate its effects on their high-res cityscape visualizations. One of their sims takes a small segment of Tokyo, and shows roughly 300,000 virtual buildings of today being shaken by the equivalent of the 1995 Kobe earthquake. In a nutshell, the simulation platform offers the researchers a space where they can shake Tokyo in many different magnitudes and tremor styles. Lalith Wijerathne demonstrates how 300,000 buildings in Tokyo would shake in the event of a super quake. Image: Emiko Jozuka “Instead of waiting for nature to do the experiment, we can do the experiment in the computer ourselves and observe how the buildings respond, or what damage could be caused,” explained Wijerathne. On the simulation, the buildings that show up as red are the ones that are more susceptible to damage in the event of a big earthquake. Currently, private building data is unavailable, so in order to simulate their shaking cityscape, the researchers gather building data from Google Earth and refer to Japanese design codes to suss out what kind of beam and column sizes each building might use. Wijerathne admitted that their simulations would be more accurate with concrete building data, but foresaw that it would take some time before that became available. Mega tremors like the one that hit Tohoku on 11 March 2011 are rare yet deadly occurrences. As the threat of a powerful earthquake looms, efforts are being made at both the civic and national level to inform disaster responses. Earthquake simulations over at ERI may provide government players with more effective disaster management strategies, while over at the Bousaikan, the disaster response instruction is on a more personal level. Inside the Bousaikan, which was decked out in sparkling Christmas decorations at the time of my visit, Goto posed for a photograph beside the center’s mascot, a cheerful blue elephant. A video of dancing firefighters and young women demonstrating disaster response techniques played on a screen just behind him. “Some people in Japan have never experienced a big earthquake before, so they’re surprised when they try out the simulator,” remarked Goto. “Once you’ve experienced it, and have been trained how to react, your body will remember what to do when a real earthquake strikes.” Cool Japan is a column about the quirky and serious happenings in the Japanese scientific, technological and cultural realms. It covers the unknown, the mainstream, and the otherwise interesting developments in Japan.

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