Improving the user experience of the rCUDA remote GPU virtualization framework

Graphics processing units (GPUs) are being increasingly embraced by the high‐performance computing community as an effective way to reduce execution time by accelerating parts of their applications. remote CUDA (rCUDA) was recently introduced as a software solution to address the high acquisition costs and energy consumption of GPUs that constrain further adoption of this technology. Specifically, rCUDA is a middleware that allows a reduced number of GPUs to be transparently shared among the nodes in a cluster. Although the initial prototype versions of rCUDA demonstrated its functionality, they also revealed concerns with respect to usability, performance, and support for new CUDA features. In response, in this paper, we present a new rCUDA version that (1) improves usability by including a new component that allows an automatic transformation of any CUDA source code so that it conforms to the needs of the rCUDA framework, (2) consistently features low overhead when using remote GPUs thanks to an improved new communication architecture, and (3) supports multithreaded applications and CUDA libraries. As a result, for any CUDA‐compatible program, rCUDA now allows the use of remote GPUs within a cluster with low overhead, so that a single application running in one node can use all GPUs available across the cluster, thereby extending the single‐node capability of CUDA. Copyright © 2014 John Wiley & Sons, Ltd.     

High‐end colorimetric display characterization using an adaptive training set

Abstract— A new, accurate, and technology‐independent display color‐characterization model is introduced. It is based on polyharmonic spline interpolation and on an optimized adaptive training data set. The establishment of this model is fully automatic and requires only a few minutes, making it efficient in a practical situation. The experimental results are very good for both the forward and inverse models. Typically, the proposed model yields an average model prediction error of about 1 ΔE^ab* unit or below for several displays. The maximum error is shown to be low as well.     

From rendering to tracking point-based 3D models

This paper adds to the abundant visual tracking literature with two main contributions. First, we illustrate the interest of using Graphic Processing Units (GPU) to support efficient implementations of computer vision algorithms, and secondly, we introduce the use of point-based 3D models as a shape prior for real-time 3D tracking with a monocular camera.     

PartialRC: A Partial Recomputing Method for Efficient Fault Recovery on GPGPUs

GPGPUs are increasingly being used to as performance accelerators for HPC (High Performance Computing) applications in CPU/GPU heterogeneous computing systems, including TianHe-1A, the world’s fastest supercomputer in the TOP500 list, built at NUDT (National University of Defense Technology) last year. However, despite their performance advantages, GPGPUs do not provide built-in fault-tolerant mechanisms to offer reliability guarantees required by many HPC applications. By analyzing the SIMT (single-instruction, multiple-thread) characteristics of programs running on GPGPUs, we have developed PartialRC, a new checkpoint-based compiler-directed partial recomputing method, for achieving efficient fault recovery by leveraging the phenomenal computing power of GPGPUs. In this paper, we introduce our PartialRC method that recovers from errors detected in a code region by partially re-computing the region, describe a checkpoint-based faulttolerance framework developed on PartialRC, and discuss an implementation on the CUDA platform. Validation using a range of representative CUDA programs on NVIDIA GPGPUs against FullRC (a traditional full-recomputing Checkpoint-Rollback-Restart fault recovery method for CPUs) shows that PartialRC reduces significantly the fault recovery overheads incurred by FullRC, by 73.5% when errors occur earlier during execution and 74.6% when errors occur later on average. In addition, PartialRC also reduces error detection overheads incurred by FullRC during fault recovery while incurring negligible performance overheads when no fault happens.     

GPU acceleration of the stochastic grid bundling method for early-exercise options

In this work, a parallel graphics processing units (GPU) version of the Monte Carlo stochastic grid bundling method (SGBM) for pricing multi-dimensional early-exercise options is presented. To extend the method's applicability, the problem dimensions and the number of bundles will be increased drastically. This makes SGBM very expensive in terms of computational costs on conventional hardware systems based on central processing units. A parallelization strategy of the method is developed and the general purpose computing on graphics processing units paradigm is used to reduce the execution time. An improved technique for bundling asset paths, which is more efficient on parallel hardware is introduced. Thanks to the performance of the GPU version of SGBM, a general approach for computing the early-exercise policy is proposed. Comparisons between sequential and GPU parallel versions are presented.     

基于OpenCL的直方图生成算法优化方法研究

随着GPU计算能力及可编程性的不断增强,采用GPU作为通用加速器对应用程序进行性能加速已经成为提升程序性能的主要模式。直方图生成算法是计算机视觉的常用算法,在图像处理、模式识别、图像搜索等领域都有着广泛的应用。随着图像处理规模的扩大和实时性要求的提高,通过GPU提升直方图生成算法性能的需求也越来越强。在GPU计算平台关键优化方法和技术的基础上,完成了直方图生成算法在GPU计算平台上的实现及优化。实验结果表明,通过使用直方图备份、访存优化、数据本地化及规约优化等优化方法,直方图生成算法在AMD HD7850 GPU计算平台上的性能相对于优化前的版本达到了1.8~13.3倍的提升;相对于CPU版本,在不同数据规模下也达到了7.2~210.8倍的性能提升。     

CPU-GPGPU异构体系结构相关技术综述

随着GPU的发展,其计算能力和访存带宽都超过了CPU,在GPU上进行通用计算也变得越来越流行,这样就构成了CPU-GPGPU的新型异构体系结构。虽然这种新型体系结构表现出了强大的性能优势并受到了学术界和产业界的广泛关注,但如何更好地在这种结构上高效地编写和运行程序仍然存在很大的挑战。本文综述了针对这一体系结构现有的可编程性技术、可靠性技术和低功耗技术,并结合这些技术展望了CPU-GPGPU这种异构系统的发展趋势。     

High‐speed Marching Cubes using HistoPyramids

We present an implementation approach for Marching Cubes (MC) on graphics hardware for OpenGL 2.0 or comparable graphics APIs. It currently outperforms all other known graphics processing units (GPU)‐based iso‐surface extraction algorithms in direct rendering for sparse or large volumes, even those using the recently introduced geometry shader (GS) capabilites. To achieve this, we outfit the Histogram Pyramid (HP) algorithm, previously only used in GPU data compaction, with the capability for arbitrary data expansion. After reformulation of MC as a data compaction and expansion process, the HP algorithm becomes the core of a highly efficient and interactive MC implementation. For graphics hardware lacking GSs, such as mobile GPUs, the concept of HP data expansion is easily generalized, opening new application domains in mobile visual computing. Further, to serve recent developments, we present how the HP can be implemented in the parallel programming language CUDA (compute unified device architecture), by using a novel 1D chunk/layer construction.     

一种面向GPGPU的行为感知的存储调度策略

随着通用图形处理器在高性能计算领域的广泛应用,新的并行执行模式被提出。在新模式下,当前的存储调度策略未能使存储器的吞吐率达到最大。分析了图形处理器上多程序并行执行模式下应用程序访存行为特征及其性能损失不公平的原因,提出了一种基于访存行为感知的存储调度策略,利用不同程序类型的优势进行优先级调度。实验表明,该方法能够明显改善不同类型程序间性能损失不均衡的问题,相比基准结构对所有测试程序的存储系统吞吐率和公平性分别有平均9.7%和15.0%的提升。