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Geographic information systems deal with spatial data and its analysis. Spatial data contains many attributes with location information. Spatial autocorrelation is a fundamental concept in spatial analysis. It suggests that similar objects tend to cluster in geographic space. Hotspots, an example of autocorrelation, are statistically significant clusters of spatial data. Other autocorrelation measures like Moran’s I are used to quantify spatial dependence. Large scale spatial autocorrelation methods are compute- intensive. Fast methods for hotspots detection and analysis are crucial in recent times of COVID-19 pandemic. Therefore, we have developed parallelization methods on heterogeneous CPU and GPU environments. To the best of our knowledge, this is the first GPU and SIMD-based design and implementation of autocorrelation kernels. Earlier methods in literature introduced cluster-based and MapReduce-based parallelization. We have used Intrinsics to exploit SIMD parallelism on x86 CPU architecture. We have used MPI Graph Topology to minimize inter-process communication. Our benchmarks for CPU/GPU optimizations gain up to 750X relative speedup with a 8 GPU setup when compared to baseline sequential implementation. Compared to the best implementation using OpenMP + R-tree data structure on a single compute node, our accelerated hotspots benchmark gains a 25X speedup. For real world US counties and COVID data evolution calculated over 500 days, we gain up to 110X speedup reducing time from 33 minutes to 0.3 minutes. 

The activation of O 2 at thiolate–ligated iron( ii ) sites is essential to the function of numerous metalloenzymes and synthetic catalysts. Iron–thiolate bonds in the active sites of nonheme iron enzymes arise from either coordination of an endogenous cysteinate residue or binding of a deprotonated thiol-containing substrate. Examples of the latter include sulfoxide synthases, such as EgtB and OvoA, that utilize O 2 to catalyze tandem S–C bond formation and S -oxygenation steps in thiohistidine biosyntheses. We recently reported the preparation of two mononuclear nonheme iron–thiolate complexes (1 and 2) that serve as structural active-site models of substrate-bound EgtB and OvoA ( Dalton Trans. 2020, 49 , 17745–17757). These models feature monodentate thiolate ligands and tripodal N 4 ligands with mixed pyridyl/imidazolyl donors. Here, we describe the reactivity of 1 and 2 with O 2 at low temperatures to give metastable intermediates (3 and 4, respectively). Characterization with multiple spectroscopic techniques (UV-vis absorption, NMR, variable-field and -temperature Mössbauer, and resonance Raman) revealed that these intermediates are thiolate-ligated iron( iii ) dimers with a bridging oxo ligand derived from the four-electron reduction of O 2 . Structural models of 3 and 4 consistent with the experimental data were generated via density functional theory (DFT) calculations. The combined experimental and computational results illuminate the geometric and electronic origins of the unique spectral features of diiron( iii )-μ-oxo complexes with thiolate ligands, and the spectroscopic signatures of 3 and 4 are compared to those of closely-related diiron( iii )-μ-peroxo species. Collectively, these results will assist in the identification of intermediates that appear on the O 2 reaction landscapes of iron–thiolate species in both biological and synthetic environments.