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12th International Conference on Algorithms and Architectures for parallel Processing, ICA3PP 2012

作者： Jia, Haipeng Zhang, Yunquan Long, Guoping Yan, Shengen Lab. of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences China College of Information Science and Engineering Ocean University of China China State Key Laboratory of Computing Science Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences China

ISBN: (纸本)9783642330773

It is challenging to optimize GPU kernels because this progress requires deep technical knowledge of the underlying hardware. Modern GPU architectures are becoming more and more diversified, which further exacerbates the already difficult problem of performance optimization. This paper presents an insightful performance tuning chain for GPUs. The goal is to help non-expert programmers with limited knowledge of GPU architectures implement high performance GPU kernels directly. We achieve it by providing performance information to identify GPU program performance bottlenecks and decide which optimization methods should be adopted, so as to facilitate the best match between algorithm features and underlying hardware characteristics. To demonstrate the usage of tuning chain, we optimize three representative GPU kernels with different compute intensity: Matrix Transpose, Laplace Transform and Integral on both NVIDIA and AMD GPUs. Experimental results demonstrate that under the guidance of our tuning chain, performance of those kernels achieves 7.8~42.4 times speedup compared to their naïve implementations on both NVIDIA and AMD GPU platforms. © 2012 Springer-Verlag.

关键词： Chains

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Crowd intelligence in AI 2.0 era

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Frontiers of Information Technology & Electronic Engineering 2017年第1期18卷 15-43页

作者： Wei LI Wen-jun WU Huai-min WANG Xue-qi CHENG Hua-jun CHEN Zhi-hua ZHOU Rong DING State Key Laboratory of Software Development Beihang University National Laboratory for Parallel and Distributed Processing College of ComputerNational University of Defense Technology Institute of Computing Technology Chinese Academy of Sciences College of Computer Science and Technology Zhejiang University National Key Laboratory for Novel Software Technology Nanjing University

The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence(AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-tooffline(O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence.

关键词： Crowd intelligence Artificial intelligence 2.0 Crowdsourcing Human computation

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Optimizing Batched Small Matrix Multiplication on Multi-core DSP Architecture 22

Optimizing Batched Small Matrix Multiplication on Multi-core...

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22nd IEEE International Symposium on parallel and Distributed Processing with Applications, ISPA 2024

作者： Zuo, Xiaohan Xiao, Chunhua Wang, Qinglin Shi, Chen Chongqing University College of Computer Science Chongqing China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha410073 China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment Changsha410073 China

ISBN: (纸本)9798331509712

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many High-Performance computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x. © 2024 IEEE.

关键词： Network security

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parallelized Race Detection Based on GPU Architecture

Parallelized Race Detection Based on GPU Architecture

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10th Annual Conference of Advanced Computer Architecture, ACA 2014

作者： Dai, Zhuofang Zhang, Zheng Wang, Haojun Li, Yi Zhang, Weihua Software School Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Parallel Processing Institute Fudan University Shanghai 201203 China State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou 450001 China

ISBN: (纸本)9783662444900

In order to harness abundant hardware resources, parallel programming has become a necessity in multicore era. However, parallel programs are prone to concurrency bugs, especially data races. Even worse, current software tools always suffer from both large runtime overheads and poor scalability, while most of hardware supports for race detection are not available in parallel programming. Therefore, it has been a challenge that how to introduce a practical and fast race detection tools. Nowadays, GPUs with massive parallel computation resources have become one of the most popular hardware platforms. Hence, the prevalence of GPU architectures has opened an opportunity of accelerating data race detection. In this paper, we first have a deeply analysis on data race detection algorithms like happens-before and observe that these algorithms have very good computation and data parallelism. Based on the observation, we propose Grace, a software approach that leverages massive parallelism computation units of GPU architectures to accelerate data race detection. Grace deploys detection, the most computation intensive workload, on GPU to fully utilize the computation resource in GPU. Moreover, Grace leverages coarse-grained pipeline parallelism and data parallelism through exploiting the computation resource in multi-core CPUs to further improve performance. Experimental results show that Grace is fast and scalable. It achieves over 80x speedup compared to the sequential version even under a 128-thread configuration. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Graphics processing unit

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GB-RC4: Effective brute force attacks on RC4 algorithm using GPU 7

GB-RC4: Effective brute force attacks on RC4 algorithm using...

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7th International Green and Sustainable computing Conference, IGSC 2016

作者： Xue, Pei Li, Tao Dong, Han Liu, Chunbo Ma, Wenjing Pei, Songwen College of Computer and Control Engineering Nankai University Tianjin300071 China Information Security Evaluation Center Civil Aviation University of China Tianjin300300 China Laboratory of Parallel Software and Computational Science State Key Laboratory of Computing Science Institute of Software CAS Beijing China Shanghai Key Lab of Modern Optical Systems Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781509051175

Encryption algorithms are applied to a variety of fields and the security of encryption algorithms depends heavily on the computational infeasibility of exhaustive key-space search. RC4 algorithm has an extensive application for stream encryption, however, the disadvantages of traditional RC4 serial algorithm are large computational quantity and slow computation speed, which means a great challenge for throughput of computing system. Graphics processors recently have been widely used as accelerators for high throughput computing. In this paper, GPU is used to improve the efficiency and performance of attacking RC4 algorithm. First, we revised RC4 algorithm to make it easier to parallelize on a GPU. Then we take advantage of constant memory and shared memory in GPU to search the key space of RC4 algorithm efficiently. Besides, we solved the bank conflict problem in shared memory to further improve the performance. Our approach was evaluated in the whole key space of 40-bit, and the results show it can complete searching of the whole key space within 12.8 hours. © 2016 IEEE.

关键词： Graphics processing unit

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Efficient bayesian maximum margin multiple kernel learning 1

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15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016

作者： Du, Changying Du, Changde Long, Guoping Jin, Xin Li, Yucheng Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing100190 China Institute of Computing Technology CAS Beijing100190 China Research Center for Brain-inspired Intelligence Institute of Automation Chinese Academy of Sciences Beijing100190 China Central Software Institute Huawei Technologies Co. Ltd Beijing100085 China

ISBN: (数字)9783319461281

ISBN: (纸本)9783319461274

Multiple Kernel Learning (MKL) suffers from slow learning speed and poor generalization ability. Existing methods seldom address these problems well simultaneously. In this paper, by defining a multiclass (pseudo-) likelihood function that accounts for the margin loss for kernelized classification, we develop a robust Bayesian maximum margin MKL framework with Dirichlet and the three parameter Beta normal priors imposed on the kernel and sample combination weights respectively. For inference, we exploit the data augmentation idea and devise an efficient MCMC algorithm in the augmented variable space, employing the Riemann manifold Hamiltonian Monte Carlo technique to sample from the conditional posterior of kernel weights, and making use of local conjugacy for all other variables. Such geometry and conjugacy based posterior sampling leads to very fast mixing rate and scales linearly with the number of kernels used. Extensive experiments on classification tasks validate the superiority of the proposed method in both efficacy and efficiency. © Springer International Publishing AG 2016.

关键词： Monte Carlo methods

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Recent Advances in the Message Passing Interface 1

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丛书名： Lecture Notes in Computer science

1000年

作者： Rainer Keller Edgar Gabriel Michael Resch Jack Dongarra

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Research on Hybrid-Cloud-Based User Privacy Protection of O2O Platform 13th

Research on Hybrid-Cloud-Based User Privacy Protection of O2...

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Chunhui Piao Yang Zuo Changyou Zhang School of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang China Laboratory of Parallel Software and Computing Science Institute of software Chinese Academy of Scienses Beijing China

ISBN: (纸本)9781509061204

O2O (Online-to-Offline) is a new rapidly growing e-commerce model. It combines offline physical business with online network marketing and electronic payment to provide convenient and efficient localized services for consumers. However, user privacy concerns have become one of the challenging issues affecting further O2O e-commerce applications. Taking the catering takeaway O2O e-commerce as an example, the user privacy and cost issues occurring in the big data era are presented through analyzing of the service model of current mainstream catering takeaway platforms. A hybrid-cloud-based privacy preserving application framework for catering takeaway O2O e-commerce is proposed. The similarities between users are calculated using SimRank model and user-restaurant relationship diagram. Considering their personalized privacy requirements, the users are grouped and anonymized. Even if the attacker obtains all the transaction information stored on the public cloud, the transaction relationship of a specific order cannot be uniquely identified. The feasibility and effectiveness of the proposed user privacy protection method is illustrated by an example.

关键词： Cloud computing Privacy Business Data privacy Big data Analytical models Security

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parallel computing for large-scale author name disambiguation in medline 21

Parallel computing for large-scale author name disambiguatio...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Tang, Anyao Wu, Chengkun Liu, Jie Wang, Wei Yang, Xi Xing, Yuting Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410073 China State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781728120584

Author name disambiguation (AND) is an important task in the field of scientific data mining. It has become a great challenge with the rapid growth of academic digital libraries. The task of AND for a large number of authors is computationally intensive. In particular, an author's name in MEDLINE is represented by full last name and initials, like 'Zhang S', which leads to a lot of identical strings that actually represent different names. In this paper, we proposed an efficient algorithm for parallel AND computation. The proposed algorithm mainly addresses the load balancing issue across many computing nodes. It involves the following strategies:(1) Author-based load balancing, which splits the computation load for each core by author name labels. (2) Matrix-based strategy, which calculates the pairwise similarity between publications and saves them in a matrix globally shared by all processes. Then group them by width-first search. We combine the above two strategies, the second of which is used to calculate authors with a large number of documents, and the other authors apply the first. We constructed a publications database written by Chinese authors from MEDLINE, the biggest public database for biomedical literature (abstracts). For benchmark testing, we experimented our algorithm with a dataset of 1 million publications on the Tianhe-2A supercomputer. Firstly, we trained an AND classifier that can achieve 98.1% of F1. The serial computation time is estimated to be approximately 246 hours, while the parallel execution time is approximately 66 hours in the case of four cores on a single node (with a speedup of 3.7x). Finally, we reduced the total parallel computing time of 1 million documents to about 2 hours and achieved 65.8% of parallelism efficiency using 200 cores on 90 nodes. © 2019 IEEE.

关键词： Matrix algebra

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Implementing the jacobi algorithm for solving eigenvalues of symmetric matrices with CUDA

Implementing the jacobi algorithm for solving eigenvalues of...

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2012 IEEE 7th International Conference on Networking, Architecture and Storage, NAS 2012

作者： Wang, Tao Guo, Longjiang Li, Guilin Li, Jinbao Wang, Renda Ren, Meirui He, Jing School of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin 150080 China Software School of Xiamen University Xiamen Fujian 361005 China Department of Computer Science Kennesaw State University Kennesaw GA 30144 United States

ISBN: (纸本)9780769547220

Solving the eigenvalues of matrices is an open problem which is often related to scientific computation. With the increasing of the order of matrices, traditional sequential algorithms are unable to meet the needs for the calculation time. Although people can use cluster systems in a short time to solve the eigenvalues of large-scale matrices, it will bring an increase in equipment costs and power consumption. This paper proposes a parallel algorithm named Jacobi on gpu which is implemented by CUDA (Computer Unified Device Architecture) on GPU (Graphic Process Unit) to solve the eigenvalues of symmetric matrices. In our experimental environment, we have Intel Core i5-760 quad-core CPU, NVIDIA GeForce GTX460 card, and Win7 64-bit operating system. When the size of matrix is 10240×10240, the number of iterations is 10000 times, the speedup ratio is 13.71. As the size of matrices increase, the speedup ratio increases correspondingly. Moreover, as the number of iterations increases, the speedup ratio is very stable. When the size of matrix is 8192×8192, the number of iterations are 1000, 2000, 4000, 8000 and 16000 respectively, the standard deviation of the speedup ratio is 0.1161. The experimental results show that the Jacobi on gpu algorithm can save more running time than traditional sequential algorithms and the speedup ratio is 3.02∼13.71. Therefore, the computing time of traditional sequential algorithms to solve the eigenvalues of matrices is reduced significantly. © 2012 IEEE.

关键词： Iterative methods

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