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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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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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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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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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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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Forward problem solution acceleration with graphical processing units (GPU) for electrical tomography imaging 7

Forward problem solution acceleration with graphical process...

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7th World Congress in Industrial Process Tomography, WCIPT7

作者： Matusiak, Bartosz Romanowski, Andrzej Sankowski, Dominik Grudzień, Krzysztof Institute of Applied Computer Science Lodz University of Technology Stefanowskiego 18/22 Lodz90-924 Poland

ISBN: (纸本)9780853163237

the computational effort is associated in electrical tomography mainly with forward problem solution. As the recent developments in general purpose graphical processing units (gpgpu) shown possibilities of using parallel computing, the authors of this paper propose a novel algorithm dedicated to forward solving that accelerates the computations vs. classical implementations of imaging algorithms for central processors (CPU). the main contribution here is a method of matrix decomposition coupled with calculation groupings for efficient use of global and local memory usage with minimization of transfers between gpgpu and CPU memory. Tests were conducted for 8 and 16 electrode measurement image reconstructions with use of CUDA based implementation of algorithms and comparison tests for 32 and 64 bit platforms. Proposed algorithm designed for gpgpu enabled several times shorter computation time compared to the algorithm performed by the CPU, which can be directly translated into spatial and/or temporal resolution enhancement of reconstructed images. © 2014 International Society for Industrial Process Tomography.

关键词： Image reconstruction

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A CPU-GPU hybrid implementation and model-driven scheduling of the fast multipole method

A CPU-GPU hybrid implementation and model-driven scheduling ...

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

作者： Choi, Jee Chandramowlishwaran, Aparna Madduri, Kamesh Vuduc, Richard Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States Computational Science and Engineering Georgia Institute of Technology Atlanta GA United States CSAIL Massachusetts Institute of Technology Cambridge MA United States Computer Science and Engineering Department Pennsylvania State University University Park PA United States

ISBN: (纸本)9781450327664

this paper presents an optimized CPU-GPU hybrid implementation and a GPU performance model for the kernelindependent fast multipole method (FMM). We implement an optimized kernel-independent FMM for GPUs, and combine it with our previous CPU implementation to create a hybrid CPU+GPU FMM kernel. When compared to another highly optimized GPU implementation, our implementation achieves as much as a 1.9× speedup. We then extend our previous lower bound analyses of FMM for CPUs to include GPUs. this yields a model for predicting the execution times of the different phases of FMM. using this information, we estimate the execution times of a set of static hybrid schedules on a given system, which allows us to automatically choose the schedule that yields the best performance. In the best case, we achieve a speedup of 1.5× compared to our GPU-only implementation, despite the large difference in computational powers of CPUs and GPUs. We comment on one consequence of having such performance models, which is to enable speculative predictions about FMM scalability on future systems. Copyright 2014 ACM.

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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