13791
Andreas Klockner
A large amount of numerically-oriented code is written and is being written in legacy languages. Much of this code could, in principle, make good use of data-parallel throughput-oriented computer architectures. Loo.py, a transformation-based programming system targeted at GPUs and general data-parallel architectures, provides a mechanism for user-controlled transformation of array programs. This transformation capability is […]
Vadim Demchik
General principles of pseudorandom numbers production for Monte Carlo simulations on GPUs are discussed by creating an OpenCL open-source library of pseudorandom number generators PRNGCL. The library contains implementation of a number of the most popular uniform generators. The most popular pseudorandom number generators for Monte Carlo simulations and libraries for GPUs are reviewed. Some […]
Ramiro Marco Figuera
In this Master Thesis an analysis of illumination conditions at the lunar south pole using parallel computing techniques is presented. Due to the small inclination (1.54o) of the lunar rotational axis with respect to the ecliptic plane and the topography of the lunar south pole, which allows long illumination periods, the study of illumination conditions […]
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Kamran Karimi
OpenCL, along with CUDA, is one of the main tools used to program GPGPUs. However, it allows running the same code on multi-core CPUs too, making it a rival for the long-established OpenMP. In this paper we compare OpenCL and OpenMP when developing and running compute-heavy code on a CPU. Both ease of programming and […]
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Johannes Koster
The analysis of next-generation sequencing (NGS) data is a major topic in bioinformatics: short reads obtained from DNA, the molecule encoding the genome of living organisms, are processed to provide insight into biological or medical questions. This thesis provides novel solutions to major topics within the analysis of NGS data, focusing on parallelization, scalability and […]
Vasvi Kakkad
Advances in technology have given rise to applications that are deployed on wireless sensor networks (WSNs), the cloud, and the Internet of things. There are many emerging applications, some of which include sensor-based monitoring, web traffic processing, and network monitoring. These applications collect large amount of data as an unbounded sequence of events and process […]
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Peter Klages, Kevin Bandura, Nolan Denman, Andre Recnik, Jonathan Sievers, Keith Vanderlinde
Interferometric radio telescopes often rely on computationally expensive O(N^2) correlation calculations; fortunately these computations map well to massively parallel accelerators such as low-cost GPUs. This paper describes the OpenCL kernels developed for the GPU based X-engine of a new hybrid FX correlator. Channelized data from the F-engine is supplied to the GPUs as 4-bit, offset-encoded […]
Luna Backes
Computer vision (CV) is widely expected to be the next "Big Thing" in mobile computing. For example, Google has recently announced their project "Tango", a 5-inch Android phone containing highly customized hardware and software designed to track the full 3-dimensional motion of the device as you hold it while simultaneously creating a map of the […]
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Weifeng Liu, Brian Vinter
Sparse matrix-vector multiplication (SpMV) is a fundamental building block for numerous applications. In this paper, we propose CSR5 (Compressed Sparse Row 5), a new storage format, which offers high-throughput SpMV on various platforms including CPUs, GPUs and Xeon Phi. First, the CSR5 format is insensitive to the sparsity structure of the input matrix. Thus the […]
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 […]
Lukas Polok, Viorela Ila, Pavel Smrz
Sparse matrix multiplication is an important algorithm in a wide variety of problems, including graph algorithms, simulations and linear solving to name a few. Yet, there are but a few works related to acceleration of sparse matrix multiplication on a GPU. We present a fast, novel algorithm for sparse matrix multiplication, outperforming the previous algorithm […]
Tomas Karnagel, Dirk Habich, Wolfgang Lehner
In several parts of query optimization, like join enumeration or physical operator selection, there is always the question of how much optimization is needed and how large the performance benefits are. In particular, a decision for either global optimization (e.g., during query optimization) or local optimization (during query execution) has to be taken. In this […]
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