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We propose a new method derived from DACCER (Distributed Assessment of the Closeness CEntrality Ranking): the modified DACCER (MDACCER), for assessing traditional closeness centrality ranking. MDACCER presents a relaxation that allows it to take advantage of massively parallel environments like General Purpose Graphics Processing Units (GPGPUs). Traditional DACCER proposal assesses Closeness centrality...
A shift is underway in high performance computing (HPC) towards heterogeneous parallel architectures that emphasize medium and fine grain thread parallelism. Many scientific computing algorithms, including simple finite-differencing methods, have already been mapped to heterogeneous architectures with order-of-magnitude gains in performance as a result. Recent case studies examining high-resolution...
Combining several types of devices and architectures is at the heart of heterogeneous computing's power efficiency advantage, but the strength of heterogeneous systems is also their Achilles heel, i.e. the diversity of the devices and ecosystems needed to maintain them present major technological challenges. Some of the biggest challenges are in the realm of system programing. We believe that for...
Sharpness is an algorithm used to sharpen images. As the increase of image size, resolution, and the requirements for real-time processing, the performance of sharpness needs to get improved greatly. The independent pixel calculation of sharpness makes a good opportunity to use GPU to largely accelerate the performance. However, to transplant it to GPU, one challenge is that sharpness involves several...
In this paper, we would like to introduce a GPU accelerated solver for systems of linear equations with an infinite precision. The infinite precision means that the system can provide a precise solution without any rounding error. These errors usually come from limited precision of floating point values within their natural computer representation. In a simplified description, the system is using...
The Single Instruction Multiple Thread (SIMT) architecture based, Graphic Processing Units (GPUs) are emerging as more efficient than Multiple Instruction Multiple Data (MIMD) architectures in exploiting parallelism. A GPU has numerous shader cores and thousands of simultaneous finegrained active threads. These threads are grouped into Cooperative Thread Arrays (CTAs). All the threads within a CTA...
Intelligent GPU cache bypassing can improve the efficiency of using GPU memory bandwidth, which can benefit GPU performance. In this paper, we study a pure hardware-based GPU cache bypassing method that can be applied to GPU applications without having to recompile the programs. Moreover, we introduce a hybrid method that can exploit profiling information to further enhance the hardware-based bypassing...
Intuitionistic fuzzy edge detection algorithm has been used for the signification or characterization of images. It has been designed by experts and the algorithm provides to aim to minimize errors. However, it has a fixed value for thresholding. In this paper, a hybrid algorithm has been developed using the Otsu method which is calculated a threshold value depending on the images. To be applicable...
We present JolokiaC++ a compiler framework to ease coding of irregular data applications on GPUs. The effectiveness of the compiler and runtime systems of JolokiaC++ is tested using three kernels IRREG, MOLDYN and NBF, executed on NVIDIA GPUs. We developed extensions for the generic parallel constructs that allow portable and efficient programming of codes with irregular accesses on the GPU. We present...
Recently, convolutional networks have achieved great successes in the field of computer vision. In order to improve the efficiency of convolutional networks, large amount of solutions focusing on training algorithms and parallelism strategies have been proposed. In this paper, a novel algorithm based on look-up table is proposed to speed up convolutional networks with small filters by applying GPU...
There are many scientific applications ranging from weather prediction to oil and gas exploration that requires high-performance computing. It aids industries and researchers to enrich further their advancements. With the advent of general purpose computing over GPUs, most of the applications above are shifting towards High-Performance Computing (HPC). Agent-based crowd simulation is one of the candidates...
The integration of spatial information into spectral unmixing process has attracted much attention in recent years. Several approaches have been developed to incorporate spatial considerations into the endmember extraction/estimation procedure. Spatial preprocessing algorithms are one of the most commonly adopted techniques to guide endmember identification algorithms in terms of the spatial characteristics...
Graphics Processing Units (GPUs) have been successfully used to accelerate scientific applications due to their computation power and the availability of programming languages that make more approachable writing scientific applications for GPUs. However, since the programming model of GPUs requires offloading all the data to the GPU memory, the memory footprint of the application is limited to the...
Massively parallel computing is applied extensively in various scientific and engineering domains. With the growing interest in many-core architectures and due to the lack of explicit support for inter-block synchronization specifically in GPUs, synchronization becomes necessary to minimize inter-block communication time. In this paper, we have proposed two new inter-block synchronization techniques:...
Scientific applications need to be moved among supercomputers, such as Tianhe-2 and TSUBAME 2.5. OpenACC provides a directive-based approach for a single source code base with function portability across different accelerators used in the supercomputers. However, the performance portability is not guaranteed by the OpenACC standard. Therefore, we propose a systematic optimization method, instead of...
We implement a promising algorithm for sparse-matrix sparse-vector multiplication (SpMSpV) on the GPU. An efficient k-way merge lies at the heart of finding a fast parallel SpMSpV algorithm. We examine the scalability of three approaches -- no sorting, merge sorting, and radix sorting -- in solving this problem. For breadth-first search (BFS), we achieve a 1.26x speedup over state-of-the-art sparse-matrix...
We develop a methodology for modeling the energy efficiency of tiled nested-loop codes running on a graphics processing unit (GPU) and use it for energy efficiency optimization. % We use the polyhedral model, a We assume that a highly optimized and parametrized version of a tiled nested -- loop code, either written by an expert programmer or automatically produced by a polyhedral compilation tool...
As the era of Moore's Law and increasing CPU clock rates nears its stopping point the focus of chip and hardware design has shifted to increasing the number of computation cores present on the chip. This increase can be most clearly seen in the rise of Graphic Processing Units (GPU) where hundreds or thousands of slower cores work in parallel to accomplish tasks. Programming for these chips represents...
Effective parallel programming for GPUs requires careful attention to several factors, including ensuring coalesced access of data from global memory. There is a need for tools that can provide feedback to users about statements in a GPU kernel where non-coalesced data access occurs, and assistance in fixing the problem. In this paper, we address both these needs. We develop a two-stage framework...
GPU's SIMD architecture is a double-edged sword confronting parallel tasks with control flow divergence. On the one hand, it provides a high performance yet power-efficient platform to accelerate applications via massive parallelism; however, on the other hand, irregularities induce inefficiencies due to the warp's lockstep traversal of all diverging execution paths. In this work, we present a software...
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