The Visualization Toolkit (VTK) is an open source, permissively licensed, cross-platform toolkit for scientific data processing, visualization, and data analysis. It is over two decades old, originally developed for a very different graphics card architecture. Modern graphics cards feature fully programmable, highly parallelized architectures with large core counts. VTK's rendering code was rewritten to take advantage of modern graphics cards, maintaining most of the toolkit's programming interfaces. This offers the opportunity to compare the performance of old and new rendering code on the same systems/cards. Significant improvements in rendering speeds and memory footprints mean that scientific data can be visualized in greater detail than ever before. The widespread use of VTK means that these improvements will reap significant benefits.

Ultrascale Visualization of Climate DataUltrascale Visualization Climate Data Analysis Tools Project Team Collaboration across research, government, academic, and private sectors is integrating more than 70 scientific computing libraries and applications through a tailorable provenance framework, empowering scientists to exchange and examine data in novel ways. Fueled by exponential increases in the computational and storage capabilities of high-performance computing platforms, climate simulations are evolving toward higher numerical fidelity, complexity, volume, and dimensionality. These technological breakthroughs are coming at a time of exponential growth in climate data, with estimates of hundreds of exabytes by 2020. 1 To meet the challenges and exploit the opportunities that such explosive growth affords, a consortium of four national laboratories, two universities, a government agency, and two private companies formed to explore the next wave in climate science. Working in close collaboration with domain experts, the Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) project aims to provide high-level solutions to a variety of climate data analysis and visualization problems:Dealing with big data analytics. Climate science is no different from other domains in its pursuit of solutions to process, analyze, and visualize massive datasets. Sensitivity analysis. The community must be able to push ensemble analysis, uncertainty quantification, and metrics computation to new boundaries. Heterogeneous data sources. Climate science data comes from simulations, observations, and reanalysis. Any visualization and analysis solution must unify these sources. Reproducibility. All science must support systematic data maintenance by providing provenance to ensure reliable and persistent links between workflows. Multiple disciplinary domains. Complexity stems from the need to incorporate a broad nexus of climate and other related science domains such as climate adaptation and mitigation for water, energy, and agriculture conservation. • Flexible, scalable architecture. Any unifying structure must be able to incorporate both existing and future software components with minimal or no infrastructure modification.

Advances in detectors and computational technologies provide new opportunities for applied research and the fundamental sciences. Concurrently, dramatic increases in the three V's (Volume, Velocity, and Variety) of experimental data and the scale of computational tasks produced the demand for new real-time processing systems at experimental facilities.Recently, this demand was addressed by the Spark-MPI approach connecting the Spark data-intensive platform with the MPI high-performance framework. In contrast with existing data management and analytics systems, Spark introduced a new middleware based on resilient distributed datasets (RDDs), which decoupled various data sources from high-level processing algorithms. The RDD middleware significantly advanced the scope of data-intensive applications, spreading from SQL queries to machine learning to graph processing. Spark-MPI further extended the Spark ecosystem with the MPI applications using the Process Management Interface. The paper explores this integrated platform within the context of online ptychographic and tomographic reconstruction pipelines.

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Vascular Endothelial Growth Factor (VEGF) and its receptor play an important role both in physiologic and pathologic angiogenesis, which is identi ed in ovarian cancer progression and metastasis development. The aim of the present investigation is to identify a potential Vascular Endothelial Growth Factor inhibitor which is playing a crucial role in stimulating the immunosuppressive microenvironment in tumour cells of the ovary and to examine for an effectiveness of identi ed inhibitor for the treatment of ovarian cancer using various in-silico approaches. 12 established VEGF inhibitors were collected from various literature. The compound AEE788 displays the great a nity towards the target protein as a result of docking study. AEE788 was further used for structure-based virtual screening in order to obtain a more structurally similar compound with high a nity. Among the 80 Virtual screened compounds, CID 88265020, explicates much better a nity than established compound AEE788. Based on Molecular Dynamics Simulation, pharmacophore and comparative toxicity analysis of both the best-established compound and the best virtual screened compound displayed a trivial variation in associated properties. The virtual screened compound CID 88265020 have a high a nity with the lowest re-rank score, and holds a huge potential to inhibit the VGFR and can be implemented for prospective future investigations in Ovarian Cancer.