GPU-OSDDA: a bit-vector GPU-based deadlock detection algorithm for single-unit resource systems

This article presents a GPU-based single-unit deadlock detection methodology and its algorithm, GPU-OSDDA. Our GPU-based design utilizes parallel hardware of GPU to perform computations and thus is able to overcome the major limitation of prior hardware-based approaches by having the capability of handling thousands of processes and resources, whilst achieving real-world run-times. By utilizing a bit-vector technique for storing algorithm matrices and designing novel, efficient algorithmic methods, we not only reduce memory usage dramatically but also achieve two orders of magnitude speedup over CPU equivalents. Additionally, GPU-OSDDA acts as an interactive service to the CPU, because all of the aforementioned computations and matrix management techniques take place on the GPU, requiring minimal interaction with the CPU. GPU-OSDDA is implemented on three GPU cards: Tesla C2050, Tesla K20c, and Titan X. Our design shows overall speedups of 6-595X over CPU equivalents.     

A GPU‐based Streaming Algorithm for High‐Resolution Cloth Simulation

We present a GPU‐based streaming algorithm to perform high‐resolution and accurate cloth simulation. We map all the components of cloth simulation pipeline, including time integration, collision detection, collision response, and velocity updating to GPU‐based kernels and data structures. Our algorithm perform intra‐object and inter‐object collisions, handles contacts and friction, and is able to accurately simulate folds and wrinkles. We describe the streaming pipeline and address many issues in terms of obtaining high throughput on many‐core GPUs. In practice, our algorithm can perform high‐fidelity simulation on a cloth mesh with 2M triangles using 3GB of GPU memory. We highlight the parallel performance of our algorithm on three different generations of GPUs. On a high‐end NVIDIA Tesla K20c, we observe up to two orders of magnitude performance improvement as compared to a single‐threaded CPU‐based algorithm, and about one order of magnitude improvement over a 16‐core CPU‐based parallel implementation.     

A CPU-GPU hybrid approach for the unsymmetric multifrontal method

Multifrontal is an efficient direct method for solving large-scale sparse and unsymmetric linear systems. The method transforms a large sparse matrix factorization process into a sequence of factorizations involving smaller dense frontal matrices. Some of these dense operations can be accelerated by using a graphic processing unit (GPU). We analyze the unsymmetric multifrontal method from both an algorithmic and implementational perspective to see how a GPU, in particular the NVIDIA Tesla C2070, can be used to accelerate the computations. Our main accelerating strategies include (i) performing BLAS on both CPU and GPU, (ii) improving the communication efficiency between the CPU and GPU by using page-locked memory, zero-copy memory, and asynchronous memory copy, and (iii) a modified algorithm that reuses the memory between different GPU tasks and sets thresholds to determine whether certain tasks be performed on the GPU. The proposed acceleration strategies are implemented by modifying UMFPACK, which is an unsymmetric multifrontal linear system solver. Numerical results show that the CPU-GPU hybrid approach can accelerate the unsymmetric multifrontal solver, especially for computationally expensive problems.     

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.     

GPU-accelerated preconditioned iterative linear solvers

This work is an overview of our preliminary experience in developing a high-performance iterative linear solver accelerated by GPU coprocessors. Our goal is to illustrate the advantages and difficulties encountered when deploying GPU technology to perform sparse linear algebra computations. Techniques for speeding up sparse matrix-vector product (SpMV) kernels and finding suitable preconditioning methods are discussed. Our experiments with an NVIDIA TESLA M2070 show that for unstructured matrices SpMV kernels can be up to 8 times faster on the GPU than the Intel MKL on the host Intel Xeon X5675 Processor. Overall performance of the GPU-accelerated Incomplete Cholesky (IC) factorization preconditioned CG method can outperform its CPU counterpart by a smaller factor, up to 3, and GPU-accelerated The incomplete LU (ILU) factorization preconditioned GMRES method can achieve a speed-up nearing 4. However, with better suited preconditioning techniques for GPUs, this performance can be further improved.     

A mixed-precision algorithm for the solution of Lyapunov equations on hybrid CPU-GPU platforms

We describe a hybrid Lyapunov solver based on the matrix sign function, where the intensive parts of the computation are accelerated using a graphics processor (GPU) while executing the remaining operations on a general-purpose multi-core processor (CPU). The initial stage of the iteration operates in single-precision arithmetic, returning a low-rank factor of an approximate solution. As the main computation in this stage consists of explicit matrix inversions, we propose a hybrid implementation of Gauß-Jordan elimination using look-ahead to overlap computations on GPU and CPU. To improve the approximate solution, we introduce an iterative refinement procedure that allows to cheaply recover full double-precision accuracy. In contrast to earlier approaches to iterative refinement for Lyapunov equations, this approach retains the low-rank factorization structure of the approximate solution. The combination of the two stages results in a mixed-precision algorithm, that exploits the capabilities of both general-purpose CPUs and many-core GPUs and overlaps critical computations. Numerical experiments using real-world data and a platform equipped with two Intel Xeon QuadCore processors and an Nvidia Tesla C1060 show a significant efficiency gain of the hybrid method compared to a classical CPU implementation.     

HPCCD: Hybrid Parallel Continuous Collision Detection using CPUs and GPUs

We present a novel, hybrid parallel continuous collision detection (HPCCD) method that exploits the availability of multi‐core CPU and GPU architectures. HPCCD is based on a bounding volume hierarchy (BVH) and selectively performs lazy reconstructions. Our method works with a wide variety of deforming models and supports self‐collision detection. HPCCD takes advantage of hybrid multi‐core architectures – using the general‐purpose CPUs to perform the BVH traversal and culling while GPUs are used to perform elementary tests that reduce to solving cubic equations. We propose a novel task decomposition method that leads to a lock‐free parallel algorithm in the main loop of our BVH‐based collision detection to create a highly scalable algorithm. By exploiting the availability of hybrid, multi‐core CPU and GPU architectures, our proposed method achieves more than an order of magnitude improvement in performance using four CPU‐cores and two GPUs, compared to using a single CPU‐core. This improvement results in an interactive performance, up to 148 fps, for various deforming benchmarks consisting of tens or hundreds of thousand triangles.     

Parallelization of 2D MPDATA EULAG algorithm on hybrid architectures with GPU accelerators

EULAG (Eulerian/semi-Lagrangian fluid solver) is an established computational model developed for simulating thermo-fluid flows across a wide range of scales and physical scenarios. The dynamic core of EULAG includes the multidimensional positive definite advection transport algorithm (MPDATA) and elliptic solver. In this work we investigate aspects of an optimal parallel version of the 2D MPDATA algorithm on modern hybrid architectures with GPU accelerators, where computations are distributed across both GPU and CPU components.Using the hybrid OpenMP–OpenCL model of parallel programming opens the way to harness the power of CPU–GPU platforms in a portable way. In order to better utilize features of such computing platforms, comprehensive adaptations of MPDATA computations to hybrid architectures are proposed. These adaptations are based on efficient strategies for memory and computing resource management, which allow us to ease memory and communication bounds, and better exploit the theoretical floating point efficiency of CPU–GPU platforms. The main contributions of the paper are:

• •method for the decomposition of the 2D MPDATA algorithm as a tool to adapt MPDATA computations to hybrid architectures with GPU accelerators by minimizing communication and synchronization between CPU and GPU components at the cost of additional computations;
• •method for the adaptation of 2D MPDATA computations to multicore CPU platforms, based on space and temporal blocking techniques;
• •method for the adaptation of the 2D MPDATA algorithm to GPU architectures, based on a hierarchical decomposition strategy across data and computation domains, with support provided by the developed GPU task scheduler allowing for the flexible management of available resources;
• •approach to the parametric optimization of 2D MPDATA computations on GPUs using the autotuning technique, which allows us to provide a portable implementation methodology across a variety of GPUs.

Hybrid platforms tested in this study contain different numbers of CPUs and GPUs – from solutions consisting of a single CPU and a single GPU to the most elaborate configuration containing two CPUs and two GPUs. Processors of different vendors are employed in these systems – both Intel and AMD CPUs, as well as GPUs from NVIDIA and AMD. For all the grid sizes and for all the tested platforms, the hybrid version with computations spread across CPU and GPU components allows us to achieve the highest performance. In particular, for the largest MPDATA grids used in our experiments, the speedups of the hybrid versions over GPU and CPU versions vary from 1.30 to 1.69, and from 1.95 to 2.25, respectively.     

Optimising lossless stages in a GPU-based MPEG encoder

Modern GPUs excel in parallel computations, so they are an interesting target to perform matrix transformations such as the DCT, a fundamental part of MPEG video coding algorithms. Considering a system to encode synthetic video (e.g., computer-generated frames), this approach becomes even more appealing, since the images to encode are already in the GPU, eliminating the costs of transferring raw video from the CPU to the GPU. However, after a raw frame has been transformed and quantized by the GPU, the resulting coefficients must be reordered, entropy encoded and framed into the resulting MPEG bitstream. These last steps are essentially sequential and their straightforward GPU implementation is inefficient compared to CPU-based implementations. We present different approaches to implement part of these steps in GPU, aiming for a better usage of the memory bus, compensating the suboptimal use of the GPU with the gains in transfer time. We analyze three approaches to perform the zigzag scan and Huffman coding combining GPU and CPU, and two approaches to assemble the results to build the actual output bitstream both in GPU and CPU memory. Our experiments show that optimising the amount of data transferred from GPU to CPU implementing the last sequential compression steps in the GPU, and using a parallel fast scan implementation of the zigzag scanning improve the overall performance of the system. Savings in transfer time outweigh the extra cost incurred in the GPU.     

PMVS算法的CPU多线程和GPU两级粒度并行策略

PMVS(Patch-based Multi-View Stereo)三维重建算法被广泛应用于无人机航拍影像的三维场景重建中。针对PMVS三维重建算法计算量大、时间复杂度高的问题,提出了PMVS算法的CPU多线程和GPU两级粒度并行策略(Multithread and GPU Parallel Schema,MGPS),方法具体包括:基于GPU的PMVS算法特征提取和片面扩散的并行设计;多影像的GPU和CPU任务分配机制,以使得部分任务分配给CPU采用多线程并行,部分任务分配给GPU并行时,程序总运行时间最短。实验采用搭载24核CPU和NVIDIA Tesla K20 GPU的高性能服务器作为测试平台,针对分辨率为4081×2993的16幅无人机影像进行三维重建。实验结果表明,相比串行的PMVS算法,基于MGPS的PMVS算法取得4倍左右的加速比,其中特征提取最高加速13倍,计算误差在10%以内,该方法实现了更高效的PMVS三维重建。基于MGPS的PMVS算法还可用于文物保护、医学图像处理、虚拟现实等领域。     

基于GPU的LARED-P算法加速

GPU拥有几百GFlops甚至上TFlops的浮点计算能力,将GPU应用于粒子模拟,可有效提高大规模粒子模拟的速度,降低计算成本。本文利用GPU加速三维激光等离子体模拟算法LARED-P,提出了基于CPU+GPU的任务划分、GPU上任务分解、大规模计算核心的分解方法,结合使用了寄存器、纹理内存对算法进行加速。在双精度条件下,移植后的算法在工作频率为1.44GHz的NVIDIA Tesla S1070的单个GPU上获得了相当于主频2.4GHz的Intel(R)Core(TM)2 Quad CPU Q6600单核的6倍加速比。     

基于GPU的遥感图像配准并行程序设计与存储优化

遥感图像配准是遥感图像应用的一个重要处理步骤.随着遥感图像数据规模与遥感图像配准算法计算复杂度的增大,遥感图像配准面临着处理速度的挑战.最近几年,GPU计算能力得到极大提升,面向通用计算领域得到了快速发展.结合GPU面向通用计算领域的优势与遥感图像配准面临的处理速度问题,研究了GPU加速处理遥感图像配准的算法.选取计算量大计算精度高的基于互信息小波分解配准算法进行GPU并行设计,提出了GPU并行设计模型;同时选取GPU程序常用面向存储级的优化策略应用于遥感图像配准GPU程序,并利用CUDA(compute unified device architecture)编程语言在nVIDIA Tesla M2050GPU上进行了实验.实验结果表明,提出的并行设计模型与面向存储级的优化策略能够很好地适用于遥感图像配准领域,最大加速比达到了19.9倍.研究表明GPU通用计算技术在遥感图像处理领域具有广阔的应用前景.     

A fast algorithm for constructing inverted files on heterogeneous platforms

Given a collection of documents residing on a disk, we develop a new strategy for processing these documents and building the inverted files extremely quickly. Our approach is tailored for a heterogeneous platform consisting of multicore CPUs and highly multithreaded GPUs. Our algorithm is based on a number of novel techniques, including a high-throughput pipelined strategy, a hybrid trie and B-tree dictionary data structure, dynamic work allocation to CPU and GPU threads, and optimized CUDA indexer implementation. We have performed extensive tests of our algorithm on a single node (two Intel Xeon X5560 Quad-core CPUs) with two NVIDIA Tesla C1060 GPUs attached to it, and were able to achieve a throughput of more than 262 MB/s on the ClueWeb09 dataset. Similar results were obtained for widely different datasets. The throughput of our algorithm is superior to the best known algorithms reported in the literature even when compared to those run on large clusters.     

Fast and Scalable CPU/GPU Collision Detection for Rigid and Deformable Surfaces

We present a new hybrid CPU/GPU collision detection technique for rigid and deformable objects based on spatial subdivision. Our approach efficiently exploits the massive computational capabilities of modern CPUs and GPUs commonly found in off‐the‐shelf computer systems. The algorithm is specifically tailored to be highly scalable on both the CPU and the GPU sides. We can compute discrete and continuous external and self‐collisions of non‐penetrating rigid and deformable objects consisting of many tens of thousands of triangles in a few milliseconds on a modern PC. Our approach is orders of magnitude faster than earlier CPU‐based approaches and up to twice as fast as the most recent GPU‐based techniques.     

A user mode CPU–GPU scheduling framework for hybrid workloads

Cloud platforms composed of multi-core CPU and many-core Graphics Processing Unit (GPU) have become powerful platforms to host incremental CPU–GPU workloads. In this paper, we study the problem of optimizing the CPU resource management while keeping the quality of service (QoS) of games. To this end, we propose vHybrid, a lightweight user mode runtime framework, in which we integrate a scheduling algorithm for GPU and two algorithms for CPU to efficiently utilize CPU resources with the control accuracy of QoS. vHybrid can maintain the desired QoS with low CPU utilization, while being able to guarantee better QoS performance with little overhead. Our evaluations show that vHybrid saves 37.29% of CPU utilization with satisfactory QoS for hybrid workloads, and reduces three orders of magnitude for QoS fluctuations, without any impact on GPU workloads.     

基于CUDA的塔台模拟机冲突检测算法

塔台模拟机冲突检测算法是一种耗时大的并行算法。针对其导致塔台模拟系统核心服务器CPU负担过重的缺点,在常用冲突检测算法的基础上,提出一种基于统一设备构架（CUDA）的塔台模拟机冲突检测实现方案。首先介绍GPU并行运算的体系结构基础,并将基于卡尔曼滤波的目标物体跟踪技术的分层冲突检测算法移植到GPU。然后利用相同价格的CPU和GPU对比运算效果。实验结果表明：与相同算法的CPU实现方案相比,GPU实现方案将计算效率提高10～50倍。使用此方案,极大地减轻了核心服务器的负担,使塔台模拟机的性能得到质的提高。     

海洋环流数值模式POP的GPU并行优化

POP是一种全球海洋环流模式,广泛应用于海洋研究和气候预测。但是,随着模式分辨率的提高,POP对计算能力的需求呈几何级数增长,从而限制了POP模式的发展。本文在分析POP原理和特征的基础上,采用CUDA Fortran编程模型将POP模式移植到GPU平台上,并采用了网格块间并行和网格块内并行相结合的多层次并行实现全局存储器合并访问,减少局部存储器的使用,利用寄存器提高数据重用度和增大GPU端代码以减少CPU与GPU间的通信等优化策略。实验结果表明,与运行在Intel Xeon X56756核处理器上的串行程序和6进程并行程序相比,GPUPOP可以分别获得8.47倍和1.5倍的加速效果。     

Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model

A Modern Graphics Processing unit (GPU) is able to perform massively parallel scientific computations at low cost. We extend our implementation of the checkerboard algorithm for the two-dimensional Ising model [T. Preis et al., Journal of Chemical Physics 228 (2009) 4468-4477] in order to overcome the memory limitations of a single GPU which enables us to simulate significantly larger systems. Using multi-spin coding techniques, we are able to accelerate simulations on a single GPU by factors up to 35 compared to an optimized single Central Processor Unit (CPU) core implementation which employs multi-spin coding. By combining the Compute Unified Device Architecture (CUDA) with the Message Parsing Interface (MPI) on the CPU level, a single Ising lattice can be updated by a cluster of GPUs in parallel. For large systems, the computation time scales nearly linearly with the number of GPUs used. As proof of concept we reproduce the critical temperature of the 2D Ising model using finite size scaling techniques.     

基于图形处理器的边缘检测算法

边缘检测是一种高度并行的算法,计算量较大,传统的CPU处理难以满足实时要求。针对图像边缘检测问题的计算密集性,在分析常用边缘检测算法的基础上,利用CUDA(Compute Unified Device Architecture,计算统一设备架构)软硬件体系架构,提出了图像边缘检测的GPU(Graphics Processing Unit,图形处理器)实现方案。首先介绍GPU高强度并行运算的体系结构基础,并将Roberts和Sobel这两个具有代表性的图像边缘检测算法移植到GPU,然后利用当前同等价格的CPU和GPU进行对比实验,利用多幅不同分辨率图像作为测试数据,对比CPU和GPU方案的计算效率。实验结果表明,与相同算法的CPU实现相比,其GPU实现获得了相同的处理效果,并将计算效率最高提升到了17倍以上,以此证明GPU在数字图像处理的实际应用中大有潜力。     

Scalable and fast SVM regression using modern hardware

Support Vector Machine (SVM) regression is an important technique in data mining. The SVM training is expensive and its cost is dominated by: (i) the kernel value computation, and (ii) a search operation which finds extreme training data points for adjusting the regression function in every training iteration. Existing training algorithms for SVM regression are not scalable to large datasets because: (i) each training iteration repeatedly performs expensive kernel value computations, which is inefficient and requires holding the whole training dataset in memory; (ii) the search operation used in each training iteration considers the whole search space which is very expensive. In this article, we significantly improve the scalability and efficiency of SVM regression by exploiting the high performance of Graphics Processing Units (GPUs) and solid state drives (SSDs). Our key ideas are as follows. (i) To reduce the cost of repeated kernel value computations and avoid holding the whole training dataset in the GPU memory, we precompute all the kernel values and store them in the CPU memory extended by the SSD; together with an efficient strategy to read the precomputed kernel values, reusing precomputed kernel values with an efficient retrieval is much faster than computing them on-the-fly. This also alleviates the restriction that the training dataset has to fit into the GPU memory, and hence makes our algorithm scalable to large datasets, especially for large datasets with very high dimensionality. (ii) To enhance the performance of the frequently used search operation, we design an algorithm that minimizes the search space and the number of accesses to the GPU global memory; this optimized search algorithm also avoids branch divergence (one of the causes for poor performance) among GPU threads to achieve high utilization of the GPU resources. Our proposed techniques together form a scalable solution to the SVM regression which we call SIGMA. Our extensive experimental results show that SIGMA is highly efficient and can handle very large datasets which the state-of-the-art GPU-based algorithm cannot handle. On the datasets of size that the state-of-the-art GPU-based algorithm can handle, SIGMA consistently outperforms the state-of-the-art GPU-based algorithm by an order of magnitude and achieves up to 86 times speedup.