EGPGV13: Eurographics Symposium on Parallel Graphics and Visualization

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https://diglib.eg.org/handle/10.2312/354

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Browsing EGPGV13: Eurographics Symposium on Parallel Graphics and Visualization by Subject "Concurrent Programming"

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    GPU Acceleration of Particle Advection Workloads in a Parallel, Distributed Memory Setting
    (The Eurographics Association, 2013) Camp, David; Krishnan, Hari; Pugmire, David; Garth, Christoph; Johnson, Ian; Bethel, E. Wes; Joy, Kenneth I.; Childs, Hank; Fabio Marton and Kenneth Moreland
    Although there has been significant research in GPU acceleration, both of parallel simulation codes (i.e., GPGPU) and of single GPU visualization and analysis algorithms, there has been relatively little research devoted to visualization and analysis algorithms on GPU clusters. This oversight is significant: parallel visualization and analysis algorithms have markedly different characteristics - computational load, memory access pattern, communication, idle time, etc. - than the other two categories. In this paper, we explore the benefits of GPU acceleration for particle advection in a parallel, distributed-memory setting. As performance properties can differ dramatically between particle advection use cases, our study operates over a variety of workloads, designed to reveal insights about underlying trends. This work has a three-fold aim: (1) to map a challenging visualization and analysis algorithm - particle advection - to a complex system (a cluster of GPUs), (2) to inform its performance characteristics, and (3) to evaluate the advantages and disadvantages of using the GPU. In our performance study, we identify which factors are and are not relevant for obtaining a speedup when using GPUs. In short, this study informs the following question: if faced with a parallel particle advection problem, should you implement the solution with CPUs, with GPUs, or does it not matter?
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    VtkSMP: Task-based Parallel Operators for VTK Filters
    (The Eurographics Association, 2013) Ettinger, Mathias; Broquedis, F.; Gautier, T.; Ploix, S.; Raffin, Bruno; Fabio Marton and Kenneth Moreland
    NUMA nodes are potentially powerful but taking benefit of their capabilities is challenging due to their architecture (multiple computing cores, advanced memory hierarchy). They are nonetheless one of the key components to enable processing the ever growing amount of data produced by scientific simulations. In this paper we study the parallelization of patterns commonly used in VTK algorithms and propose a new multithreaded plugin for VTK that eases the development of parallel multi-core VTK filters. We specifically focus on task-based approaches and show that with a limited code refactoring effort we can take advantage of NUMA node capabilities. We experiment our patterns on a transform filter, base isosurface extraction filter and a min/max tree accelerated isosurface extraction. We support 3 programming environments, OpenMP, Intel TBB and X-KAAPI, and propose different algorithmic refinements according to the capabilities of the target environment. Results show that we can speed execution up to 30 times on a 48-core machine.