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检索条件"任意字段=Proceedings of Workshop on General Purpose Processing Using GPUs"

共 104 条记录，以下是11-20 订阅

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Real-time embedded vision system development using AIBO Vision workshop 2

Real-time embedded vision system development using AIBO Visi...

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5th Mexican International Conference in Computer Science (ENC 2004)

作者： Lovell, N Griffith Univ Sch CIT Inst Intelligent & Integrated Syst Brisbane Qld 4111 Australia

ISBN: (纸本)0769521606

Development of visual processing components in embedded systems is a difficult task. The nature of embedded devices not only limits the processing power available to applications, but the limited I/O facilities also considerably hinder development. Several complexities emerge for testing, debugging, quality evaluation and profiling when developing on an embedded device. This means that vision processing algorithms are not only harder to design for embedded systems, but require more time to debug and test than would be the case on a general purpose computing device. However the increase in deployment of robotic systems has seen embedded vision development becoming increasingly necessary in many problem domains. AIBO Vision workshop 2 (AVW2) is a utility that address these problems associated with vision processing development on embedded systems by providing a flexible, off-line, modularised vision pipeline. That is, AVW2 aids developers in this area as it provides an immersive environment where each step (module) can be flexibly placed in a user configured vision pipeline. The output can then be rigorously inspected and tested The code of the embedded system is executed in the environment of the AVW2 tool allowing profiling (experimental evaluation of the time requirements of the code).

关键词： Computer vision

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proceedings of 2nd workshop on general purpose processing on Graphics processing Units, GPGPU-2

Proceedings of 2nd Workshop on General Purpose Processing on...

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2nd workshop on general purpose processing on Graphics processing Units, GPGPU-2

ISBN: (纸本)9781605585178

The proceedings contain 12 papers. The topics discussed include: accelerating cosmological data analysis with graphics processors;high performance computation and interactive display of molecular orbitals on gpus and multi-core CPUs;GPU acceleration of a production molecular docking code;accelerating phase unwrapping and affine transformations for optical quadrature microscopy using CUDA;performance analysis of accelerated image registration using GPGPU;accelerating LINPACK with CUDA on heterogenous clusters;a high-level directive-based language for GPU programming;architecture-aware optimization targeting multithreaded stream computing;QR decomposition on gpus;3D finite difference computation on gpus using CUDA;optimization of tele-immersion codes;and understanding software approaches for GPGPU reliability.

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GPGPU 2020 - proceedings of the 2020 general purpose processing using GPU

GPGPU 2020 - Proceedings of the 2020 General Purpose Process...

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13th Annual workshop on general purpose processing using Graphics processing Unit, GPGPU 2020

ISBN: (纸本)9781450370257

The proceedings contain 7 papers. The topics discussed include: GPGPU performance estimation for frequency scaling using cross-benchmarking;the Minos computing library: efficient parallel programming for extremely heterogeneous systems;unveiling kernel concurrency in multiresolution filters on gpus with an image processing DSL;high-level hardware feature extraction for GPU performance prediction of stencils;automatic generation of specialized direct convolutions for mobile gpus;custom code generation for a graph DSL;and automated test generation for OpenCL kernels using fuzzing and constraint solving.

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3D finite difference computation on gpus using CUDA 2

3D finite difference computation on GPUs using CUDA

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2nd workshop on general purpose processing on Graphics processing Units, GPGPU-2

作者： Micikevicius, Paulius NVIDIA 2701 San Tomas Expressway Santa Clara CA 95050 United States

ISBN: (纸本)9781605585178

In this paper we describe a GPU parallelization of the 3D finite difference computation using CUDA. Data access redundancy is used as the metric to determine the optimal implementation for both the stencil-only computation, as well as the discretization of he wave equation, which is currently of great interest in seismic computing. For the larger stencils, the described approach achieves the throughput of between 2,400 to over 3,000 million of output points per second on a single Tesla 10-series GPU. This is roughly an order of magnitude higher than a 4-core Harpertown CPU running a similar code from seismic industry. Multi-GPU parallelization is also described, achieving linear scaling with gpus by overlapping inter-GPU communication with computation. © 2009 ACM.

关键词： Graphics processing unit

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GPGPU Performance Estimation for Frequency Scaling using Cross-Benchmarking 20

GPGPU Performance Estimation for Frequency Scaling Using Cro...

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13th Annual workshop on general purpose processing using Graphics processing Unit (GPGPU)

作者： Wang, Qiang Liu, Chengjian Chu, Xiaowen Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China

ISBN: (纸本)9781450370257

Dynamic Voltage and Frequency Scaling (DVFS) on general-purpose Graphics processing Units (GPgpus) is now becoming one of the most significant techniques to balance computational performance and energy consumption. However, there are still few fast and accurate models for predicting GPU kernel execution time under different core and memory frequency settings, which is important to determine the best frequency configuration for energy saving. Accordingly, a novel GPGPU performance estimation model with both core and memory frequency scaling is herein proposed. We design a cross-benchmarking suite, which simulates kernels with a wide range of instruction distributions. The synthetic kernels generated by this suite can be used for model pre-training or as supplementary training samples. Then we apply two different machine learning algorithms, Support Vector Regression (SVR) and Gradient Boosting Decision Tree (GBDT), to study the correlation between kernel performance counters and kernel performance. The models trained only with our cross-benchmarking suite achieve satisfying accuracy (16%similar to 22% mean absolute error) on 24 unseen real application kernels. Validated on three modern gpus with a wide frequency scaling range, by using a collection of 24 real application kernels, the proposed model is able to achieve accurate results (5.1%, 2.8%, 6.5% mean absolute error) for the target gpus (GTX 980, Titan X Pascal and Tesla P100).

关键词： Graphics processing Units Dynamic Voltage and Frequency Scaling GPU Performance Modeling Machine Learning

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Parallel spatial query processing on gpus using R-trees

Parallel spatial query processing on GPUs using R-trees

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2nd ACM SIGSPATIAL International workshop on Analytics for Big Geospatial Data, BigSpatial 2013

作者： You, Simin Zhang, Jianting Gruenwald, Le Dept. of Computer Science CUNY Graduate Center New York NY 10016 United States Dept. of Computer Science City College of New York New York NY 10031 United States School of Computer Science University of Oklahoma Norman OK 73071 United States

ISBN: (纸本)9781450325349

R-Trees are popular spatial indexing techniques that have been widely adopted in many geospatial applications. As commodity gpus (Graphics processing Units) are increasingly becoming available on personal workstations and cluster computers, there are considerable research interests in applying the massive data parallel GPGPU (general purpose computing on gpus) technologies to index and query large-scale geospatial data on gpus using R-Trees. In this study, we aim at evaluating the potentials of accelerating both R-Tree bulk loading and spatial window query processing on gpus using R-Trees. In addition to designing an efficient data layout schema for R-Trees on gpus, we have implemented several parallel spatial window query processing techniques on gpus using both dynamically generated R-Trees constructed on CPUs and bulk loaded R-Trees constructed on gpus. Extensive experiments using both synthetic and real-world datasets have shown that our GPU based parallel query processing techniques using R-Trees can achieve about 10X speedups on average over 8-core CPU parallel implementations by effectively utilizing large numbers of processors and high memory bandwidth on gpus. © 2013 ACM.

关键词： Query processing

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3rd workshop on general-purpose Computation on Graphics processing Units, GPGPU-3, Held in cooperation with ACM ASPLOS XV

3rd Workshop on General-Purpose Computation on Graphics Proc...

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3rd workshop on general-purpose Computation on Graphics processing Units, GPGPU-3, Held in cooperation with ACM ASPLOS XV

ISBN: (纸本)9781605589350

The proceedings contain 12 papers. The topics discussed include: parallel multiclass classification using SVMs on gpus;cortical architectures on a GPGPU;compiling python to a hybrid execution environment;modeling GPU-CPU workloads and systems;implementing the PGI accelerator model;a mapping path for multi-GPGPU accelerated computers from a portable high level programming abstraction;GPGPU role within a 500 TFLOPS heterogeneous cluster;the scalable heterogeneous computing (SHOC) benchmark suite;accelerating MATLAB image processing toolbox functions on gpus;best-effort semantic document search on gpus;accelerating SQL database operations on a GPU with CUDA;accelerating the local outlier factor algorithm on a GPU for intrusion detection systems;and iterative induced dipoles computation for molecular mechanics on gpus.

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Multi-stage programming for gpus in C++ using PACXX 16

Multi-stage programming for GPUs in C++ using PACXX

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9th Annual workshop on general purpose processing using Graphics processing Unit, GPGPU 2016

作者： Haidl, Michael Steuwer, Michel Humernbrum, Tim Gorlatch, Sergei University of Muenster Germany University of Edinburgh United Kingdom

ISBN: (纸本)9781450341950

Writing and optimizing programs for high performance on systems with Graphics processing Units (gpus) remains a challenging task even for expert programmers. A promising optimization technique is multi-stage programming - evaluating parts of the program upfront on the CPU and embedding the computed values in the GPU code, thus allowing for more aggressive compiler optimizations. Unfortunately, such optimizations are not possible in CUDA, whereas to apply them in OpenCL, programmers are forced to manipulate the GPU source code as plain strings, which is error-prone and type-unsafe. In this paper, we describe PACXX - our approach to GPU programming in C++, with the convenient features of modern C++14 standard: type deduction, lambda expressions, and algorithms from the standard template library (STL). using PACXX, a GPU program is written as a single C++ program, rather than two distinct host and kernel programs. We extend PACXX with an easy-to-use and type-safe API for multi-stage programming avoiding the pitfalls of string manipulation. using just-in-time compilation techniques, PACXX generates efficient GPU code at runtime. Our evaluation shows that using PACXX allows for writing multi-stage code easier and safer than currently possible in CUDA or OpenCL. With two application studies we demonstrate that multi-stage programs can significantly outperform equivalent non-staged versions. Furthermore, we show that PACXX generates code with high performance, comparable to industrial-strength OpenCL compilers. © 2016 ACM.

关键词： Graphics processing unit

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Automated Test Generation for OpenCL Kernels using Fuzzing and Constraint Solving 20

Automated Test Generation for OpenCL Kernels using Fuzzing a...

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13th Annual workshop on general purpose processing using Graphics processing Unit (GPGPU)

作者： Peng, Chao Rajan, Ajitha Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland

ISBN: (纸本)9781450370257

Graphics processing Units (gpus) are massively parallel processors offering performance acceleration and energy efficiency unmatched by current processors (CPUs) in computers. These advantages along with recent advances in the programmability of gpus have made them attractive for general-purpose computations. Despite the advances in programmability, GPU kernels are hard to code and analyse due to the high complexity of memory sharing patterns, striding patterns for memory accesses, implicit synchronisation, and combinatorial explosion of thread interleavings. Existing few techniques for testing GPU kernels use symbolic execution for test generation that incur a high overhead, have limited scalability and do not handle all data types. We propose a test generation technique for OpenCL kernels that combines mutation-based fuzzing and selective constraint solving with the goal of being fast, effective and scalable. Fuzz testing for GPU kernels has not been explored previously. Our approach for fuzz testing randomly mutates input kernel argument values with the goal of increasing branch coverage. When fuzz testing is unable to increase branch coverage with random mutations, we gather path constraints for uncovered branch conditions and invoke the Z3 constraint solver to generate tests for them. In addition to the test generator, we also present a schedule amplifier that simulates multiple work-group schedules, with which to execute each of the generated tests. The schedule amplifier is designed to help uncover inter work-group data races. We evaluate the effectiveness of the generated tests and schedule amplifier using 217 kernels from open source projects and industry standard benchmark suites measuring branch coverage and fault finding. We find our test generation technique achieves close to 100% coverage and mutation score for majority of the kernels. Overhead incurred in test generation is small (average of 0.8 seconds). We also confirmed our technique scales easily

关键词： Software Testing GPU OpenCL Test Case Generation Fuzz Testing Constraint Solving Data Race

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5th workshop on general-purpose Computation on Graphics processing Units, GPGPU-5 - Held in Cooperation with ACM ASPLOS XVII

5th Workshop on General-Purpose Computation on Graphics Proc...

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5th workshop on general-purpose Computation on Graphics processing Units, GPGPU-5 - Held in Cooperation with ACM ASPLOS XVII

ISBN: (纸本)9781450312332

The proceedings contain 13 papers. The topics discussed include: a distributed data-parallel framework for analysis and visualization algorithm development;FLAT: a GPU programming framework to provide embedded MPI;a GPU-based high-throughput image retrieval algorithm;dynamic particle system for mesh extraction on the GPU;high-performance sparse matrix-vector multiplication on gpus for structured grid computations;high performance 3-D FFT using multiple CUDA gpus;Paragon: collaborative speculative loop execution on GPU and CPU;JaBEE: framework for object-oriented Java bytecode compilation and execution on graphics processor units;enabling task-level scheduling on heterogeneous platforms;auto-tuning interactive ray tracing using an analytical GPU architecture model;full system simulation of many-core heterogeneous SoCs using GPU and QEMU semihosting;and reducing off-chip memory traffic by selective cache management scheme in GPgpus.

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