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Owe Philipsen, Christopher Pinke, Alessandro Sciarra, Matthias Bach
We present the Lattice QCD application CL2QCD, which is based on OpenCL and can be utilized to run on Graphic Processing Units as well as on common CPUs. We focus on implementation details as well as performance results of selected features. CL2QCD has been successfully applied in LQCD studies at finite temperature and density and […]
Mario Spera
Graphics Processing Units (GPUs) can speed up the numerical solution of various problems in astrophysics including the dynamical evolution of stellar systems; the performance gain can be more than a factor 100 compared to using a Central Processing Unit only. In this work I describe some strategies to speed up the classical N-body problem using […]
Simon Jones, Matthew Studley, Alan Winfield
It is desirable for a robot to be able to run on-board simulations of itself in a model of the world to evaluate action consequences and test new controller solutions, but simulation is computationally expensive. Modern mobile System-on-Chip devices have high performance at low power consumption levels and now incorporate powerful graphics processing units, making […]
Anders Boesen Lindbo Larsen
This technical report introduces CUDArray – a CUDA-accelerated subset of the NumPy library. The goal of CUDArray is to combine the ease of development from NumPy with the computational power of Nvidia GPUs in a lightweight and extensible framework. Since the motivation behind CUDArray is to facilitate neural network programming, CUDArray extends NumPy with a […]
Markus Steinberger, Michael Kenzel, Pedro Boechat, Bernhard Kerbl, Mark Dokter, Dieter Schmalstieg
In this paper, we present Whippletree, a novel approach to scheduling dynamic, irregular workloads on the GPU. We introduce a new programming model which offers the simplicity and expressiveness of task-based parallelism while retaining all aspects of the multilevel execution hierarchy essential to unlocking the full potential of a modern GPU. At the same time, […]
Jayanth Chennamangalam, Simon Scott, Glenn Jones, Hong Chen, John Ford, Amanda Kepley, D. R. Lorimer, Jun Nie, Richard Prestage, D. Anish Roshi, Mark Wagner, Dan Werthimer
The Graphics Processing Unit (GPU) has become an integral part of astronomical instrumentation, enabling high-performance online data reduction and accelerated online signal processing. In this paper, we describe a wide-band reconfigurable spectrometer built using an off-the-shelf GPU card. This spectrometer, when configured as a polyphase filter bank (PFB), supports a dual-polarization bandwidth of up to […]
Andrew Gearhart
As computing devices evolve with successive technology generations, many machines target either the mobile or high-performance computing/datacenter environments. In both of these form factors, energy consumption often represents the limiting factor on hardware and software efficiency. On mobile devices, limitations in battery technology may reduce possible hardware capability due to a tight energy budget. On […]
Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz, Bertrand Meyer
Using GPUs as general-purpose processors has revolutionized parallel computing by offering, for a large and growing set of algorithms, massive data-parallelization on desktop machines. As an obstacle to widespread adoption, programming GPUs has remained difficult due to the need of using low-level control of the hardware to achieve good performance. This paper suggests a programming […]
Marwan Abdellah
For embarrassingly parallel algorithms, a Graphics Processing Unit (GPU) outperforms a traditional CPU on price-per-flop and price-per-watt by at least one order of magnitude. This had led to the mapping of signal and image processing algorithms, and consequently their applications, to run entirely on GPUs. This paper presents CUFFTSHIFT, a ready-to-use GPU-accelerated library, that implements […]
Felix Weninger, Johannes Bergmann, Bjorn Schuller
In this article, we introduce CURRENNT, an open-source parallel implementation of deep recurrent neural networks (RNNs) supporting graphics processing units (GPUs) through NVIDIA’s Computed Unified Device Architecture (CUDA). CURRENNT supports uni- and bidirectional RNNs with Long Short-Term Memory (LSTM) memory cells which overcome the vanishing gradient problem. To our knowledge, CURRENNT is the first publicly […]
Bruce Merry
Sorting and scanning are two fundamental primitives for constructing highly parallel algorithms. A number of libraries now provide implementations of these primitives for GPUs, but there is relatively little information about the performance of these implementations. We benchmark seven libraries for 32-bit integer scan and sort, and sorting 32-bit values by 32-bit integer keys.We show […]
Changsheng Huang, Baochang Shi, Zhaoli Guo, Zhenhua Chai
Conducting lattice Boltzmann method on GPU has been proved to be an effective manner to gain a significant performance benefit, thus the GPU or multi-GPU based lattice Boltzmann method is considered as a promising and competent candidate in the study of large-scale complex fluid flows. In this work, a multi-GPU based lattice Boltzmann algorithm coupled […]
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