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The Computed Tomography (CT) is a imaging method based on X-rays to obtain cross-sectional images from an object. It is a widely used method in several areas, such as medicine, archeology or material sciences. Tomographic reconstruction techniques, use the projections of images from multiple directions. There are several algorithms for this purpose but can be classified according to their reconstruction...
GPU-based clusters are widely chosen for accelerating a variety of scientific applications in high-end cloud environments. With their growing popularity, there is a necessity for improving the system throughput and decreasing the turnaround time for co-executing applications on the same GPU device. However, resource contention among multiple applications on a multi-tasked GPU leads to the performance...
Presented paper explains general purpose approach to the parallel pixel processing on GPU. It presents essential dataset structuring, correct type assignment and kernel configuration for CUDA application interface. Paper also explains data movement and optimal computation saturation. Transfers are also analyzed in correlation with the computation especially for the embarrassingly parallel problem...
Performance modeling plays an important role for optimal hardware design and optimized application implementation. This paper presents a very low overhead performance model, called VLAG, to approximate the data localities exploited by GPU kernels. VLAG receives source code-level information to estimate per memory-access instruction, per data array, and per kernel localities within GPU kernels. VLAG...
GPUs continue to increase the number of compute resources with each new generation. Many data-parallel applications have been re-engineered to leverage the thousands of cores on the GPU. But not every kernel can fully utilize all the resources available. Many applications contain multiple kernels that could potentially be run concurrently. To better utilize the massive resources on the GPU, device...
Fault-tolerance is becoming increasingly important as we enter the era of exascale computing. Increasing the number of cores results in a smaller mean time between failures, and consequently, higher probability of errors. Among the different software fault tolerance techniques, checkpoint/restart is the most commonly used method in supercomputers, the de-facto standard for large-scale systems. Although...
Due to energy efficiency, heterogeneous computing is gaining more and more attention. Since FPGA implementations are time consuming, high-level synthesis (HLS) is used to close the productivity gap. OpenCL has become accepted as a good programming model for HLS, due to its portability, good capability of design verification and rich instruction set. This work implements different optimization strategies...
We propose a design for a fine-grained lock-based skiplist optimized for Graphics Processing Units (GPUs). While GPUs are often used to accelerate streaming parallel computations, it remains a significant challenge to efficiently offload concurrent computations with more complicated data-irregular access and fine-grained synchronization. Natural building blocks for such computations would be concurrent...
The performance of commodity video-gaming embedded devices (consoles, graphics cards, tablets, etc.) has been advancing at a rapid pace owing to strong consumer demand and stiff market competition. Gaming devices are currently amongst the most powerful and cost-effective computational technologies available in quantity. In this article, we evaluate a sample of current generation video-gaming devices...
This paper deals with the evaluation of FPGAs resurgence for hardware acceleration applied to computed tomography on the back-projection operator used in iterative reconstruction algorithms. We focus our attention on the tools developed by FPGAs manufacturers, in particular the Intel FPGA SDK for OpenCL, that promises a new level of hardware abstraction from the developer's perspective, allowing a...
Nowadays, there are many embedded systems with different architectures that have incorporated GPUs. However, it is difficult to develop CPU-GPU embedded systems using component-based development (CBD), since existing CBD approaches have no support for GPU development. In this context, when targeting a particular CPU-GPU platform, the component developer is forced to construct hardware-specific components,...
We present a set of new batched CUDA kernels for the LU factorization of a large collection of independent problems of different size, and the subsequent triangular solves. All kernels heavily exploit the registers of the graphics processing unit (GPU) in order to deliver high performance for small problems. The development of these kernels is motivated by the need for tackling this embarrasingly-parallel...
Sparse general matrix-matrix multiplication (SpGEMM) is one of the key kernels of preconditioners such as algebraic multigrid method or graph algorithms. However, the performance of SpGEMM is quite low on modern processors due to random memory access to both input and output matrices. As well as the number and the pattern of non-zero elements in the output matrix, important for achieving locality,...
Adaptive Dynamic Programming (ADP) with critic-actor architecture is a useful way to achieve online learning control. The algorithm Gaussian-Kernel Adaptive Dynamic Programming (GK-ADP) that has been developed before has a kind of two-phase iteration, which not only approximates value function, but also optimizes hyper-parameters simultaneously. However, just like most iteration algorithms are applied...
Coevolutionary particle swarm optimization (CPSO) algorithm has been investigated and applied in the real world widely. When tackling the large-scale and complex real time optimization problems, the running time of CPSO algorithm is a barrier. In this paper, Graphics Processing Unit (GPU) is introduced to provide speedup in order to meet the real time requirements. The CPSO algorithm has been implemented...
In this paper, we use a restoration method that rapidly restores blurred images using local patches proposed by Senshiki et al. [1]. The computation time is significantly reduced by that method, but it is not yet a practical. Therefore, we propose to accelerate by implementing the image restoration processing on GPU. By measuring the processing time of the image restoration, we show the superiority...
CNNs (Convolutional Neural Networks) have demonstrated superior results in a wide range of applications. However, the time-consuming convolution operations required by CNNs pose great challenges to designers. GPGPUs (General Purpose Graphic Processing Units) have been widely used to exploiting the massive parallelism of convolution operations. This paper proposes a software-based loop-unrolling technique...
This study presents a new algorithm and corresponding statistical package for estimating optimal bandwidth for a nonparametric kernel regression. Kernel regression is widely used in Economics, Statistics, and other fields. The formula for the optimal "bandwidth," or smoothing parameter, is well-known. In practice, however, the computational demands of estimating the optimal bandwidth have...
Because sparse matrix-vector multiplication (SpMV) is an important and widely used computational kernel in many real-world applications, it behooves us to accelerate SpMV on modern multi- and many-core architectures. While many storage formats have been developed to facilitate SpMV operations, the compressed sparse row (CSR) format is still the most popular and general storage format. However, parallelizing...
Background Subtraction is the major important step in many image processing applications which can be applied in much of video surveillances. The major result of this method is accuracy as well as processing time. So we mainly focused on these two challenges. We parallelized the Two Layered CodeBook Model on Graphical Processing Unit (GPU) for increasing the processing speed and the accuracy of the...
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