high performance computing on graphics processing units: hgpu.org

This paper focuses on the current revolution in using the GPU for general-purpose computations (GPGPU), and how to maximally exploit its powerful resources. Recently, the advent of next-generation GPGPU replaced the traditional way of exploiting the graphics hardware. We have migrated real-time depth image-based rendering – for use in contemporary 3DTV technology – and noticed […]

Memory optimization is an important strategy to gain high performance for sequence alignment implemented by CUDA on GPGPU. Smith-Waterman (SW) algorithm is the most sensitive algorithm widely used for local sequence alignment but very time consuming. Although several parallel methods have been used in some studies and shown good performances, advantages of GPGPU memory hierarchy […]

This paper proposes a new connected component labeling algorithm for GPGPU applications based on NVIDIA’s CUDA. Various approaches and algorithms for connected component labeling with minimal execution time were designed, but the most of them have been focused on optimizing CPU algorithm. Therefore it is hard to apply these approaches to GPGPU programming models such […]

The GPUs are emerging as a general-purpose high-performance computing device. Growing GPGPU research has made numerous GPGPU workloads available. However, a systematic approach to characterize these benchmarks and analyze their implication on GPU microarchitecture design evaluation is still lacking. In this research, we propose a set of microarchitecture agnostic GPGPU workload characteristics to represent them […]

Exact inference is a key problem in exploring probabilistic graphical models. The computational complexity of inference increases dramatically with the parameters of the graphical model. To achieve scalability over hundreds of threads remains a fundamental challenge. In this paper, we use a lightweight scheduler hosted by the CPU to allocate cliques in junction trees to […]

Heterogeneous multi-core platforms are increasingly prevalent due to their perceived superior performance over homogeneous systems. The best performance, however, can only be achieved if tasks are accurately mapped to the right processors. OpenCL programs can be partitioned to take advantage of all the available processors in a system. However, finding the best partitioning for any […]

Main objective of this paper is to outline possible ways how to achieve a substantial acceleration in case of advection-diffusion equation (A-DE) calculation, which is commonly used for a description of the pollutant behavior in atmosphere. A-DE is a land of partial differential equation (PDE) and in general case it is usually solved by numerical […]

In this paper, we combine and extend two of our previous works to provide a more complete solution for the GPU acceleration of the Method of Moments, using CUDA by NVIDIA. To this end, the formulations of the original 1982 Rao-Wilton-Glisson paper are revisited, and the scattering analysis of a square PEC plate is considered […]

In this paper we designed a remote sensing image data fusion algorithm on GPU (Graphics Processing Unit) using the programmability of GPU which is a parallel vector processor. Both of the forward IHS and inverse IHS transform computation were mapped into GPU. We realized parallel rendering and output of the three components of the IHS, […]

Fluid simulations based on Smoothed Particle Hydrodynamics (SPH) have been widely used for generating complex motion of fluid. However,implementation of searching particle neighbors on graphics processing unit (GPU) can not be satisfied till now. In this paper, we present a new grid-based neighbor search method on GPU for GPU-based SPH fluid simulation. Using this new […]

Advances in nonlinear dimensionality reduction provide a way to understand and visualize the underlying structure of complex data sets. The performance of large-scale nonlinear dimensionality reduction is of key importance in data mining, machine learning, and data analysis. In this paper, we concentrate on improving the performance of nonlinear dimensionality reduction using large-scale data sets […]

Physic-based fluid simulation is used extensively nowadays; however the traditional serial algorithm can’t satisfy the real-time requirement due to its complexity and computeintensive. The development of modern GPU makes this possible. In this paper, a Smoothed Particle Hydrodynamics (SPH) method for incompressible fluid was implemented using CUDA on GPU. Since the algorithm was executed on […]