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Sreeram Potluri
Accelerators (such as NVIDIA GPUs) and coprocessors (such as Intel MIC/Xeon Phi) are fueling the growth of next-generation ultra-scale systems that have high compute density and high performance per watt. However, these many-core architectures cause systems to be heterogeneous by introducing multiple levels of parallelism and varying computation/communication costs at each level. Application developers also […]
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Cedric Nugteren, Gert-Jan van den Braak, Henk Corporaal
Programming models such as CUDA and OpenCL allow the programmer to specify the independence of threads, effectively removing ordering constraints. Still, parallel architectures such as the graphics processing unit (GPU) do not exploit the potential of data-locality enabled by this independence. Therefore, programmers are required to manually perform data-locality optimisations such as memory coalescing or […]
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Aruna Dore, Sunitha Lasrado
GPU (Graphic processing system) enhance the performance of the performance of the computing field due to its hundreds of cores in parallel. CUDA (Compute Unified Device Architecture) and OpenCL (Open Computing Language) programming models are included in GPU. The advantage of these two programming models in GPU is that developers don’t have to understand any […]
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Ursula Reiterer
Clustering is a basic task in exploratory data analysis. It is used to partition elements of a set into disjoint groups, so-called clusters, such that elements within a group are similar to each other, but dissimilar to elements of other groups. Several clustering algorithms exist, which can be applied depending on the type of dataset […]
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Sebastian Mayr
Ray tracing denotes a class of rendering algorithms that are well-known for their flexibility and their capability of generating highly realistic images of three dimensional models. However, due to the heavy computational requirements, it has traditionally been used for offline rendering. Improving the performance of ray tracing has been an active area of research and […]
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Andreas Hormandinger
This thesis focuses on the use of automatic code generation to combine different classes of optimizations to find the best optimization for parallel reduction in OpenCL on various devices. It also introduces the optimizations used. In the end the results of the combinations will be evaluated and discussed.
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Matthaus Wander, Lorenz Schwittmann, Christopher Boelmann, Torben Weis
When a client queries for a non-existent name in the Domain Name System (DNS), the server responds with a negative answer. With the DNS Security Extensions (DNSSEC), the server can either use NSEC or NSEC3 for authenticated negative answers. NSEC3 claims to protect DNSSEC servers against domain enumeration, but incurs significant CPU and bandwidth overhead. […]
Yuan Wen, Zheng Wang, Michael F.P. O'Boyle
Heterogeneous systems consisting of multiple CPUs and GPUs are increasingly attractive as platforms for high performance computing. Such platforms are usually programmed using OpenCL which provides program portability by allowing the same program to execute on different types of device. As such systems become more mainstream, they will move from application dedicated devices to platforms […]
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Mathias Bourgoin, Emmanuel Chailloux
We present WebSpoc, an OCaml GPGPU library targeting web applications that is built upon SPOC and js_of_ocaml. SPOC is an OCaml GPGPU library focusing on abstracting memory transfers, handling GPGPU computations and offering easy portability. Js_of_ocaml is the OCaml byte-code to JavaScript compiler. Thus, WebSpoc provides high performance computations from the web browser while benefiting […]
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Robin Kumar, Amandeep Kaur Cheema
Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. Neural network is the well-known branch of machine learning & it has been used extensively by researchers for prediction of data and the prediction accuracy depends upon fine tuning of particular financial data. In this paper […]
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Alastair F. Donaldson
I present a tutorial overview demonstrating the key technique used by GPUVerify, a static verification tool for graphics processing unit (GPU) kernels. The technique is a method for translating a massively parallel GPU kernel into a sequential program such that correctness of the sequential program implies data race-freedom of the parallel kernel.
Michael Gowanlock, Henri Casanova
The processing of moving object trajectories arises in many application domains. We focus on a trajectory similarity search, the distance threshold search, which finds all trajectories within a given distance of a query trajectory over a time interval. A multithreaded CPU implementation that makes use of an in-memory R-tree index can achieve high parallel efficiency. […]
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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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