High Performance Parallel JPEG2000 Streaming Decoder Using GPGPU-CPU Heterogeneous System

Le, Roto; Mundy, Joseph L.; Bahar, R. Iris

doi:10.1109/asap.2012.34

Cited by 5 publications

(1 citation statement)

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“…In code partitioning, the CPU and GPU execute different tasks and these tasks together accomplish the complete functionality. This idea has been used in [18] for mapping the JPEG2000 streaming decoder onto CPU+GPU heterogeneous platforms. Since the CPU and GPU tasks may need to communicate with each other during execution, data transfer between the host memory and the GPU global memory is necessary in code partitioning.…”

Section: Task Designmentioning

confidence: 99%

GPU acceleration of Data Assembly in Finite Element Methods and its energy implications

Tang

Chen

et al. 2013

2013 IEEE 24th International Conference on Application-Specific Systems, Architectures and Processors

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The Finite Element Method (FEM) is a numerical technique widely used in finding approximate solutions for many scientific and engineering problems. The Data Assembly (DA) stage in FEM can take up to 50% of the total FEM execution time. Accelerating DA with Graphics Processing Units (GPUs) presents challenges due to DA's mixed compute-intensive and memory-intensive workloads. This paper uses a representative finite element mini-application to explore DA acceleration on CPU+GPU platforms. Implementations based on different thread, kernel and task design approaches are developed and compared. Their performance and energy consumption are measured on four CPU+GPU and two CPU only platforms. The results show that (i) the performance and energy for different implementations on the same platform can vary significantly but the performance and energy trends are the same, and (ii) there exist performance and energy tradeoffs across some platforms if the best implementation is chosen for each of the platforms.

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Section: Task Designmentioning

confidence: 99%