The Experts below are selected from a list of 51 Experts worldwide ranked by ideXlab platform
Shuwu Zhang - One of the best experts on this subject based on the ideXlab platform.
-
a Histogram Algorithm for fast audio retrieval
International Symposium Conference on Music Information Retrieval, 2005Co-Authors: Wei Liang, Shuwu ZhangAbstract:This paper describes a fast audio detection method for specific audio retrieval in the AV stream. The method is a Histogram matching Algorithm based on structural and perceptual features. This Algorithm extracts audio features based on human perception on the sound scene and locates the special audio clip by fast Histogram matching. Experimental results based on the advertisement detection in TV program showed that the Algorithm can achieve a very high overall precision and recall rate both about 97% with very fast search time about 1/40 on real time.
Köfinger J. - One of the best experts on this subject based on the ideXlab platform.
-
CADISHI: Fast parallel calculation of particle-pair distance Histograms on CPUs and GPUs
'Elsevier BV', 2019Co-Authors: Reuter K., Köfinger J.Abstract:We report on the design, implementation, optimization, and performance of the CADISHI software package, which calculates Histograms of pair-distances of ensembles of particles on CPUs and GPUs. These Histograms represent 2-point spatial correlation functions and are routinely calculated from simulations of soft and condensed matter, where they are referred to as radial distribution functions, and in the analysis of the spatial distributions of galaxies and galaxy clusters. Although conceptually simple, the calculation of radial distribution functions via distance binning requires the evaluation of O(N2) particle-pair distances where N is the number of particles under consideration. CADISHI provides fast parallel implementations of the distance Histogram Algorithm for the CPU and the GPU, written in templated C++ and CUDA. Orthorhombic and general triclinic periodic boxes are supported, in addition to the non-periodic case. The CPU kernels feature cache blocking, vectorization and thread-parallelization to obtain high performance. The GPU kernels are tuned to exploit the memory and processor features of current GPUs, demonstrating Histogramming rates of up to a factor 40 higher than on a high-end multi-core CPU. To enable high-throughput analyses of molecular dynamics trajectories, the compute kernels are driven by the Python-based CADISHI engine. It implements a producer-consumer data processing pattern and thereby enables the complete utilization of all the CPU and GPU resources available on a specific computer, independent of special libraries such as MPI, covering commodity systems up to high-end high-performance computing nodes. Data input and output are performed efficiently via HDF5. In addition, our CPU and GPU kernels can be compiled into a standard C library and used with any application, independent from the CADISHI engine or Python. The CADISHI software is freely available under the MIT license
Reuter, Mendeley K Data) - One of the best experts on this subject based on the ideXlab platform.
-
CADISHI: Fast parallel calculation of particle-pair distance Histograms on CPUs and GPUs
2018Co-Authors: Reuter, Mendeley K Data)Abstract:We report on the design, implementation, optimization, and performance of the CADISHI software package, which calculates Histograms of pair-distances of ensembles of particles on CPUs and GPUs. These Histograms represent 2-point spatial correlation functions and are routinely calculated from simulations of soft and condensed matter, where they are referred to as radial distribution functions, and in the analysis of the spatial distributions of galaxies and galaxy clusters. Although conceptually simple, the calculation of radial distribution functions via distance binning requires the evaluation of O(N^2) particle-pair distances where N is the number of particles under consideration. CADISHI provides fast parallel implementations of the distance Histogram Algorithm for the CPU and the GPU, written in templated C++ and CUDA. Orthorhombic and general triclinic periodic boxes are supported, in addition to the non-periodic case. The CPU kernels feature cache-blocking, vectorization and thread-parallelization to obtain high performance. The GPU kernels are tuned to exploit the memory and processor features of current GPUs, demonstrating Histogramming rates of up to a factor 40 higher than on a high-end multi-core CPU. To enable high-throughput analyses of molecular dynamics trajectories, the compute kernels are driven by the Python-based CADISHI engine. It implements a producer–consumer data processing pattern and thereby enables the complete utilization of all the CPU and GPU resources available on a specific computer, independent of special libraries such as MPI, covering commodity systems up to high-end HPC nodes. Data input and output are performed efficiently via HDF5. In addition, our CPU and GPU kernels can be compiled into a standard C library and used with any application, independent from the CADISHI engine or Python. The CADISHI software is freely available under the MIT license
Köfinger Jürgen - One of the best experts on this subject based on the ideXlab platform.
-
CADISHI: Fast parallel calculation of particle-pair distance Histograms on CPUs and GPUs
'Elsevier BV', 2018Co-Authors: Reuter Klaus, Köfinger JürgenAbstract:We report on the design, implementation, optimization, and performance of the CADISHI software package, which calculates Histograms of pair-distances of ensembles of particles on CPUs and GPUs. These Histograms represent 2-point spatial correlation functions and are routinely calculated from simulations of soft and condensed matter, where they are referred to as radial distribution functions, and in the analysis of the spatial distributions of galaxies and galaxy clusters. Although conceptually simple, the calculation of radial distribution functions via distance binning requires the evaluation of $\mathcal{O}(N^2)$ particle-pair distances where $N$ is the number of particles under consideration. CADISHI provides fast parallel implementations of the distance Histogram Algorithm for the CPU and the GPU, written in templated C++ and CUDA. Orthorhombic and general triclinic periodic boxes are supported, in addition to the non-periodic case. The CPU kernels feature cache-blocking, vectorization and thread-parallelization to obtain high performance. The GPU kernels are tuned to exploit the memory and processor features of current GPUs, demonstrating Histogramming rates of up to a factor 40 higher than on a high-end multi-core CPU. To enable high-throughput analyses of molecular dynamics trajectories, the compute kernels are driven by the Python-based CADISHI engine. It implements a producer-consumer data processing pattern and thereby enables the complete utilization of all the CPU and GPU resources available on a specific computer, independent of special libraries such as MPI, covering commodity systems up to high-end HPC nodes. Data input and output are performed efficiently via HDF5. (...) The CADISHI software is freely available under the MIT license.Comment: 19 page
Wei Liang - One of the best experts on this subject based on the ideXlab platform.
-
a Histogram Algorithm for fast audio retrieval
International Symposium Conference on Music Information Retrieval, 2005Co-Authors: Wei Liang, Shuwu ZhangAbstract:This paper describes a fast audio detection method for specific audio retrieval in the AV stream. The method is a Histogram matching Algorithm based on structural and perceptual features. This Algorithm extracts audio features based on human perception on the sound scene and locates the special audio clip by fast Histogram matching. Experimental results based on the advertisement detection in TV program showed that the Algorithm can achieve a very high overall precision and recall rate both about 97% with very fast search time about 1/40 on real time.