GPGPU Technology Research Articles

A Method to Measure the Degree of the Favorite Location Visiting of Mobile Objects

To understand the mobility of humans or things, it is necessary to measure the degrees of location visits in everyday mobility. In this paper, we discuss measures that can present human preferences to certain locations based on location data and analysis. From raw positioning data and the concept of location clusters, which are sets of positioning data representing location areas, several measures can be deduced. First, the location point and location area can be separated because visiting a pin point location is different from visiting a certain area. Second, the number of visits to a location and the duration of a visit to a location have different meanings. Third, the rank of the location visited is sometimes more meaningful than the absolute counts. In consideration of these aspects, we established six basic measures and two derived measures. The actual calculation of each measure requires raw positioning data to be processed. The raw positioning data were collected by volunteers over several years of their everyday lives. All measures for multiple volunteers were generated and analyzed for verification. The processing of raw positioning data to generate measures requires a vast number of calculations, like big data processing. As a solution, we implemented a generation process using the programming language R; GPGPU technology was utilized to derive numerical results within areas on able time limit with considerable speed-ups, because an undesirably large amount of time was required to process measures with CPU-only machines.

Method of semantic application verification in GPGPU technology

An application development and verification method for massively parallel systems using NVIDIA GPUs is proposed. The method allows creating models at different levels of abstraction using the apparatus of marked transition systems. The compositions (product) of such systems are transformed into a Petri net, which are then analyzed by appropriate means. The proposed method allows specifying model properties by temporal logic formulas. This allows studying the properties of massively parallel systems which is almost impossible to analyze manually, since the number of execution threads in the latest NVIDIA video adapter architectures (Pascal, Volta, Turing, Ampere) is measured in hundreds of thousands or millions.

A highly accurate GPU Lattice Boltzmann method with directional interpolation for the probability distribution functions

SummaryIn this article, a highly accurate and graphics processing unit (GPU)‐accelerated Lattice Boltzmann Method (LBM) is presented. The methodology is derived from a combination of conventional and recent LBM algorithms, mainly focusing on reducing the computational time, memory allocation, and complexity of existing algorithms. The general implementation focuses on accelerating the overall methodology using GPGPU technology based on Compute Shaders from OpenGL and avoids the storage of the distribution function components to reduce the memory allocation size. Furthermore, an efficient spatial interpolation of the probability distribution function components is described, based on a directional interpolation, without unnecessary control points for the reconstruction of virtual nodes data. The present methodology, tested for spatial accuracy via two‐ and three‐dimensional Lid‐Driven Cavity benchmark cases, shows excellent agreement with the results reported in the literature. Additionally, time efficiency is analyzed by comparing different configurations for the construction of virtual streaming points.

Застосування технології GPGPU при роботі з BigData

Застосування технології GPGPU при роботі з BigData

Serial and Parallel Implementations of Hybrid Fluid Model of Information Flows in Networks with Complex Topology

This paper is dedicated to the issue of modeling information flows in networks with complex topologies and it describes a comparison of the sequential (written in the MATLAB language) and parallel (based on GPGPU technology) software implementations of the hybrid fluid model (HFM) of Internet traffic. Obtained performance estimates of both software implementations indicate a higher performance of parallel software implementation HFM. The directions of further research, the results of which will be the basis for the later development of parallel software implementation HFM are proposed.

Parallel simulation of adaptive random Boolean networks using the GPGPU technology

Parallel simulation of adaptive random Boolean networks using the GPGPU technology

Searching approximate global optimal Heilbronn configurations of nine points in the unit square via GPGPU computing

In this paper we present a method of applying the GPGPU technology to compute the approximate optimal solution to the Heilbronn problem for nine points in the unit square, namely, points $$P_1,P_2,\ldots ,P_9$$P1,P2,ź,P9 in $$[0,1]\times [0,1]$$[0,1]×[0,1] so that the minimal area of triangles $$P_iP_jP_k\,(1\le i

GPU-based parallel method of temperature field analysis in a floor heater with a controller

Abstract A parallel method enabling acceleration of the numerical analysis of the transient temperature field in an air floor heating system is presented in this paper. An initial-boundary value problem of the heater regulated by an on/off controller is formulated. The analogue model is discretized using the implicit finite difference method. The BiCGStab method is used to compute the obtained system of equations. A computer program implementing simultaneous computations on CPUand GPU(GPGPUtechnology) was developed. CUDA environment and linear algebra libraries (CUBLAS and CUSPARSE) are used by this program. The time of computations was reduced eight times in comparison with a program executed on the CPU only. Results of computations are presented in the form of time profiles and temperature field distributions. An influence of a model of the heat transfer coefficient on the simulation of the system operation was examined. The physical interpretation of obtained results is also presented.Results of computations were verified by comparing them with solutions obtained with the use of a commercial program - COMSOL Mutiphysics.

Створення і дослідження паралельних схем алгоритму Джонсона в технології GPGPU

Johnson’s all pairs shortest path algorithm application in an edge weighted, directed graph is considered. Its formalization in terms of Glushkov’s modified systems of algorithmic algebras was made. The expediency of using GPGPU technology to accelerate the algorithm is proved. A number of schemas of parallel algorithm optimized for using in GPGPU were obtained. Suggested approach to the implementation of the schemes obtained using computing architecture NVIDIA CUDA. An experimental study of improved performance by using GPU for computations was made.

Дослідження можливостей графічних процесорів при реалізації алгоритмів симетричного шифрування

The analysis of GPUs information technology has been conducted. The possibility of using them as additional computational resources to solve some problems that require significant computing resources (including cryptographic tasks) has been determined. Modern graphic processors can contain up to thousands of cores. One can achieve greater performance by paralleling. Block ciphers are an excellent target for parallelization - while encrypting large amounts of data you can encrypt one block in the flow. AES block encryption algorithm using OpenCL has been implemented. OpenCL is a completely open standard; its use is not subjected to royalties. The purpose of OpenCL is to complement OpenGL and OpenAL, which are open branch standards for three-dimensional computer graphics and sound, using GPU capabilities. OpenCL standard provides parallelism at the level of instructions and data level as well and is the development of GPGPU technology, which uses stream processors for non-graphical data. OpenCL appendix consists of a host programme and a set of nuclei (kernels). The dependence of speed encryption upon data volume has been studied

Evaluation of fermion loops applied to the calculation of the [formula omitted] mass and the nucleon scalar and electromagnetic form factors

The exact evaluation of the disconnected diagram contributions to the flavor-singlet pseudo-scalar meson mass, the nucleon σ-term and the nucleon electromagnetic form factors is carried out utilizing GPGPU technology with the NVIDIA CUDA platform. The disconnected loops are also computed using stochastic methods with several noise reduction techniques. Various dilution schemes as well as the truncated solver method are studied. We make a comparison of these stochastic techniques to the exact results and show that the number of noise vectors depends on the operator insertion in the fermion loop.

1806 非構造格子に基づく浅水波方程式解法のGPGPUによる高速化(OS18-2.計算力学におけるGPUコンピューティング(2),OS・一般セッション講演)

A rapid numerical simulator was constructed by applying the GPGPU technology to solve the 2-dimensional shallow water equations. This simulator is based on an explicit finite volume scheme combined with unstructured triangular meshes. With the power of GPGPU, this simulator can be used to simulate large scale Tsunami movement. Furthermore, by using unstructured meshes, complex boundary could also be represented well. The simulation results are verified and validated, and show that it is sufficiently accurate for real-world simulations.

LiDAR data reduction using vertex decimation and processing with GPGPU and multicore CPU technology

Airborne light detection and ranging (LiDAR) topographic data provide highly accurate representations of the earth's surface. However, large data volumes pose computing issues when disseminating and processing the data. The main goals of this paper are to evaluate a vertex decimation algorithm used to reduce the size of the LiDAR data and to test parallel computation frameworks, particularly multicore CPU and GPU, in processing the data. In this paper we use a vertex decimation technique to reduce the number of vertices available in a triangulated irregular network (TIN) representation of LiDAR data. In order to validate and verify the algorithm, the authors have used last returns only (LRO) and all returns (AR) of points from four tiles of LiDAR data taken from flat and undulating terrains. The results for flat terrain data showed decimation rates of roughly 95% for last returns only and 55% for all returns without significant loss of accuracy in terrain representation. Accordingly, file sizes were reduced by about 96.5% and 60.5%. The processing speed greatly benefited from parallel programming using the multicore CPU framework. The GPU usage demonstrated an additional impediment caused by noncomputational overhead. Nonetheless, tremendous acceleration was demonstrated by the GPU environment in the computational part alone.

High Performance Computing: CUDA as a Supporting Technology for Next Generation Augmented Reality Applications

The main purpose of this survey is presenting the potential of GPGPU technology for real time markerless augmented reality related processing. CUDA is a GPGPU technology developed by NVIDIA that allows programmers to use the C programming language to code algorithms for execution on the GPU. Applications that require mathematically intensive computation of large amounts of data are ideal targets for GPU computing. In this survey, CUDA architecture will be depicted, together with an optimized programming model for obtaining better results using the parallel approach. A case study, mainly related to tracking algorithms, will also be shown in order to demonstrate the performance improvement in comparison to sequential approaches.