This paper examines the feasibility of implementing core algorithms from Radiance using a new type ray tracing engine optimized for highly parallel graphics hardware environments. It presents solutions to a number of implementation challenges. First, the Radiance data format is reinterpreted as a set of buffered data arrays compatible with graphics processing unit (GPU) memory. Second, the ray ...

Mapping algorithms in an efficient way to the target hardware poses a challenge for algorithm designers. This is particular true for heterogeneous systems hosting accelerators like graphics cards. While algorithm developers have profound knowledge of the application domain, they often lack detailed insight into the underlying hardware of accelerators in order to exploit the provided processing ...

In this paper, the application of High-Performance Computing (HPC) techniques in processing of geospatial data in Geographic Information System (GIS) is presented. We evaluate two parallel/distributed architectures and programming models: Message Passing Interface (MPI) over Network of Workstations (NoW) and Compute Unified Device Architecture (CUDA) on Graphics Processing Unit (GPU) in well-kn...

In this study we test the idea of using a graphics processing unit (GPU) as an embedded co-processor for real-time speed-limit sign detection. We implement a system that operates in real-time within the computational limits of contemporary embedded general-purpose processors. The input to the system is a set of grayscale videos recorded from a camera mounted in a vehicle. We implement a new tec...

We propose a system that provides interactive denoising, vortex detection, and visualization of vector data. All the major phases of the system are implemented on a GPU (graphics processing unit). Once the vector field has been loaded onto the graphics adapter, no intermediate results need to be read back by the application. By taking advantage of modern GPUs’ parallel processing capabilities, ...

Recently, there have been some emerging processor designs that the CPU and the GPU (Graphics Processing Unit) are integrated in a single chip and share Last Level Cache (LLC). However, the main memory bandwidth of such coupled CPU-GPU architectures can be much lower than that of a discrete GPU. As a result, current GPU query coprocessing paradigms can severely suffer from memory stalls. In this...

The k nearest neighbor (kNN) search is a computationally intensive application critical to fields such as image processing, statistics, and biology. Recent works have demonstrated the efficacy of k-d tree based implementations on multi-core CPUs. It is unclear, however, whether such tree based implementations are amenable for execution in high-density processors typified today by the graphics p...

This paper presents a comprehensive survey on parallelizing computations involved in optimization problem on Graphics Processing Unit (GPU) using CUDA (Compute Unified Design Architecture). GPU have multithread cores with high memory bandwidth which allow for greater ease of use and also more radially support a layer body of applications. Many researchers have reported significant speedups with...

This contribution shows how unsupervised Markovian segmentation techniques can be accelerated when implemented on graphics hardware equipped with a Graphics Processing Unit (GPU). Our strategy exploits the intrinsic properties of local interactions between sites of a Markov Random Field model with the parallel computation ability of a GPU. This paper explains how classical iterative site-wise-u...

We propose a simple method to implement floating-point vector math operations and matrix multiplication on graphics hardware, focusing on identification of details, in both software and hardware, which affect performance and ease of use. Before widespread adoption of the graphics processing unit (GPU) as another computation processor, we must address the need of application interfaces (APIs) th...