18,583 research outputs found

    High dose-rate endobronchial brachytherapy: a dosimetric study

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    OBJETIVO: Avaliar a distribuição de dose em diferentes situações de braquiterapia endobrônquica de alta taxa de dose, com foco principalmente nos volumes de altas doses, e tentar definir situações de melhor ou pior distribuição de dose que possam servir de guia na prática clínica. MATERIAIS E MÉTODOS: Estudo teórico, simulando braquiterapia endobrônquica de alta taxa de dose utilizando dois cateteres, com variação da extensão de carregamento, angulação entre os cateteres, profundidade de cálculo e o intervalo entre as paradas da fonte. Com prescrição de 7,5 Gy, foram calculados os volumes englobados pelas isodoses correspondentes a 100%, 150% e 200% da dose prescrita (V100, V150 e V200, respectivamente) e as razões V150/V100 e V200/V100. RESULTADOS: Os volumes aumentaram com o aumento da extensão de carregamento dos cateteres, profundidade de cálculo e angulação, com tendência a um aumento proporcionalmente menor para angulações maiores. As relações V150/V100 e V200/V100 foram, em geral, homogêneas, ao redor de 0,50 e 0,30, respectivamente. CONCLUSÃO: A distribuição de dose na situação considerada padrão é em geral adequada. Nenhum parâmetro específico que pudesse ser relacionado à maior toxicidade foi identificado. Recomendamos uma avaliação rápida da qualidade do implante por meio da análise das relações V150/V100 e V200/V100.OBJECTIVE: To evaluate the dose distribution in different situations of high dose-rate endobronchial brachytherapy, focusing especially on high-dose volumes, and try to identify better or worse situations in terms of dose distribution to aid as guidance in the clinical practice. MATERIALS AND METHODS: Theoretical study simulating high dose-rate endobronchial brachytherapy utilizing two catheters, varying the loading extent, angle between the catheters, prescription depth, and source step. With a prescription dose of 7.5 Gy, the volumes involved by the 100%, 150% and 200% isodoses (V100, V150 and V200, respectively) and V150/V100 and V200/V100 ratios were calculated. RESULTS: There was a volume enhancement with larger loaded lengths, increase in prescription depth and angles, with a tendency towards a proportionally smaller increase with larger angulations. In general, the V150/V100 and V200/V100 ratios were homogeneous, respectively around 0.50 and 0.30. CONCLUSION: Overall, the dose distribution in the standard situation was appropriate. No specific parameter that could be related to a higher toxicity was identified. The authors recommend a swift evaluation of the treatment quality through the analysis of the V150/V100 and V200/V100 ratios

    NVIDIA Tensor Core Programmability, Performance & Precision

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    The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called "Tensor Core" that performs one matrix-multiply-and-accumulate on 4x4 matrices per clock cycle. The NVIDIA Tesla V100 accelerator, featuring the Volta microarchitecture, provides 640 Tensor Cores with a theoretical peak performance of 125 Tflops/s in mixed precision. In this paper, we investigate current approaches to program NVIDIA Tensor Cores, their performances and the precision loss due to computation in mixed precision. Currently, NVIDIA provides three different ways of programming matrix-multiply-and-accumulate on Tensor Cores: the CUDA Warp Matrix Multiply Accumulate (WMMA) API, CUTLASS, a templated library based on WMMA, and cuBLAS GEMM. After experimenting with different approaches, we found that NVIDIA Tensor Cores can deliver up to 83 Tflops/s in mixed precision on a Tesla V100 GPU, seven and three times the performance in single and half precision respectively. A WMMA implementation of batched GEMM reaches a performance of 4 Tflops/s. While precision loss due to matrix multiplication with half precision input might be critical in many HPC applications, it can be considerably reduced at the cost of increased computation. Our results indicate that HPC applications using matrix multiplications can strongly benefit from using of NVIDIA Tensor Cores.Comment: This paper has been accepted by the Eighth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES) 201

    1964. V100. March Bulletin.

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    https://digitalcommons.hope.edu/catalogs/1136/thumbnail.jp
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