We present a new approach for path finding in weighted graphs using pre-computed minimal distance fields. By selecting the most promising minimal distance field at any given node and switching between them, our algori...
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We present a new approach for path finding in weighted graphs using pre-computed minimal distance fields. By selecting the most promising minimal distance field at any given node and switching between them, our algorithm aims to find the shortest path possible. As we show, this approach scales excellently for various topologies, graph sizes and hardware specifications while maintaining a mean length error below 1% and reasonable memory consumption. By utilizing a simplified structure and keeping backtracking to a minimum, we are able to leverage the same approach on the massively parallel GPUs or any other shared memory parallel architecture, reducing the run time even further. (C) 2021 Elsevier Ltd. All rights reserved.
Magnetic resonance spectroscopy (MRS) is an advanced biochemical technique used to identify metabolic compounds in living tissue. While its sensitivity and specificity to chemical imbalances render it a valuable tool ...
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Magnetic resonance spectroscopy (MRS) is an advanced biochemical technique used to identify metabolic compounds in living tissue. While its sensitivity and specificity to chemical imbalances render it a valuable tool in clinical assessment, the results from this modality are abstract and difficult to interpret. With this design study we characterized and explored the tasks and requirements for evaluating these data from the perspective of a MRS research specialist. Our resulting tool, SpectraMosaic, links with upstream spectroscopy quantification software to provide a means for precise interactive visual analysis of metabolites with both single- and multi-peak spectral signatures. Using a layered visual approach, SpectraMosaic allows researchers to analyze any permutation of metabolites in ratio form for an entire cohort, or by sample region, individual, acquisition date, or brain activity status at the time of acquisition. A case study with three MRS researchers demonstrates the utility of our approach in rapid and iterative spectral data analysis. (C) 2020 The Author(s). Published by Elsevier Ltd.
Cluster architectures are increasingly used to solve high-performance computingapplications. To build more computational power, sets of clusters, interconnected by high-speed networks, can be used in an elaboration t...
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Cluster architectures are increasingly used to solve high-performance computingapplications. To build more computational power, sets of clusters, interconnected by high-speed networks, can be used in an elaboration to form a cluster grid. In this type of architecture, it is difficult to exploit all the internal resources of a cluster, because each one can be shielded by a firewall and is usually configured with machines where there is only one visible IP front-end node that hides all its internal nodes from the external world. The exploitation of resources is even more complicated if we consider the general case where each internal node of a cluster can be a front-end node of an another cluster. This type of architecture has been defined as a multilayer cluster grid. In this paper, a Parallel Virtual Machine (PVM) extension is presented which, through a middleware solution based on the H2O distributed metacomputing framework, permits the building of a parallel virtual machine in a multilayer cluster grid environment. In addition, the existing code written for PVM can be executed into this environment without modifications. Copyright (C) 2007 John Wiley & Sons, Ltd.
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