high performance computing on graphics processing units: hgpu.org

A parallel implementation of root finding on an SIMD application accelerator is reported. These are roots of stochastic differential equations in the computational finance domain which require a stochastic simulation to be performed for each evaluation of the pricing function. Experiments show that a speedup of 15X can be achieved over using a stand-alone CPU […]

Graphics Processing Units (GPUs) are highly parallel Single Instruction Multiple Data (SIMD) engines, with extremely high degrees of available hardware parallelism. The task of implementing a software routine on a GPU currently requires significant manual design, iteration and experimentation. This paper presents an automated approach to partition a software application into kernels (which are executed […]

Understanding the neural circuitry of the retina requires us to map the connectivity of individual neurons in large neuronal tissue sections and analyze signal communication across processes from the electron microscopy images. One of the major bottlenecks in the critical path is the image mosaicing process where 2D slices are assembled from scanned microscopy image […]

In order for scientists to learn more about molecular biology, it is imperative that they have the ability to construct accurate models that predict the reactions of species of molecules. Generating these models using deterministic approaches is not feasible as these models may violate some of the assumptions underlying classical differential equations models (e.g., small […]

Since the introduction of organized trading of options for commodities and equities, computing fair prices for options has been an important problem in financial engineering. A variety of numerical methods, including Monte Carlo methods, binomial trees, and numerical solution of stochastic differential equations, are used to compute fair prices. Traders and brokerage firms constantly strive […]

Since CUDA programing model appeared on the general purpose computations, the developers can extract all the power contained in GPUs (Graphics Processing Unit) across many computational domains. Among these domains, P systems or membrane systems provide a high level computational modeling framework that allows, in theory, to obtain polynomial time solutions to NP-complete problems by […]

The finite element method (FEM) is widely used for numerical solution of partial differential equations. Two computationally expensive tasks have to be performed in FEM – equations assembly and solution of the system of equations. We present mapping of the equations assembly problem for StVenant-Kirchhoff material to GPU computation model and show results of its […]

In recent years, Graphics Processing Units (GPUs) have emerged as a powerful accelerator for general-purpose computations. GPUs are attached to every modern desktop and laptop host CPU as graphics accelerators. GPUs have over a hundred cores with lots of parallelism. Initially, they were used only for graphics applications such as image processing and video games. […]

We describe herein the APElink+ board, a PCIe interconnect adapter featuring the latest advances in wire speed and interface technology plus hardware support for a RDMA programming model and experimental acceleration of GPU networking; this design allows us to build a low latency, high bandwidth PC cluster, the APEnet+ network, the new generation of our […]

In this paper, we present an implementation of extremely randomized trees (ERT), a supervised machine learning algorithm utilizing decision tree ensembles, in CUDA, nVidia’s GPU parallel programming extensions for C/C++. We describe the CUDA programming model and NVIDIA GPU architectures and explain the design tradeoffs that we made to exploit various forms of parallelism available […]

This paper discusses the emergence of graphics processing units (GPUs) that contain architecture features for accelerating non-graphics (or GPGPU) applications. It provides an introduction for those interested in undertaking research at the intersection of manycore computing and GPU architecture. First, the motivation for using GPUs for non-graphics processing rather than developing specialized hardware is outlined. […]

Recent advances in Graphics Processing Units (GPUs) provide opportunities to exploit GPUs for non-graphics applications. Scientific computation is inherently parallel, which is a good candidate to utilize the computing power of GPUs. This report investigates QR factorization, which is an important building block of scientific computation. We analyze different mapping mtheods of QR factorization on […]