Initial results on computational performance of Intel many integrated core,sandy bridge,and graphical processing unit architectures: implementation of a 1D c++/OpenMP electrostatic particle‐in‐cell code

We present initial comparison performance results for Intel many integrated core (MIC), Sandy Bridge (SB), and graphical processing unit (GPU). A 1D explicit electrostatic particle‐in‐cell code is used to simulate a two‐stream instability in plasma. We compare the computation times for various number of cores/threads and compiler options. The parallelization is implemented via OpenMP with a maximum thread number of 128. Parallelization and vectorization on the GPU is achieved with modifying the code syntax for compatibility with CUDA. We assess the speedup due to various auto‐vectorization and optimization level compiler options. Our results show that the MIC is several times slower than SB for a single thread, and it becomes faster than SB when the number of cores increases with vectorization switched on. The compute times for the GPU are consistently about six to seven times faster than the ones for MIC. Compared with SB, the GPU is about two times faster for a single thread and about an order of magnitude faster for 128 threads. The net speedup, however, for MIC and GPU are almost the same. An initial attempt to offload parts of the code to the MIC coprocessor shows that there is an optimal number of threads where the speedup reaches a maximum. Copyright © 2014 John Wiley & Sons, Ltd.     

基于粒子群优化粒子滤波和CUDA加速的故障诊断方法

在非线性系统中,粒子滤波需要大量粒子才能保证状态估计的准确度,这降低了算法的实时性,导致故障诊断的准确率和实时性不佳。针对该问题,提出基于GPU平台的粒子群优化粒子滤波(PSOPF)并行算法。通过分析PSOPF算法的并行性,设计并实现一种基于CUDA并行计算架构的PSOPF并行算法,利用大量的GPU线程对算法进行加速。为解决拒绝重采样对GPU全局内存的非合并访问带来的执行效率低问题,通过改进拒绝重采样并行算法,使线程束中的线程对同一内存区段中的粒子进行重采样,提高了其执行效率。通过对风力机组变桨距系统故障诊断验证了算法的有效性。实验结果表明,该方法可满足故障诊断准确率和实时性的要求。     

Accelerating incompressible flow computations with a Pthreads-CUDA implementation on small-footprint multi-GPU platforms

Graphics processor units (GPU) that are originally designed for graphics rendering have emerged as massively-parallel “co-processors” to the central processing unit (CPU). Small-footprint multi-GPU workstations with hundreds of processing elements can accelerate compute-intensive simulation science applications substantially. In this study, we describe the implementation of an incompressible flow Navier–Stokes solver for multi-GPU workstation platforms. A shared-memory parallel code with identical numerical methods is also developed for multi-core CPUs to provide a fair comparison between CPUs and GPUs. Specifically, we adopt NVIDIA’s Compute Unified Device Architecture (CUDA) programming model to implement the discretized form of the governing equations on a single GPU. Pthreads are then used to enable communication across multiple GPUs on a workstation. We use separate CUDA kernels to implement the projection algorithm to solve the incompressible fluid flow equations. Kernels are implemented on different memory spaces on the GPU depending on their arithmetic intensity. The memory hierarchy specific implementation produces significantly faster performance. We present a systematic analysis of speedup and scaling using two generations of NVIDIA GPU architectures and provide a comparison of single and double precision computational performance on the GPU. Using a quad-GPU platform for single precision computations, we observe two orders of magnitude speedup relative to a serial CPU implementation. Our results demonstrate that multi-GPU workstations can serve as a cost-effective small-footprint parallel computing platform to accelerate computational fluid dynamics (CFD) simulations substantially.     

Accelerated event-by-event Monte Carlo microdosimetric calculations of electrons and protons tracks on a multi-core CPU and a CUDA-enabled GPU

For microdosimetric calculations event-by-event Monte Carlo (MC) methods are considered the most accurate. The main shortcoming of those methods is the extensive requirement for computational time. In this work we present an event-by-event MC code of low projectile energy electron and proton tracks for accelerated microdosimetric MC simulations on a graphic processing unit (GPU). Additionally, a hybrid implementation scheme was realized by employing OpenMP and CUDA in such a way that both GPU and multi-core CPU were utilized simultaneously. The two implementation schemes have been tested and compared with the sequential single threaded MC code on the CPU. Performance comparison was established on the speed-up for a set of benchmarking cases of electron and proton tracks. A maximum speedup of 67.2 was achieved for the GPU-based MC code, while a further improvement of the speedup up to 20% was achieved for the hybrid approach. The results indicate the capability of our CPU–GPU implementation for accelerated MC microdosimetric calculations of both electron and proton tracks without loss of accuracy.     

异构平台下格子Boltzmann方法实现及性能分析

对CPU+GPU异构平台下的多种并行编程模式进行了研究,并针对格子Boltzmann方法实现了CUDA,MPI+CUDA,MPI+OpenMP+CUDA多级并行算法。结果表明,算法具有较好的加速性能;提出的根据计算量比例参数调节CPU和GPU之间负载均衡的方法,对于在异构平台上实现多级并行处理及资源的有效利用具有一定的参考和应用价值。     

Solving knapsack problems on GPU

A parallel implementation via CUDA of the dynamic programming method for the knapsack problem on NVIDIA GPU is presented. A GTX 260 card with 192 cores (1.4 GHz) is used for computational tests and processing times obtained with the parallel code are compared to the sequential one on a CPU with an Intel Xeon 3.0 GHz. The results show a speedup factor of 26 for large size problems. Furthermore, in order to limit the communication between the CPU and the GPU, a compression technique is presented which decreases significantly the memory occupancy.     

RSA算法的CUDA高效实现技术

CUDA（Compute Unified Device Architecture）作为一种支持GPU通用计算的新型计算架构,在大规模数据并行计算方面得到了广泛的应用。RSA算法是一种计算密集型的公钥密码算法,给出了基于CUDA的RSA算法并行化高效实现技术,其关键为引入大量独立并发的Montgomery模乘线程,并给出了具体的线程组织、数据存储结构以及基于共享内存的性能优化实现技术。根据RSA算法CUDA实现方法,在某款GPU上测试了RSA算法的运算性能和吞吐率。实验结果表明,与RSA算法的通用CPU实现方式相比,CUDA实现能够实现超过40倍的性能加速。     

面向CPU-GPU架构的源到源自动映射方法

针对GPU上应用开发移植困难的问题,提出了一种串行计算源程序到并行计算源程序的映射方法。该方法从串行源程序中获得可并行化循环的层次信息,建立循环体结构与GPU线程的对应关系,生成GPU端核心函数代码;根据变量引用读写属性生成CPU端控制代码。基于该方法实现了一个编译原型系统,完成了C语言源程序到CUDA源程序的自动生成。对原型系统在功能和性能方面的测试结果表明,该系统生成的CUDA源程序与C语言源程序在功能上一致,其性能有显著提高,在一定程度上解决了计算密集型应用向CPU-GPU异构多核系统移植困难的问题。     

Efficient magnetohydrodynamic simulations on distributed multi-GPU systems using a novel GPU Direct–MPI hybrid approach

Modern graphics processing units (GPUs) have been widely utilized in magnetohydrodynamic (MHD) simulations in recent years. Due to the limited memory of a single GPU, distributed multi-GPU systems are needed to be explored for large-scale MHD simulations. However, the data transfer between GPUs bottlenecks the efficiency of the simulations on such systems. In this paper we propose a novel GPU Direct–MPI hybrid approach to address this problem for overall performance enhancement. Our approach consists of two strategies: (1) We exploit GPU Direct 2.0 to speedup the data transfers between multiple GPUs in a single node and reduce the total number of message passing interface (MPI) communications; (2) We design Compute Unified Device Architecture (CUDA) kernels instead of using memory copy to speedup the fragmented data exchange in the three-dimensional (3D) decomposition. 3D decomposition is usually not preferable for distributed multi-GPU systems due to its low efficiency of the fragmented data exchange. Our approach has made a breakthrough to make 3D decomposition available on distributed multi-GPU systems. As a result, it can reduce the memory usage and computation time of each partition of the computational domain. Experiment results show twice the FLOPS comparing to common 2D decomposition MPI-only implementation method. The proposed approach has been developed in an efficient implementation for MHD simulations on distributed multi-GPU systems, called MGPU–MHD code. The code realizes the GPU parallelization of a total variation diminishing (TVD) algorithm for solving the multidimensional ideal MHD equations, extending our work from single GPU computation (Wong et al., 2011) to multiple GPUs. Numerical tests and performance measurements are conducted on the TSUBAME 2.0 supercomputer at the Tokyo Institute of Technology. Our code achieves 2 TFLOPS in double precision for the problem with 1200³ grid points using 216 GPUs.     

Accelerating dissipative particle dynamics simulations on GPUs: Algorithms,numerics and applications

We present a scalable dissipative particle dynamics simulation code, fully implemented on the Graphics Processing Units (GPUs) using a hybrid CUDA/MPI programming model, which achieves 10–30 times speedup on a single GPU over 16 CPU cores and almost linear weak scaling across a thousand nodes. A unified framework is developed within which the efficient generation of the neighbor list and maintaining particle data locality are addressed. Our algorithm generates strictly ordered neighbor lists in parallel, while the construction is deterministic and makes no use of atomic operations or sorting. Such neighbor list leads to optimal data loading efficiency when combined with a two-level particle reordering scheme. A faster in situ generation scheme for Gaussian random numbers is proposed using precomputed binary signatures. We designed custom transcendental functions that are fast and accurate for evaluating the pairwise interaction. The correctness and accuracy of the code is verified through a set of test cases simulating Poiseuille flow and spontaneous vesicle formation. Computer benchmarks demonstrate the speedup of our implementation over the CPU implementation as well as strong and weak scalability. A large-scale simulation of spontaneous vesicle formation consisting of 128 million particles was conducted to further illustrate the practicality of our code in real-world applications.     

Accelerating data gravitation-based classification using GPU

Data gravitation-based classification model, a new physic law inspired classification model, has been demonstrated to be an effective classification model for both standard and imbalanced tasks. However, due to its large scale of gravitational computation during the feature weighting process, DGC suffers from high computational complexity, especially for large data sets. In this paper, we address the problem of speeding up gravitational computation using graphics processing unit (GPU). We design a GPU parallel algorithm namely GPU–DGC to accelerate the feature weighting process of the DGC model. Our GPU–DGC model distributes the gravitational computing process to parallel GPU threads, in order to compute gravitation simultaneously. We use 25 open classification data sets to evaluate the parallel performance of our algorithm. The relationship between the speedup ratio and the number of GPU threads is discovered and discussed based on the empirical studies. The experimental results show the effectiveness of GPU–DGC, with the maximum speedup ratio of 87 to the serial DGC. Its sensitivity to the number of GPU threads is also discovered in the empirical studies.

     

基于CUDA的位并行近似串匹配算法

为满足文本检索、计算生物学等领域海量数据匹配对高性能计算的要求,提出一种基于计算统一设备架构(CUDA)的位并行近似串匹配算法。结合图形处理器(GPU)的高并行计算结构及存储带宽特性,通过优化数据存储方式,实现并行化动态规划矩阵算法(BPM)的加速,并对加速性能进行对比测试。实验结果表明,BPM算法通过GPU加速能获得20倍左右的加速比。     

FILiC:一种CUDA上的交互型库函数框架

CUDA是NVIDIA公司推出的GPU编程模型,它为高效利用GPU计算能力提供了强大的支持。但CUDA线程无法直接访问I/O设备、网卡等外围设备,在CUDA线程与外围设备的交互功能方面,目前CUDA的支持十分有限,仅支持非实时的屏幕打印(printf)。因此提出了一种交互型库函数框架FILiC,它通过设备和主机之间的巧妙交互,高效实现了CUDA线程实时的较完整I/O等函数;并且该框架具有很好的可扩展性,CUDA程序员或者编译器开发者可基于该框架按需求开发新的CUDA线程交互功能。     

Parallelization of a multi-blocked CFD code via three strategies for fluid flow and heat transfer analysis

This paper reports on a parallel implementation of a general 3D multi-block CFD code. The parallelization is achieved by using three strategies. Firstly, it is done on dual-processor PC-clusters where Windows NT systems are running. A multi-thread programming model is adopted for the multi-block code, where one thread corresponds to a block. Shared-memory is used for the exchange of inner-boundaries between neighboring blocks (threads) on the same node, while WinSockets are employed for those on different nodes. Secondly, the parallelization is extended to UNIX operating system. MPI is applied for all the message passing between different processors, including those on the same node. Thirdly, Pthreads (POSIX threads), a standardized application interface for threads, are adopted to take the advantage of the shared-memory feature of the SMP nodes, while MPI is only applied for the message passing between processors on different nodes. In all the strategies, a static load-balancing method is employed for equitable distribution of computational work to specified nodes. The parameters of the present code is studied in detail to facilitate the explanation of the speedup results. Two examples are provided to show the speedup and load balancing of the parallel calculation. Detailed comparison is made to evaluate the efficiency of different strategies.     

基于GPU的GRAPES数值预报系统中RRTM模块的并行化研究

GRAPES(Global and Regional Assimilation and Prediction System)是由中国气象科学研究院自主研究开发的中国新一代数值天气预报系统,由于其处理的数据量非常庞大以及对实时性的要求较高,因此一直是并行计算领域研究的热点。首次运用GPU(图形处理器)通用计算及CUDA技术对CRAPES_Meso。模式中物理过程的RRTM(快速辐射传输模式)长波辐射模块进行并行化处理。在性能分析的基础上,针对GPU体系结构的特点,从代码优化、存储器优化、编译选项等方面对程序性能进行优化,并取得了14X倍的加速比。经过测试表明,长波辐射RRTM模块在GPU上并行计算过程正确、稳定而且有效,并为GRAPES系统未来在GPU平台上的并行化发展奠定了一定的基础。     

Real‐Time Fluid Effects on Surfaces using the Closest Point Method

The Closest Point Method (CPM) is a method for numerically solving partial differential equations (PDEs) on arbitrary surfaces, independent of the existence of a surface parametrization. The CPM uses a closest point representation of the surface, to solve the unmodified Cartesian version of a surface PDE in a 3D volume embedding, using simple and well‐understood techniques. In this paper, we present the numerical solution of the wave equation and the incompressible Navier‐Stokes equations on surfaces via the CPM, and we demonstrate surface appearance and shape variations in real‐time using this method. To fully exploit the potential of the CPM, we present a novel GPU realization of the entire CPM pipeline. We propose a surface‐embedding adaptive 3D spatial grid for efficient representation of the surface, and present a high‐performance approach using CUDA for converting surfaces given by triangulations into this representation. For real‐time performance, CUDA is also used for the numerical procedures of the CPM. For rendering the surface (and the PDE solution) directly from the closest point representation without the need to reconstruct a triangulated surface, we present a GPU ray‐casting method that works on the adaptive 3D grid.     

基于CUDA的弱可压SPH流体建模与仿真

为了实现小尺度范围流体场景的实时、真实感模拟,采用弱可压SPH方法对水体进行建模,提出了流体计算的CPU GPU混合架构计算方法。针对邻域粒子查找算法影响流体计算效率的问题,采用三维空间网格对整个模拟区域进行均匀网格划分,利用并行前缀求和和并行计数排序实现邻域粒子的查找。最后,采用基于CUDA并行加速的Marching Cubes算法实现流体表面提取,利用环境贴图表现流体的反射和折射效果,实现流体表面着色。实验结果表明,所提出的流体建模和模拟算法能实现小尺度范围流体的实时计算和渲染,绘制出水的波动、翻卷和木块在水中晃动的动态效果,当粒子数达到1 048 576个时,GPU并行计算方法相较CPU方法的加速比为60.7。     

基于CUDA的GMM模型快速训练方法及应用

由于能够很好地近似描述任何分布,混合高斯模型(GMM)在模式在识别领域得到了广泛的应用.GMM模型参数通常使用迭代的期望最大化(EM)算法训练获得,当训练数据量非常庞大及模型混合数很大时,需要花费很长的训练时间.NVIDIA公司推出的统一计算设备架构(Computed unified device architecture,CUDA)技术通过在图形处理单元(GPU)并发执行多个线程能够实现大规模并行快速计算.本文提出一种基于CUDA,适用于特大数据量的GMM模型快速训练方法,包括用于模型初始化的K-means算法的快速实现方法,以及用于模型参数估计的EM算法的快速实现方法.文中还将这种训练方法应用到语种GMM模型训练中.实验结果表明,与Intel DualCore PentiumⅣ3.0 GHz CPU的一个单核相比,在NVIDIA GTS250 GPU上语种GMM模型训练速度提高了26倍左右.     

Parallel Computing Experiences with CUDA

The CUDA programming model provides a straightforward means of describing inherently parallel computations, and NVIDIA's Tesla GPU architecture delivers high computational throughput on massively parallel problems. This article surveys experiences gained in applying CUDA to a diverse set of problems and the parallel speedups over sequential codes running on traditional CPU architectures attained by executing key computations on the GPU.     

CUDA并行技术与数字图像几何变换

CUDA是GPU通过并发执行多个线程以实现大规模快速并行计算能力的技术,它能使对GPU编程变得更容易。介绍了CUDA基本特性及主要编程模型,在此基础上,提出并实现了基于NVIDIA CUDA技术的图像快速几何变换。采用位置偏移增量代替原变换算法中大量乘法运算,并把CUDA技术的快速并行计算能力应用到数字图像几何变换中,解决了基于CPU的传统图像几何变换运算效率低下的问题。实验结果证明使用CUDA技术,随着处理图像尺寸的增加,对数字图像几何变换处理效率最高能够提高到近100倍。