Implementation of Scale and Rotation Invariant On-Line Object Tracking Based on CUDA

Miao, Qiguang; Wang, Guijin; Lin, Xiao

doi:10.1587/transinf.e94.d.2549

Cited by 3 publications

(1 citation statement)

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“…CUDA is a heterogeneous computing model that involves both the CPU and the GPU, and has been used for speeding up a large number of applications [11]- [13]. In the CUDA model, the GPU is regarded as a coprocessor which is capable of executing a great number of threads in parallel.…”

Section: Gpu Architecture and Cuda Modelmentioning

confidence: 99%

Auto-Tuning of Thread Assignment for Matrix-Vector Multiplication on GPUs

Wang

Zhu

et al. 2013

IEICE Trans. Inf. & Syst.

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SUMMARYModern GPUs have evolved to become a more general processor capable of executing scientific and engineering computations. It provides a highly parallel computing environment due to its large number of computing cores, which are suitable for numerous data parallel arithmetic computations, particularly linear algebra operations. The matrixvector multiplication is one of the most important dense linear algebraic operations. It is applied to a diverse set of applications in many fields and must therefore be fully optimized to achieve a high-performance. In this paper, we proposed a novel auto-tuning method for matrix-vector multiplication on GPUs, where the number of assigned threads that are used to compute one element of the result vector can be auto-tuned according to the size of matrix. On the Nvidia's GPU GTX 650 with the most recent Kepler architecture, we developed an auto-tuner that can automatically select the optimal number of assigned threads for calculation. Based on the auto-tuner's result, we developed a versatile generic matrix-vector multiplication kernel with the CUDA programming model. A series of experiments on different shapes and sizes of matrices were conducted for comparing the performance of our kernel with that of the kernels from CUBLAS 5.0, MAGMA 1.3 and a warp method. The experiments results show that the performance of our matrix-vector multiplication kernel is close to the optimal behavior with increasing of the size of the matrix and has very little dependency on the shape of the matrix, which is a significant improvement compared to the other three kernels that exhibit unstable performance behavior for different shapes of matrices.

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Section: Gpu Architecture and Cuda Modelmentioning

confidence: 99%