Kazuaki Ishizaki, Akihiro Hayashi, Gita Koblents, Vivek Sarkar
GPUs can enable significant performance improvements for certain classes of data parallel applications and are widely used in recent computer systems. However, GPU execution currently requires explicit low-level operations such as 1) managing memory allocations and transfers between the host system and the GPU, 2) writing GPU kernels in a low-level programming model such as […]
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Akihiro Hayashi, Kazuaki Ishizaki, Vivek Sarkar, Gita Koblents
High-level languages such as Java increase both productivity and portability with productive language features such as managed runtime, type safety, and precise exception semantics. Additionally, Java 8 provides parallel stream APIs with lambda expressions to facilitate parallel programming for mainstream users of multi-core CPUs and many-core GPUs. These high-level APIs avoid the complexity of writing […]
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James Lemon, Sinan Kockara, Tansel Halic, Mutlu Mete
BACKGROUND: Dermoscopy is a highly effective and noninvasive imaging technique used in diagnosis of melanoma and other pigmented skin lesions. Many aspects of the lesion under consideration are defined in relation to the lesion border. This makes border detection one of the most important steps in dermoscopic image analysis. In current practice, dermatologists often delineate […]
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James Clarkson, Christos Kotselidis, Gavin Brown, Mikel Lujan
Heterogeneous programming has started becoming the norm in order to achieve better performance by running portions of code on the most appropriate hardware resource. Currently, significant engineering efforts are undertaken in order to enable existing programming languages to perform heterogeneous execution mainly on GPUs. In this paper we describe Jacc, an experimental framework which allows […]
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Juan Jose Fumero, Toomas Remmelg, Michel Steuwer, Christophe Dubach
GPUs (Graphics Processing Unit) and other accelerators are nowadays commonly found in desktop machines, mobile devices and even data centres. While these highly parallel processors offer high raw performance, they also dramatically increase program complexity, requiring extra effort from programmers. This results in difficult-to-maintain and non-portable code due to the low-level nature of the languages […]
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Spencer Davis, Brandon Jones, Hai Jiang
The recent rise in the popularity of mobile computing has brought the attention of mobile security to the forefront. As users depend more on tablets and smartphones, sensitive data is left to be secured using devices with vastly weaker resources than a typical computer. As mobile technology matures, the industry is starting to provide devices […]
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Artur Malinowski
Parallel algorithms are popular method of increasing system performance. Apart from showing their properties using asymptotic analysis, proof-of-concept implementation and practical experiments are often required. In order to speed up the development and provide simple and easily accessible testing environment that enables execution of reliable experiments, the paper proposes a platform with multi-core computational accelerator: […]
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Oren Segal, Philip Colangelo, Nasibeh Nasiri, Zhuo Qian, Martin Margala
We introduce SparkCL, an open source unified programming framework based on Java, OpenCL and the Apache Spark framework. The motivation behind this work is to bring unconventional compute cores such as FPGAs/GPUs/APUs/DSPs and future core types into mainstream programming use. The framework allows equal treatment of different computing devices under the Spark framework and introduces […]
Ken Miura, Tatsuya Harada
Deep learning can achieve outstanding results in various fields. However, it requires so significant computational power that graphics processing units (GPUs) and/or numerous computers are often required for the practical application. We have developed a new distributed calculation framework called "Sashimi" that allows any computer to be used as a distribution node only by accessing […]
Ken Miura, Tetsuaki Mano, Atsushi Kanehira, Yuichiro Tsuchiya, Tatsuya Harada
MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript. Especially, our matrix multiplication is 177 times faster than the fastest JavaScript benchmark. […]
Xiangyu Li
MapReduce is a programming model capable of processing massive data in parallel across hundreds of computing nodes in a cluster. It hides many of the complicated details of parallel computing and provides a straightforward interface for programmers to adapt their algorithms to improve productivity. Many MapReduce-based applications have utilized the power of this model, including […]
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Jelena Tekic, Predrag Tekic, Milos Rackovic
This paper presents performance comparison, of the lid-driven cavity flow simulation, with Lattice Boltzmann method, example, between CUDA and OpenCL parallel programming frameworks. CUDA is parallel programming model developed by NVIDIA for leveraging computing capabilities of their products. OpenCL is an open, royalty free, standard developed by Khronos group for parallel programming of heterogeneous devices […]
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Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

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Node 1
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  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
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  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

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