18,592 research outputs found
High dose-rate endobronchial brachytherapy: a dosimetric study
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 clÃnica. 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 especÃfico 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
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.
https://digitalcommons.hope.edu/catalogs/1136/thumbnail.jp
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