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Jun Xiao, Hao Chen, Jianhua Sun
Sorting is a fundamental problem in computer science, and the strict sorting usually means a strict order with ascending or descending. However, some applications in reality don’t require the strict ascending or descending order and the approximate ascending or descending order just meets the requirement. Graphics processing units (GPUs) have become accelerators for parallel computing. […]
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Alexander Ross Mace
Acentrosomal microtubules are not bound to a microtubule organising centre yet are still able to form ordered arrays. Two clear examples of this behaviour are the acentrosomal apico-basal (side wall) array in epithelial cells and the parallel organisation of plant cortical microtubules. This research investigates their formation through mathematical modelling and Monte Carlo simulations with […]
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Mark Sutherland, Joshua San Miguel, Natalie Enright Jerger
We present texture cache approximation as a method for using existing hardware on GPUs to eliminate costly global memory accesses. We develop a technique for using a GPU’s texture fetch units to generate approximate values, and argue that this technique is applicable to a wide variety of GPU kernels. Applying texture cache approximation to an […]
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Andre Viebke, Sabri Pllana
Supervised learning of Convolutional Neural Networks (CNNs), also known as supervised Deep Learning, is a computationally demanding process. To find the most suitable parameters of a network for a given application, numerous training sessions are required. Therefore, reducing the training time per session is essential to fully utilize CNNs in practice. While numerous research groups […]
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Andrea Miele
We present a preliminary study of buffer overflow vulnerabilities in CUDA software running on GPUs. We show how an attacker can overrun a buffer to corrupt sensitive data or steer the execution flow by overwriting function pointers, e.g., manipulating the virtual table of a C++ object. In view of a potential mass market diffusion of […]
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Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang
Data embedding is used in many machine learning applications to create low-dimensional feature representations, which preserves the structure of data points in their original space. In this paper, we examine the scenario of a heterogeneous network with nodes and content of various types. Such networks are notoriously difficult to mine because of the bewildering combination […]
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Suejb Memeti, Sabri Pllana
Genetic information is increasing exponentially, doubling every 18 months. Analyzing this information within a reasonable amount of time requires parallel computing resources. While considerable research has addressed DNA analysis using GPUs, so far not much attention has been paid to the Intel Xeon Phi coprocessor. In this paper we present an algorithm for large-scale DNA […]
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Amir Gholami, Judith Hill, Dhairya Malhotra, George Biros
We present a new library for parallel distributed Fast Fourier Transforms (FFT). Despite the large amount of work on FFTs, we show that significant speedups can be achieved for distributed transforms. The importance of FFT in science and engineering and the advances in high performance computing necessitate further improvements. AccFFT extends existing FFT libraries for […]
Imran Ashraf, Vlad-Mihai Sima, Koen Bertels
The growing demand of processing power is being satisfied mainly by an increase in the number of computing cores in a system. One of the main challenges to be addressed is efficient utilization of these architectures. This demands data-communication aware mapping of applications on these architectures. Appropriate tools are required to provide the detailed intra-application […]
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Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, Juergen Schmidhuber
Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D videos to segment them. They have a fixed input size and typically perceive only small local contexts of the pixels to be classified as foreground or background. In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive the entire spatio-temporal context of each pixel in […]
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Oluwapelumi Adenikinju, Julian Gilyard, Joshua Massey, Thomas Stitt
We investigate the parallel solutions to linear systems with the application focus as the global illumination problem in computer graphics. An existing CPU serial implementation using the radiosity method is given as the performance baseline where a scene and corresponding form-factor coefficients are provided. The initial computational radiosity solver uses the basic Jacobi method with […]
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Andra-Ecaterina Hugo
To face the ever demanding requirements in term of accuracy and speed of scientific simulations, the High Performance community is constantly increasing the demands in term of parallelism, adding thus tremendous value to parallel libraries strongly optimized for highly complex architectures.Enabling HPC applications to perform efficiently when invoking multiple parallel libraries simultaneously is a great […]
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