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检索条件"任意字段=7th Workshop on General Purpose Processing Using Graphics Processing Units, GPGPU 2014"

共 17 条记录，以下是11-20 订阅

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Exploring graphics processing units as parallel coprocessors for online aggregation

Exploring graphics processing units as parallel coprocessors...

引用

13th ACM International workshop on Data Warehousing and OLAP, DOLAP'10, Co-located with 19th International Conference on Information and Knowledge Management, CIKM'10

作者： Lauer, Tobias Datta, Amitava Khadikov, Zurab Anselm, Christoffer University of Freiburg Georges-Köhler-Allee 051 79110 Freiburg Germany University of Western Australia 35 Stirling Highway Perth WA 6009 Australia Jedox AG Bismarckallee 7a 79098 Freiburg Germany

ISBN: (纸本)9781450303835

Multidimensional aggregation is one of the most important computational building blocks and hence also a potential performance bottleneck in Online Analytic processing (OLAP). In order to deliver fast query responses for interactive operations such as slicing, dicing, roll-up and drill-down, it is essential that aggregates along the relevant dimensions of a data cube can be calculated as efficiently as possible. general-purpose computing on graphics processing units (gpgpu) is a recent trend used in many computing domains with the potential for tremendous speedups through the massively data-parallel computation available on such devices. We present a GPU-based cube data structure and algorithms for fast multidimensional aggregation, implemented using Nvidia's CUDA framework. Our experimental tests show a substantial speedup over state-of-the-art sequential algorithms. Moreover, the performance gain is particularly high in cases exposing the weaknesses of traditional algorithms, i.e. when the number of base cells involved in an aggregation is large. © 2010 ACM.

关键词： graphics processing unit

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Message from the workshop organizers

ACM International Conference Proceeding Series

引用

ACM International Conference Proceeding Series 2014年

作者： Cavazos, John Kaeli, Dave

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Evaluation of modern gpgpu technologies for image processing 20

Evaluation of modern GPGPU technologies for image processing

引用

8th International workshop on OpenCL, IWOCL 2020

作者： Meyer, Joachim Stemmer Imaging Puchheim Germany

ISBN: (纸本)9781450375313

Image processing has high computational requirements. As many papers (including [Frisk 2010;N. Zhang and Wang 2010;Yang et al. 2008]) have indicated, a number of image processing operations can be optimized by heavy parallelization of the computation. Currently, one of the best options for parallelized image processing is using general purpose computation on graphics processing units (gpgpu). In the area of gpgpu, a rather wide range of APIs is available and finding an appropriate choice for a project is challenging. In the course of being able to provide guidance on selecting the right API in an image processing context, four GPU programming models were compared according to their platform independence, usability, and performance. the four investigated APIs are CUDA, OpenCL 1.2, Vulkan, and SYCL. To gain information on the metrics usability and performance a test project was created, implementing a number of image processing tasks using the four GPU programming models. the test project was designed as a library, where the 4 investigated APIs could be replaced at compile-time and new operations could be easily added. the implemented reference algorithms form a pipeline for processing images from polarization cameras. the targeted platforms were mid- to high-end desktop PCs as well as embedded single board platforms ranging from Odroid to Nvidia Jetson. On all devices, either MSVC 19.x or GCC 7+ were used as host compilers. CUDA tests were compiled with NVCC and SYCL was represented by ComputeCpp and hipSYCL depending on the device. the direct comparison of the APIs, done for this work, cumulates into a decision matrix to aid organizations in their process of selecting an offloading API. It shows, for example, that CUDA and SYCL are comparable in terms of development cost, support of modern C++ and ease of integration in existing code bases. When considering modern standard adoption and GPU portability Vulkan has advantages, while OpenCL and SYCL can run on CPUs and

关键词： graphics processing unit

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Accelerating correlation power analysis using graphics processing units (GPUs) 7

Accelerating correlation power analysis using graphics proce...

引用

2014 7th International Conference on Information and Automation for Sustainability, ICIAfS 2014

作者： Gamaarachchi, Hasindu Ragel, Roshan Jayasinghe, Darshana Department of Computer Engineering University of Peradeniya Peradeniya Sri Lanka School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9781479945986

Correlation Power Analysis (CPA) is a type of power analysis based side channel attack that can be used to derive the secret key of encryption algorithms including DES (Data Encryption Standard) and AES (Advanced Encryption Standard). A typical CPA attack on unprotected AES is performed by analysing a few thousand power traces that requires about an hour of computational time on a general purpose CPU. Due to the severity of this situation, a large number of researchers work on countermeasures to such attacks. Verifying that a proposed countermeasure works well requires performing the CPA attack on about 1.5 million power traces. Such processing, even for a single attempt of verification on commodity hardware would run for several days making the verification process infeasible. Modern graphics processing units (GPUs) have support for thousands of light weight threads, making them ideal for parallelizable algorithms like CPA. While the cost of a GPU being lesser than a high performance multicore server, still the GPU performance for this algorithm is many folds better than that of a multicore server. We present an algorithm and its implementation on GPU for CPA on 128-bit AES that is capable of executing 1300x faster than that on a single threaded CPU and more than 60x faster than that on a 32 threaded multicore server. We show that an attack that would take hours on the multicore server would take even less than a minute on a much cost effective GPU. © 2014 IEEE.

关键词： graphics processing unit

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Comparison based sorting for systems with multiple GPUs 6

Comparison based sorting for systems with multiple GPUs

引用

6th workshop on general purpose Processor using graphics processing units, gpgpu 2013

作者： Tanasic, Ivan Vilanova, Lluís Jordà, Marc Cabezas, Javier Gelado, Isaac Navarro, Nacho Hwu, Wen-Mei Barcelona Supercomputing Center Spain Universitat Politecnica de Catalunya Spain University of Illinois United States

ISBN: (纸本)9781450320177

As a basic building block of many applications, sorting algorithms that efficiently run on modern machines are key for the performance of these applications. With the recent shift to using GPUs for general purpose compuing, researches have proposed several sorting algorithms for single-GPU systems. However, some workstations and HPC systems have multiple GPUs, and applications running on them are de- signed to use all available GPUs in the system. In this paper we present a high performance multi-GPU merge sort algorithm that solves the problem of sorting data distributed across several GPUs. Our merge sort algorithm first sorts the data on each GPU using an existing single- GPU sorting algorithm. then, a series of merge steps pro- duce a globally sorted array distributed across all the GPUs in the system. this merge phase is enabled by a novel pivot selection algorithm that ensures that merge steps always distribute data evenly among all GPUs. We also present the implementation of our sorting algorithm in CUDA, as well as a novel inter-GPU communication technique that enables this pivot selection algorithm. Experimental results show that an efficient implementation of our algorithm achieves a speed up of 1.9x when running on two GPUs and 3.3x when running on four GPUs, compared to sorting on a single GPU. At the same time, it is able to sort two and four times more records, compared to sorting on one GPU. Copyright 2013 ACM.

关键词： graphics processing unit

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Supporting dynamic GPU computing result reuse in the cloud 7

Supporting dynamic GPU computing result reuse in the cloud

引用

7th USENIX workshop on Hot Topics in Cloud Computing, HotCloud 2015

作者： Zhou, Husheng Fu, Yangchun Liu, Cong Department of Computer Science University of Texas at Dallas United States

graphics processing units (GPUs) have been adopted by major cloud vendors, as GPUs provide orders-of-magnitude speedup for computation-intensive data-parallel applications. In the cloud, efficiently sharing GPU resources among multiple virtual machines (VMs) is not so straightforward. Recent research has been conducted to develop GPU virtualization technologies, making it feasible for VMs to share GPU resources in a reliable manner. this paper seeks to improve the efficiency of sharing GPU resources in the cloud for accelerating general-purpose workloads. Our key observation is that redundant GPU computation requests are being seen in many GPU-accelerated workloads in the cloud, such as cloud gaming where multiple clients playing the same game call GPUs to perform physics simulation. We have measured this redundancy using a gaming case study, and found that more than 24% (47%) of the GPU computation requests called by the same VM (multiple VMs) are identical. To exploit this redundancy, we present GRU (GPU Result re-Use), a GPU sharing, result memoization and reuse ecosystem in a cloud environment. GRU transparently enables VMs in the cloud to share a single GPU efficiently, and memoizes GPU computation results for reuse. It leverages the GPU full-virtualization technology, which enables GPU result memoization and reuse without modification of existing device drivers and operating systems. We have implemented GRU on top of the Xen hypervisor. Preliminary experiments show that GRU is able to achieve a significant speedup of up to 18 times compared to the state-of-the-art GPU virtualization framework, while adding a rather small amount of runtime overheads. © 2015 USENIX Association. All rights reserved.

关键词： graphics processing unit

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Accelerating correlation power analysis using graphics processing units (GPUs)

Accelerating correlation power analysis using graphics proce...

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International Conference on Information and Automation for Sustainability (ICIAfS)

作者： Hasindu Gamaarachchi Roshan Ragel Darshana Jayasinghe Department of Computer Engineering University of Peradeniya Peradeniya Sri Lanka School of Computer Science and Engineering University of New South Wales Sydney Australia

关键词： Handheld computers graphics processing units Decision support systems Computer architecture Parallel processing Abstracts Correlation

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