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The conservative global gyrokinetic toroidal full-f five-dimensional Vlasov simulation (GT5D) is a nuclear fusion simulation program designed to analyze turbulence phenomena in tokamak plasma. In this research, we optimize it for graphics processing unit (GPU) clusters with multiple GPUs on each node. Based on the profile results of a GT5D on a CPU node, it was decided to offload the entire time development...
Utilizing accelerators in heterogeneous systems is an established approach for designing peta-scale applications. Today, CUDA offers a rich programming interface for GPU accelerators but requires developers to incorporate several layers of parallelism on both CPU and GPU. From this increasing program complexity emerges the need for sophisticated performance tools. This work contributes by analyzing...
This paper presents a technique for interactively colliding with and deforming mesostructures at a per-texel level. It is compatible with a broad range of existing mesostructure rendering techniques including both safe and unsafe ray-height field intersection algorithms. This technique is able to replace traditional 3D geometrical deformations (vertex-based) with 2D image space operations (pixel-based)...
In this paper, we present design and implementation of a fast runtime visualizer for a GPU-based 3D-FDTD electromagnetic simulation. We focus on improving the productivity of simulator development without compromising simulation performance. In order to keep the portability, we implemented a visualizer with the MVC model, where simulation kernels and visualization process were completely separated...
Modern General-Purpose computation on Graphics Processing Units (GPGPUs) explore parallelism in applications by building massively parallel architecture and apply multithreading technology to hide the instruction and memory latencies. Such architectures become increasingly popular for parallel applications using CUDA/OpenCL programming languages. In this paper, we investigate thread scheduling algorithms...
Frequent pattern mining is a field with many practical applications, where large computational power and speed are needed. Many state-of-the-art frequent pattern mining applications are an inefficient solutions for both shared memory and multiprocessor systems due to problems with parallelism and memory. One of possible solutions to the problem is the use of Graphics Processing Unit (GPU) in the system...
More and more computationally intensive scientific applications make use of hardware accelerators like general purpose graphics processing units (GPGPUs). Compared to software development for typical multi-core processors their programming is fairly complex and needs hardware specific optimizations to utilize the full computing power. To achieve high performance, critical parts of a program have to...
In this paper, we first discussed the video decoding standard and its architecture, and then analyzed the decoding complexity of each process. By using the benefit of the CUDA programming model, and taking advantages of GPU to optimize the decoding process of MC (motion compensation) and CSC(color space conversion) that are very time consuming, we proposed a MC accelerating method based on CUDA, and...
Many general-purpose applications exploit Graphics Processing Units (GPUs) by executing a set of well-known dataparallel primitives. Those primitives are usually invoked from the host many times, so their throughput has a great impact on the performance of the overall system. Thus, the study of novel algorithmic strategies to optimize their implementation on current devices is an interesting topic...
Simulation of the behaviour of a ship operating in pack ice is a computationally intensive process to which General Purpose Computing on Graphical Processing Units (GPGPU) can be applied. In this paper we present an efficient parallel implementation of such a simulator developed using the NVIDIA Compute Unified Device Architecture (CUDA). We have conducted an experiment to measure the relative performance...
GPUs and other accelerators are available on many different devices, while GPGPU has been massively adopted by the HPC research community. Although a plethora of libraries and applications providing GPU support are available, the need of implementing new algorithms from scratch, or adapting sequential programs to accelerators, will always exist. Writing CUDA or OpenCL codes, although an easier task...
This paper elaborates on a new, fresh parallel optimization algorithm specially engineered to run on Graphic Processing Units (GPUs). The underlying operation relates to Systolic Computation. The algorithm, called Systolic Genetic Search (SGS) is based on the synchronous circulation of solutions through a grid of processing units and tries to profit from the parallel architecture of GPUs. The proposed...
Sparse matrix-vector multiplication (spMVM) is the dominant operation in many sparse solvers. We investigate performance properties of spMVM with matrices of various sparsity patterns on the nVidia "Fermi" class of GPGPUs. A new "padded jagged diagonals storage" (pJDS) format is proposed which may substantially reduce the memory overhead intrinsic to the widespread ELLPACK-R scheme...
Dynamic scheduling and varying decomposition granularity are well-known techniques for achieving high performance in parallel computing. Heterogeneous clusters with highly data-parallel processors, such as GPUs, present unique problems for the application of these techniques. These systems reveal a dichotomy between grain sizes: decompositions ideal for the CPUs may yield insufficient data-parallelism...
Graphics processing units (GPUs) are increasingly critical for general-purpose parallel processing performance. GPU hardware is composed of many streaming multiprocessors, each of which employs the single-instruction multiple-data (SIMD) execution style. This massively parallel architecture allows GPUs to execute tens of thousands of threads in parallel. Thus, GPU architectures efficiently execute...
Efficient collision detection is a requirement for a large number of games. With the release of devices that enable full-body interaction, new challenges arise in this area. In this paper we present a technique for dynamic construction of octrees for collision detection, based on a cloud of points using GPGPU techniques. Since some algorithms are not suitable for the GPU processing model, our technique...
Hybrid CPU/GPU computing architecture has received great attention from the researchers of high performance computing. This new architecture provides higher computation performance than that uses only CPUs for data computation. However, the programming on this computing architecture is not easy for programmers since they have to learn the programming APIs of GPU and handle data communication between...
Nowadays microscopic analysis of tissue samples is done more and more by using digital imagery and special immunodiagnostic software. These are typically specific applications developed for one distinct field, but some subroutines are commonly repeated, for example several applications contain steps that can detect cell nuclei in a sample image. The aim of our research is developing a new data parallel...
The main aim of this work is to show, how GPGPUs can facilitate certain type of image processing methods. The software used in this paper is used to detect special tissue part, the nuclei on (HE - hematoxilin eosin) stained colon tissue sample images. Since pathologists are working with large number of high resolution images - thus require significant storage space -, one feasible way to achieve reasonable...
Map projection is a key task in cartography that transforms the geographical coordinates from one coordinate system to another. It has been widely used in the Geographic Information System application. However, map projection is a very time-consuming task, and fast processing speed is often required in interactive GIS scenarios. Parallel computation provides an opportunity to reduce run times. Nowadays,...
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