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CUDA-based programming model is heterogeneous-composed of two components: host (CPU) and device (GPU). Both components have separated memory spaces and processing units. A great challenge to increase GPU-based application performance is the data migration between these memory spaces. Currently, the CUDA platform supports the following data migration methods: UMA, zero-copy, pageable and pinned memory. In this paper, we compare the zero-copy performance method with the other methods by considering the overall application runtime. Additionally, we investigated the aspects of data migration process to enunciate causes of the performance variations. The obtained results demonstrated in some cases the zero-copy memory can provide an average performance on 19% higher than the pinned memory transfer. In the studied situation, this method was the second most efficient. Finally, we present limitations of zero-copy memory as a resource for improving performance of CUDA applications.

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RESUMO -Ao longo dos anos, diferentes abordagens para visualização de volumes foram propostas na literatura. Neste trabalho, apresentamos uma nova ferramenta de visualização volumétrica, o Hybrid Volume Rendering Visualizer (HVRview). Em contraste às demais ferramentas, o HVRview incorpora uma abordagem de Hybrid Volume Rendering (HVR) -a técnica Volume on Surface (VoS) -com o objetivo de acelerar o processo de rendering. A ferramenta HVRview implementa uma versão sequencial em CPU e uma paralela em GPU da abordagem VoS. Em alguns testes, a execução paralela propiciou um speedup superior a 24 vezes.