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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1-10 订阅

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Proceedings of the 7th workshop on general purpose processing using graphics processing units, gpgpu 2014

Proceedings of the 7th Workshop on General Purpose Processin...

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7th workshop on general purpose processing using graphics processing units, gpgpu 2014

ISBN: (纸本)9781450327664

the proceedings contain 12 papers. the topics discussed include: application-aware memory system for fair and efficient execution of concurrent gpgpu applications;efficient instrumentation of gpgpu applications using information flow analysis and symbolic execution;measuring GPU power with the K20 built-in sensor;performance evaluation and optimization mechanisms for interoperable graphics and computation on GPUs;GLZSS: LZSS lossless data compression can be faster;ad-heap: an efficient heap data structure for asymmetric multicore processors;a CPU-GPU hybrid implementation and model-driven scheduling of the fast multipole method;ParallelJS: an execution framework of JavaScript on heterogeneous systems;APR: a novel parallel repacking algorithm for efficient gpgpu parallel code transformation;and exploiting GPU hardware saturation for fast compiler optimization.

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Proceedings of the 15th workshop on general purpose processing using GPU, gpgpu 2023

Proceedings of the 15th Workshop on General Purpose Processi...

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15th Annual workshop on general purpose processing using graphics processing Unit, gpgpu 2023

ISBN: (纸本)9798400707766

the proceedings contain 7 papers. the topics discussed include: GPU auto-tuning framework for optimal performance and power consumption;LATOA: load-aware task offloading and adoption in GPU;understanding portability of automotive workload: a case study with the points-to-image kernel in SYCL on heterogeneous computing platforms;simple out of order core for gpgpus;lightweight register file caching in collector units for GPUs;exploiting scratchpad memory for deep temporal blocking: a case study for 2D Jacobian 5-point iterative stencil kernel (j2d5pt);and understanding scalability of multi-GPU systems.

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Evaluation of modern gpgpu technologies for image processing 20

Evaluation of modern GPGPU technologies for image processing

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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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Calculation of ground states of few-body nuclei using NVIDIA CUDA technology 7

Calculation of ground states of few-body nuclei using NVIDIA...

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7th International Conference Distributed Computing and Gridtechnologies in Science and Education, GRID 2016

作者： Naumenko, M.A. Samarin, V.V. Flerov Laboratory of Nuclear Reactions Joint Institute for Nuclear Research 6 Joliot-Curie st. Moscow Reg. Dubna141980 Russia Dubna State University 19 Universitetskaya st. Moscow Reg. Dubna141982 Russia

the possibility of application of modern parallel computing solutions to speed up the calculations of ground states of few-body nuclei by Feynman's continual integrals method has been investigated. these calculations may require large computational time, particularly in the case of systems with many degrees of freedom. the results of application of general-purpose computing on graphics processing units (gpgpu) using NVIDIA CUDA technology are presented. the algorithm allowing us to perform calculations directly on GPU was developed and implemented in C++ programming language. Calculations were performed on the NVIDIA Tesla K40 accelerator installed within the heterogeneous cluster of the Laboratory of Information Technologies, Joint Institute for Nuclear Research, Dubna. the energy and the square modulus of the wave function of the ground states of several fewbody nuclei have been calculated. the results show that the use of gpgpu significantly increases the speed of calculations.

关键词： Ground state

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Supporting dynamic GPU computing result reuse in the cloud 7

Supporting dynamic GPU computing result reuse in the cloud

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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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Efficient instrumentation of gpgpu applications using information flow analysis and symbolic execution

Efficient instrumentation of GPGPU applications using inform...

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7th workshop on general purpose processing using graphics processing units, gpgpu 2014

作者： Farooqui, Naila Schwan, Karsten Yalamanchili, Sudhakar School of Electrical and Computer Engineering Georgia Institute of Technology College of Computing Atlanta GA United States

ISBN: (纸本)9781450327664

Dynamic instrumentation of gpgpu binaries makes possible real-time introspection methods for performance debugging, correctness checks, workload characterization, and runtime optimization. Such instrumentation involves inserting code at the instruction level of an application, while the application is running, thereby able to accurately profile data-dependent application behavior. Runtime overheads seen from instrumentation, however, can obviate its utility. this paper shows how a combination of information flow analysis and symbolic execution can be used to alleviate these overheads. the methods and their effectiveness are demonstrated for a variety of gpgpu codes written in OpenCL that run on AMD GPU target backends. Kernels that can be analyzed entirely via symbolic execution need not be instrumented, thus eliminating kernel runtime overheads altogether. For the remaining GPU kernels, our results show 5-38% improvements in kernel runtime overheads. Copyright 2014 ACM.

关键词： Program debugging

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Measuring GPU power with the K20 built-in sensor

Measuring GPU power with the K20 built-in sensor

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7th workshop on general purpose processing using graphics processing units, gpgpu 2014

作者： Burtscher, Martin Zecena, Ivan Zong, Ziliang Department of Computer Science Texas State University United States

ISBN: (纸本)9781450327664

GPU-accelerated programs are becoming increasingly common in HPC, personal computers, and even handheld devices, making it important to optimize their energy efficiency. However, accurately profiling the power consumption of GPU code is not straightfor-ward. In fact, we have identified multiple anomalies when using the on-board power sensor of K20 GPUs. For example, we have found that doubling a kernel's runtime more than doubles its energy us-age, that kernels consume energy after they have stopped executing, and that running two kernels in close temporal proximity inflates the energy consumption of the later kernel. Moreover, we have observed that the power sampling frequency varies greatly and that the GPU sensor only performs power readings once in a while. We present a methodology to accurately compute the instant power and the energy consumption despite these issues. Copyright 2014 ACM.

关键词： graphics processing unit

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Exploiting GPU hardware saturation for fast compiler optimization

Exploiting GPU hardware saturation for fast compiler optimiz...

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7th workshop on general purpose processing using graphics processing units, gpgpu 2014

作者： Magni, Alberto Dubach, Christophe O'Boyle, Michael School of Informatics University of Edinburgh United Kingdom

ISBN: (纸本)9781450327664

graphics processing units (GPUs) are efficient devices capable of delivering high performance for general purpose computation. Realizing their full performance potential often requires extensive compiler tuning. this process is particularly expensive since it has to be repeated for each target program and platform. In this paper we study the utilization of GPU hardware re- sources across multiple input sizes and compiler options. In this context we introduce the notion of hardware saturation. Saturation is reached when an application is executed with a number of threads large enough to fully utilize the available hardware resources. We give experimental evidence of hard- ware saturation and describe its properties using 16 OpenCL kernels on 3 GPUs from Nvidia and AMD. We show that in- put sizes that saturates the GPU show performance stability across compiler transformations. using the thread-coarsening transformation as an example, we show that compiler settings maintain their relative performance across input sizes within the saturation region. Leveraging these hardware and software properties we pro- pose a technique to identify the input size at the lower bound of the saturation zone, we call it Minimum Saturation Point (MSP). By performing iterative compilation on the MSP input size we obtain results effectively applicable for much large input problems reducing the overhead of tuning by an order of magnitude on average. Copyright 2014 ACM.

关键词： graphics processing unit

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GLZSS: LZSS lossless data compression can be faster

GLZSS: LZSS lossless data compression can be faster

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7th workshop on general purpose processing using graphics processing units, gpgpu 2014

作者： Zu, Yuan Hua, Bei School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China Suzhou Institute for Advanced Study University of Science and Technology of China Suzhou Jiangsu 215123 China

ISBN: (纸本)9781450327664

the need for data compression has grown for better utilization of network bandwidth and data storage space. LZ77 is the most widely used data compression method, which has many variants in practical applications. the biggest obstacle that prevents data compression from being used in high-speed applications is its high computation overhead. In this paper, we focus on parallelizing LZSS that is a derivative of LZ77 on GPU using the NVIDIA CUDA framework to improve the compression speed. Based on in-depth understanding of LZSS's dictionary-based compression mechanism and GPU's architectural features, we propose an effective method to parallelize LZSS compression algorithm on GPU. the biggest merit of this method is that it eliminates threads serialization by carefully redesign the algorithm process. Experiments on an NVIDIA GTX 590 machine with 13 benchmark files from real world demonstrate the effectiveness of our method, which achieves 2x speedup over existing work. Copyright 2014 ACM.

关键词： graphics processing unit

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GPU port of a parallel incompressible Navier-Stokes solver based on OpenACC and MVAPICH2 7

GPU port of a parallel incompressible Navier-Stokes solver b...

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AIAA AVIATION 2014 -7th AIAA theoretical Fluid Mechanics Conference 2014

作者： Luo, Lixiang Edwards, Jack R. Luo, Hong Mueller, Frank Department of Mechanical and Aerospace Engineering North Carolina State University United States Department of Computer Science North Carolina State University United States

ISBN: (数字)9781624102936

ISBN: (纸本)9781624102936

OpenACC is a directive-based programing standard aim to provide a highly portable programming model for massively-parallel accelerators, such as general-purpose Computing on graphics processing units (gpgpu), Accelerated processing Unit (APU) and Many Integrated Core Architecture (MIC). the heterogeneous nature of these accelerators stresses a demand for careful planning of data movement and novel approaches of parallel algorithms not commonly involved in scientific computation. By following a similar concept of OpenMP, the directive-based approach of OpenACC hides many underlying implementation details, thus significantly reduces the programming complexity and increases code portability. However, many challenges remain, due to the relatively narrow interconnection bandwidth among GPUs and the very fine granularity of gpgpu architecture. the first is particularly restrictive when cross-node data exchange is involved in a cluster environment. Furthermore, gpgpu's fine-grained parallelism is in conflict with certain types of inherently serial algorithms, posing further restrictions on performance. In our study, an implicit multi-block incompressible Navier-Stokes solver is ported for gpgpu using OpenACC and MVAPICH2. A performance analysis is carried out based on the profiling of this solver running in a InfiniBand cluster with nVidia GPUs, which helps to identify the potentials of directive-based GPU programming and directions for further improvement.

关键词： graphics processing unit

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