Laszlo Lazar

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Graphics Processing Units (GPU) have been used extensively for accelerating parallelizable applications in general, and scientific computations in particular. Stencil based algorithms are used intensively in various research areas and represent good candidates for GPU based acceleration. Since scientific computations have high accuracy requirements, herein(More)
Scientific applications are typically compute intensive, often due to the requirement of solving large sparse linear systems of equations. The geometric multigrid method (GMG) is one of the most efficient algorithms for solving these systems and is well suited for parallelization. Herein we focus on an in-depth analysis of a GPU-based GMG implementation and(More)
During the past decade Graphics Processing Units (GPU) have been increasingly employed for speeding up compute intensive scientific applications. In this field, the geometric multigrid method (GMG) is one of the most efficient algorithms for solving large sparse linear systems of equations. Herein we analyze the performance of an optimized GPU based(More)
RESULTS In Cohort 1, CFR was significantly lower (2.12 0.79 vs. 2.56 0.63; p<0.001) and HMR was significantly higher (2.61 1.22 vs. 2.31 0.89; p1⁄40.04) in vessels with CAD than the vessels without CAD, within the same patient. mMR was equivalent in obstructed and nonobstructed vessels: 1.54 0.77 vs. 1.53 0.57; p1⁄40.90. Cohort 2 confirmed these findings,(More)
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