Moritz Kreutzer, Jonas Thies, Melven Rohrig-Zollner, Andreas Pieper, Faisal Shahzad, Martin Galgon, Achim Basermann, Holger Fehske, Georg Hager, Gerhard Wellein
While many of the architectural details of future exascale-class high performance computer systems are still a matter of intense research, there appears to be a general consensus that they will be strongly heterogeneous, featuring "standard" as well as "accelerated" resources. Today, such resources are available as multicore processors, graphics processing units (GPUs), and other accelerators […]
Clemens-Alexander Brust, Sven Sickert, Marcel Simon, Erik Rodner, Joachim Denzler
In this paper, we present convolutional patch networks, which are convolutional (neural) networks (CNN) learned to distinguish different image patches and which can be used for pixel-wise labeling. We show how to easily learn spatial priors for certain categories jointly with their appearance. Experiments for urban scene understanding demonstrate state-of-the-art results on the LabelMeFacade dataset. […]
Paul Harvey, Kristian Hentschel, Joseph Sventek
GPU and multicore hardware architectures are commonly used in many different application areas to accelerate problem solutions relative to single CPU architectures. The typical approach to accessing these hardware architectures requires embedding logic into the programming language used to construct the application; the two primary forms of embedding are: calls to API routines to access […]
Mark James Abraham, Teemu Murtola, Roland Schulz, Szilard Pall, Jeremy C. Smith, Berk Hess, Erik Lindahl
GROMACS is one of the most widely used open-source and free software codes in chemistry, used primarily for dynamical simulations of biomolecules. It provides a rich set of calculation types, preparation and analysis tools. Several advanced techniques for free-energy calculations are supported. In version 5, it reaches new performance heights, through several new and enhanced […]
S. Ali Mirsoleimani, Aske Plaat, Jaap van den Herik, Jos Vermaseren
Many algorithms have been parallelized successfully on the Intel Xeon Phi coprocessor, especially those with regular, balanced, and predictable data access patterns and instruction flows. Irregular and unbalanced algorithms are harder to parallelize efficiently. They are, for instance, present in artificial intelligence search algorithms such as Monte Carlo Tree Search (MCTS). In this paper we […]
Henk Mulder
With the emergence of general purpose GPU (GPGPU) programming, concurrent data processing of large arrays of data has gained a significant boost in performance. However, due to the memory architecture between the host and GPU device and other limitations in the instructions available on GPUs, the implementation of dynamic data structures, like linked list and […]
Thijs Wiefferink
The Prefix Sum is an algorithm used as a building block for various other algorithms, for example radix sort, quicksort and lexically comparing strings. Implementing the Prefix Sum algorithm on the CPU is trivial, but a parallel approach with OpenCL is more complicated. An implementation in OpenCL has been made, and optimized to minimize branch […]
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao
Deep convolutional networks have achieved great success for object recognition in still images. However, for action recognition in videos, the improvement of deep convolutional networks is not so evident. We argue that there are two reasons that could probably explain this result. First the current network architectures (e.g. Two-stream ConvNets) are relatively shallow compared with […]
Calvin Montgomery, Jeffrey L. Overbey, Xuechao Li
OpenACC provides a high-productivity API for programming GPUs and similar accelerator devices. One of the last steps in tuning OpenACC programs is selecting values for the num_gangs and vector length clauses, which control how a parallel workload is distributed to an accelerator’s processing units. In this paper, we present OptACC, an autotuner that can assist […]
Anton Akusok, Kaj-Mikael Bjork, Yoan Miche, Amaury Lendasse
This work presents a complete approach to a successful utilization of a high performance Extreme Learning Machines (ELMs) Toolbox for Big Data. It summarizes recent advantages in algorithmic performance; gives a fresh view on the ELM solution in relation to the traditional linear algebraic performance; and reaps the latest software and hardware performance achievements. The […]
Carsten Kutzner, Szilard Pall, Martin Fechner, Ansgar Esztermann, Bert L. de Groot, Helmut Grubmuller
The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well exploited with a combination of SIMD, multi-threading, and MPI-based SPMD/MPMD parallelism, while GPUs can be used as accelerators to compute interactions offloaded from the CPU. Here we evaluate which […]
Kai Zhang, Kaibo Wang, Yuan Yuan, Lei Guo, Rubao Lee, Xiaodong Zhang
In-memory key-value stores play a critical role in data processing to provide high throughput and low latency data accesses. In-memory key-value stores have several unique properties that include (1) data intensive operations demanding high memory bandwidth for fast data accesses, (2) high data parallelism and simple computing operations demanding many slim parallel computing units, and […]
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