检索结果-内蒙古大学图书馆

parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds

COMPUTER GRAPHICS FORUM 2019年第5期38卷 163-173页

作者： Jakob, J. Buchenau, C. Guthe, M. Univ Bayreuth Visual Comp Bayreuth Germany

A mandatory component for many point set algorithms is the availability of consistently oriented vertex-normals (e.g. for surface reconstruction, feature detection, visualization). Previous orientation methods on meshes or raw point clouds do not consider a global context, are often based on unrealistic assumptions, or have extremely long computation times, making them unusable on real-world data. We present a novel massively parallelized method to compute globally consistent oriented point normals for raw and unsorted point clouds. Built on the idea of graph-based energy optimization, we create a complete kNN-graph over the entire point cloud. A new weighted similarity criterion encodes the graph-energy. To orient normals in a globally consistent way we perform a highly parallel greedy edge collapse, which merges similar parts of the graph and orients them consistently. We compare our method to current state-of-the-art approaches and achieve speedups of up to two orders of magnitude. The achieved quality of normal orientation is on par or better than existing solutions, especially for real-world noisy 3D scanned data.

关键词： CCS Concepts center dot Computing methodologies -> Shape analysis center dot Theory of computation -> Computational geometry massively parallel algorithms

来源：评论

学校读者我要写书评

暂无评论

Highly scalable SFC-based dynamic load balancing and its application to atmospheric modeling

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 82卷 575-590页

作者： Lieber, Matthias Nagel, Wolfgang E. Tech Univ Dresden Ctr Informat Serv & High Performance Comp D-01062 Dresden Germany Tech Univ Dresden Comp Architecture D-01062 Dresden Germany

Load balance is one of the major challenges for efficient supercomputing, especially for applications that exhibit workload variations. Various dynamic load balancing and workload partitioning methods have been developed to handle this issue by migrating workload between nodes periodically during the runtime. However, on today's top HPC systems - and even more so on future exascale systems - runtime performance and scalability of these methods becomes a concern, due to the costs exceeding the benefits of dynamic load balancing. In this work, we focus on methods based on space-filling curves (SFC), a well-established and comparably fast approach for workload partitioning. SFCs reduce the partitioning problem from n dimensions to one dimension. The remaining task, the so-called 1D partitioning problem or chains-on-chains partitioning problem, is to decompose a 1D workload array into consecutive, balanced partitions. While published parallel heuristics for this problem cannot reliably deliver the required workload balance, especially at large scale, exact algorithms are infeasible due to their sequential nature. We therefore propose a hierarchical method that combines a heuristic and an exact algorithm and allows to trade-off between these two approaches. We compare load balance, execution time, application communication, and task migration of the algorithms using real-life workload data from two different applications on two different HPC systems. The hierarchical method provides a significant speed-up compared to exact algorithms and yet achieves nearly the optimal load balance. On a Blue Gene/Q system, it is able to partition 2.6 million tasks for 524 288 processes with over 99% of the optimal balance in 23.4 ms only, while a published fast exact algorithm requires 6.4 s. We also provide a comparison to parallel load balancing methods implemented in the Zoltan library and present results from applying our methods to COSMO-SPECS+FD4, a detailed atmospheric simulation

关键词： massively parallel algorithms Dynamic load balancing Space-filling curves One-dimensional partitioning Earth and atmospheric sciences

来源：评论

学校读者我要写书评

暂无评论

GPU-based Polynomial Finite Element Matrix Assembly for Simplex Meshes

引用

COMPUTER GRAPHICS FORUM 2018年第7期37卷 443-454页

作者： Mueller-Roemer, J. S. Stork, A. TU Darmstadt & Fraunhofer IGD Darmstadt Germany

In this paper, we present a matrix assembly technique for arbitrary polynomial order finite element simulations on simplex meshes for graphics processing units (GPU). Compared to the current state of the art in GPU-based matrix assembly, we avoid the need for an intermediate sparse matrix and perform assembly directly into the final, GPU-optimized data structure. Thereby, we avoid the resulting 180% to 600% memory overhead, depending on polynomial order, and associated allocation time, while simplifying the assembly code and using a more compact mesh representation. We compare our method with existing algorithms and demonstrate significant speedups.

关键词： CCS Concepts •Computing methodologies → massively parallel and high‐performance simulations massively parallel algorithms Physical simulation Graphics processors •Mathematics of computing → Combinatoric problems

来源：评论

学校读者我要写书评

暂无评论

parallel Approximation of the Maximum Likelihood Estimation for the Prediction of Large-Scale Geostatistics Simulations

Parallel Approximation of the Maximum Likelihood Estimation ...

引用

IEEE International Conference on Cluster Computing (CLUSTER)

作者： Abdulah, Sameh Ltaief, Hatem Sun, Ying Genton, Marc G. Keyes, David E. King Abdullah Univ Sci Technol Extreme Comp Res Ctr Comp Comp Elect & Math Sci & Engn Div Thuwal Saudi Arabia

ISBN: (纸本)9781538683194

Maximum likelihood estimation is an important statistical technique for estimating missing data, for example in climate and environmental applications, which are usually large and feature data points that are irregularly spaced. In particular, the Gaussian log-likelihood function is the de facto model, which operates on the resulting sizable dense covariance matrix. The advent of high performance systems with advanced computing power and memory capacity have enabled full simulations only for rather small dimensional climate problems, solved at the machine precision accuracy. The challenge for high dimensional problems lies in the computation requirements of the hiti-likelihood function, which necessitates O(n(2)) storage and O(n(3)) operations, where n represents the number of given spatial locations. This prohibitive computational cost may be reduced by using approximation techniques that not only enable large-scale simulations otherwise intractable, but also maintain the accuracy and the fidelity of the spatial statistics model. In this paper, we extend the Exascale GeoStatistics software framework (i.e., ExaGeoStat(1)) to support the Tile Low-Rank (TLR) approximation technique, which exploits the data sparsity of the dense covariance matrix by compressing the off-diagonal tiles up to a user-defined accuracy threshold. The underlying linear algebra operations may then be carried out on this data compression formal, which may ultimately reduce the arithmetic complexity of the maximum likelihood estimation and the corresponding memory footprint. Performance results of TLR-based computations on shared and distributed-memory systems attain up to 13X and 5X speedups, respectively, compared to full accuracy simulations using synthetic and real datasets (up to 2M), while ensuring adequate prediction accuracy.

关键词： massively parallel algorithms machine learning algorithms applied computing mathematics and statistics maximum likelihood optimization geo-statistics applications

来源：评论

学校读者我要写书评

暂无评论

faimGraph: High Performance Management of Fully-Dynamic Graphs Under Tight Memory Constraints on the GPU

faimGraph: High Performance Management of Fully-Dynamic Grap...

引用

International Conference on High Performance Computing, Networking, Storage, and Analysis (SC)

作者： Winter, Martin Mlakar, Daniel Zayer, Rhaleb Seidel, Hans-Peter Steinberger, Markus Graz Univ Technol Graz Austria Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9781538683842

In this paper, we present a fully-dynamic graph data structure for the Graphics Processing Unit (GPU). It delivers high update rates while keeping a low memory footprint using autonomous memory management directly on the GPU. The data structure is fully-dynamic, allowing not only for edge but also vertex updates. Performing the memory management on the GPU allows for fast initialization times and efficient update procedures without additional intervention or reallocation procedures from the host. Our optimized approach performs initialization completely in parallel;up to 300x faster compared to previous work. It achieves up to 200 million edge updates per second for sorted and unsorted update batches;up to 30x faster than previous work. Furthermore, it can perform more than 300 million adjacency queries and millions of vertex updates per second. On account of efficient memory management techniques like a queuing approach, currently unused memory is reused later on by the framework, permitting the storage of tens of millions of vertices and hundreds of millions of edges in GPU memory. We evaluate algorithmic performance using a PageRank and a Static Triangle Counting (STC) implementation, demonstrating the suitability of the framework even for memory access intensive algorithms.

关键词： parallel programming GPU massively parallel algorithms Dynamic graphs

来源：评论

学校读者我要写书评

暂无评论

Comparing Binary-Swap algorithms for Odd Factors of Processes 8

Comparing Binary-Swap Algorithms for Odd Factors of Processe...

引用

8th IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV)

作者： Moreland, Kenneth Sandia Natl Labs Livermore CA 94550 USA

ISBN: (纸本)9781538668733

A key component of most large-scale rendering systems is a parallel image compositing algorithm, and the most commonly used compositing algorithms are binary swap and its variants. Although shown to be very efficient, one of the classic limitations of binary swap is that it only works on a number of processes that is a perfect power of 2. Multiple variations of binary swap have been independently introduced to overcome this limitation and handle process counts that have factors that are not 2. To date, few of these approaches have been directly compared against each other, making it unclear which approach is best. This paper presents a fresh implementation of each of these methods using a common software framework to make them directly comparable. These methods to run binary swap with odd factors are directly compared. The results show that some simple compositing approaches work as well or better than more complex algorithms that are more difficult to implement.

关键词： Computing methodologies Computer graphics Rendering Computing methodologies parallel computing methodologies parallel algorithms massively parallel algorithms

来源：评论

学校读者我要写书评

暂无评论

nbodykit: A Python Toolkit for Cosmology Simulations and Data Analysis on parallel HPC Systems 7

nbodykit: A Python Toolkit for Cosmology Simulations and Dat...

引用

7th Workshop on Python for High-Performance and Scientific Computing (PyHPC)

作者： Hand, Nick Feng, Yu Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781450351249

We present nbodykit, an open source, massively parallel Python toolkit for cosmology simulations and data analysis developed for high performance computing machines. We discuss the challenges encountered while designing parallel and scalable software in Python that still exploits the unique interactive tools provided by the Python stack. Using the mpi4py library, nbodykit implements a fully parallel, canonical set of algorithms in the field of large-scale structure cosmology and also includes a set of distributed data containers, insulated from the algorithms themselves. We use the dask library to provide a straightforward method for users to manipulate data without worrying about the costs of parallel IO operations. We take advantage of the readability of Python as an interpreted language by implementing nbodykit in pure Python, while ensuring high performance by relying on external, compiled libraries, optimized for specific tasks. We demonstrate the ease of use and performance capabilities of nbodykit with several real-world scenarios in the field of cosmology.

关键词： massively parallel algorithms massively parallel and high-performance simulations Designing software Statistical software

来源：评论

学校读者我要写书评

暂无评论

A Visual Model for Quality Driven Refinement of Global Illumination 17

A Visual Model for Quality Driven Refinement of Global Illum...

引用

ACM Symposium on Applied Perception (SAP)

作者： Guenther, Robert Guthe, Stefan Guthe, Michael Bayreuth Univ Univ Str 30 D-95440 Bayreuth Germany Tech Univ Darmstadt Rundeturmstr 12 D-64283 Darmstadt Germany

ISBN: (纸本)9781450351485

When rendering complex scenes using path-tracing methods, long processing times are required to calculate a sufficient number of samples for high quality results. In this paper, we propose a new method for priority sampling in path-tracing that exploits restrictions of the human visual system by recognizing whether an error is perceivable or not. We use the stationary wavelet transformation to efficiently calculate noise-contrasts in the image based on the standard error of the mean. We then use the Contrast Sensitivity Function and Contrast Masking of the Human Visual System to detect if an error is perceivable for any given pixel in the output image. Errors that can not be detected by a human observer are then ignored in further sampling steps, reducing the amount of samples calculated while producing the same perceived quality. This approach leads to a drastic reduction in the total number of samples required and therefore in total rendering time.

关键词： visual model global illumination massively parallel algorithms

来源：评论

学校读者我要写书评

暂无评论

A parallel HEVC Intra Prediction Algorithm for Heterogeneous CPU plus GPU Platforms

引用

IEEE TRANSACTIONS ON BROADCASTING 2016年第1期62卷 103-119页

作者： Radicke, Stefan Hahn, Jens-Uwe Wang, Qi Grecos, Christos Hsch Medien D-70569 Stuttgart Germany Univ West Scotland Paisley PA1 2BE Renfrew Scotland Sohar Univ Sohar 311 Oman

The high efficiency video coding standard provides excellent coding performance but is also very complex. Especially, the intra mode decision is very time-consuming due to the large number of available prediction modes and the flexible block partitioning scheme. In this paper, a highly parallel intra prediction algorithm for heterogeneous CPU+graphics processing unit (GPU) platforms is proposed, which accelerates the encoder dramatically. It is targeted toward high-quality high definition (HD) and ultra HD applications and utilizes prediction based on original samples (POSs), where the reference samples are generated from original pixels. This makes it possible to perform intramode prediction for all prediction blocks of a video frame concurrently. In addition, parallel-friendly cost functions are proposed which enable parallel rate distortion optimization with no synchronization overhead. A detailed statistical analysis of both POS and the proposed GPU intramethod is provided and the coding performance of the presented prototype is evaluated based on a large amount of experimental data. It is shown that the complexity of the intramode selection on the CPU is reduced by up to 78.03%. This translates to significant encoding time reductions of up to 64.52% for a single-threaded encoder and up to 94.82% in combination with wavefront parallel processing. In high bitrate ranges, average rate increases of only 2.11%-4.26% and 0.80%-2.34% are observed for the proposed high-speed and high-quality configurations, respectively. Furthermore, GPU intra is shown to be extremely efficient in lossless coding scenarios, where up to 53.37% time is saved with an average bitrate increase of only 0.55% among all test cases.

关键词： HEVC graphics processing unit (GPU) intra prediction mode decision massively parallel algorithms

来源：评论

学校读者我要写书评

暂无评论

GPU-based bees swarm optimization for association rules mining

引用

JOURNAL OF SUPERCOMPUTING 2015年第4期71卷 1318-1344页

作者： Djenouri, Youcef Bendjoudi, Ahcene Mehdi, Malika Nouali-Taboudjemat, Nadia Habbas, Zineb CERIST Res Ctr DTISI Algiers Algeria USTHB LSI Algiers Algeria Univ Lorraine Ile du Saulcy LCOMS F-57045 Metz France

Association rules mining (ARM) is a well-known combinatorial optimization problem aiming at extracting relevant rules from given large-scale datasets. According to the state of the art, the bio-inspired methods proved their efficiency by generating acceptable solutions in a reasonable time when dealing with small and medium size instances. Unfortunately, to cope with large instances such as the webdocs benchmark, these methods require more and more powerful processors and are time expensive. Nowadays, computing power is no longer a real issue. It can be provided by the power of emerging technologies such as graphics processing units (GPUs) that are massively multi-threaded processors. In this paper, we investigate the use of GPUs to speed up the computation. We propose two GPU-based bees swarm algorithms for association rules mining (single evaluation in GPU, SE-GPU and multiple evaluation in GPU, ME-GPU). SE-GPU aims at evaluating one rule at a time where each thread is associated with one transaction, whereas ME-GPU evaluates multiple rules in parallel on GPU where each thread is associated with several transactions. To validate our approaches, the two algorithms have been executed to solve well-known large ARM instances. Real experiments have been carried out on an Intel Xeon 64 bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that our approaches improve the execution time up to 100 over the sequential mono-core bees swarm optimization-ARM algorithm. Moreover, the proposed approaches have been compared with CPU multi-core ones (1-8 cores). The results show that they are faster than the multi-core versions whatever is the number of used cores.

关键词： Bees swarm optimization (BSO) Association rule mining (ARM) massively parallel algorithms GPU computing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：