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parallelizing image feature extraction algorithms on multi-core platforms

JOURNAL OF parallel AND distributed COMPUTING 2016年 92卷 1-14页

作者： Lu, Yunping Li, Yi Song, Bo Zhang, Weihua Chen, Haibo Peng, Lu Fudan Univ Software Sch Shanghai 200433 Peoples R China Fudan Univ Shanghai Key Lab Data Sci Shanghai 200433 Peoples R China Fudan Univ Parallel Proc Inst Shanghai 200433 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai 200030 Peoples R China Louisiana State Univ Div Elect & Comp Engn Baton Rouge LA 70803 USA

Currently, multimedia data has become one of the most important data types processed and transferred over the Internet. To extract useful information from a huge amount of such data, SIFT and SURF, as two most popular image feature extraction algorithms, have been widely used in many applications running on multi-core platforms. However, limited parallelism in existing designs makes it hard or impossible to apply them in many applications with real-time requirements. Therefore, it has become one of the major challenges to improve the processing speed of image feature extraction algorithms. In this paper, we first analyze the parallelism constraints in the algorithms, such as imbalanced workloads and indeterminate time distributions. Based on such analyses, we present an adaptive pipeline parallel scheme (AD-PIPE) to adjust the thread number in different stages according to their workloads dynamically, which achieves a balanced partition for constant input workloads. Furthermore, we also implement a power efficient version (AE-PIPE) for AD-PIPE through scheduling threads based on variable input workloads. Experimental results show that AD-PIPE achieves a speedup of 16.88X and 2033X respectively over SIFT and SURF on a 16-core machine. Moreover, AE-PIPE achieves up to 52.94% and 58.82% power saving with only 3% performance loss. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Image feature extraction SIFT SURF Adaptive pipeline Multi-core

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Transfer mechanism of data decryption authority in JointCloud computing 12

Transfer mechanism of data decryption authority in JointClou...

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12th IEEE International Conference on Joint Cloud Computing (IEEE JCC) / 9th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (IEEE MobileCloud)

作者： Xiang, Liangliang Qin, Ying Shi, Peichang Li, Rui Yang, Shangzhi Parallel & Distributed Proc Lab PDL Sci & Technol Changsha Peoples R China Natl Univ Def Technol Engn Res Ctr Natl Fundamental Software Coll Comp Changsha 410073 Peoples R China Tianjin Univ Tianjin 300350 Peoples R China

ISBN: (纸本)9781665434799

Data security sharing in the Jointcloud has always been a difficult problem for researchers. Some cloud data sharing solutions have a series of problems, such as high communication overhead, difficult key management, data tampering, single cloud failure, etc. We propose a data-sharing mechanism based on blockchain and threshold proxy re-encryption to solve the problems listed above. The proxy re-encryption algorithm we adopted is improved based on the Conditional Proxy Broadcast Re-Encryption. By combining the secret sharing scheme to realize trust decentralization. The re-encryption computing is delivered to multiple cloud service providers, which reduces the centralization problem caused by single cloud processing. At the same time, blockchain is used to manage metadata information and data access control rights to solve data loss and tampering problems. Experimental results show that our mechanism achieves low re-encryption computing overhead.

关键词： data sharing JointCloud proxy reencryption blockchain

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Semi-supervised Non-negative Local Coordinate Factorization 1

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22nd International Conference on Neural Information Processing (ICONIP)

作者： Zhou, Cherong Zhang, Xiang Guan, Naiyang Huang, Xuhui Luo, Zhigang Sci & Technol Parallel & Distributed Proc Lab Changsha Hunan Peoples R China Coll Comp Inst Software Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Dept Comp Sci & Technol Changsha 410073 Hunan Peoples R China

ISBN: (数字)9783319265353

ISBN: (纸本)9783319265353;9783319265346

Non-negative matrix factorization (NMF) is a popular matrix decomposition technique that has attracted extensive attentions from data mining community. However, NMF suffers from the following deficiencies: (1) it is non-trivial to guarantee the representation of the data points to be sparse, and (2) NMF often achieves unsatisfactory clustering results because it completely neglects the labels of the dataset. Thus, this paper proposes a semi-supervised non-negative local coordinate factorization (SNLCF) to overcome the above deficiencies. Particularly, SNLCF induces the sparse coefficients by imposing the local coordinate constraint and propagates the labels of the labeled data to the unlabeled ones by indicating the coefficients of the labeled examples to be the class indicator. Benefit from the labeled data, SNLCF can boost NMF in clustering the unlabeled data. Experimental results on UCI datasets and two popular face image datasets suggest that SNLCF outperforms the representative methods in terms of both average accuracy and average normalized mutual information.

关键词： Non-negative matrix factorization Local coordinate coding Semi-supervised learning

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Key-Based Transaction Reordering: An Optimized Approach for Concurrency Control in Hyperledger Fabric 1

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23rd International Conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Ma, Haoliang Shi, Peichang Fu, Xiang Yi, Guodong Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci Key Lab Software Engn Complex Syst Changsha 410073 Peoples R China Xiangjiang Lab Changsha 410073 Peoples R China

ISBN: (数字)9789819708628

ISBN: (纸本)9789819708611;9789819708628

As blockchain technology garners increased adoption, permissioned blockchains like Hyperledger Fabric emerge as a popular blockchain system for developing scalable decentralized applications. Nonetheless, parallel execution in Fabric leads to concurrent conflicting transactions attempting to read and write the same key in the ledger simultaneously. Such conflicts necessitate the abortion of transactions, thereby impacting performance. The mainstream solution involves constructing a conflict graph to reorder the transactions, thereby reducing the abort rate. However, it experiences considerable overhead during scenarios with a large volume of transactions or high data contention due to capture dependencies between each transaction. Therefore, one critical problem is how to efficiently order conflicting transactions during the ordering phase. In this paper, we introduce an optimized reordering algorithm designed for efficient concurrency control. Initially, we leverage key dependency instead of transaction dependency to build a conflict graph that considers read/write units as vertices and intra-transaction dependency as edges. Subsequently, a key sorting algorithm generates a serializable transaction order for validation. Our empirical results indicate that the proposed key-based reordering method diminishes transaction latency by 36.3% and considerably reduces system memory costs while maintaining a low abort rate compared to benchmark methods.

关键词： Hyperledger Fabric Reordering Algorithm Concurrency Control Transaction Conflicts

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Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need 21

Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Nee...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Ma, Zhengyi Dou, Zhicheng Xu, Wei Zhang, Xinyu Jiang, Hao Cao, Zhao Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Huawei Distributed & Parallel Software Lab Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450384469

Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especially in the ad-hoc retrieval area. Existing pre-training approaches tailored for IR tried to incorporate weak supervised signals, such as query-likelihood based sampling, to construct pseudo query-document pairs from the raw textual corpus. However, these signals rely heavily on the sampling method. For example, the query likelihood model may lead to much noise in the constructed pre-training data. In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval. Since the anchor texts are created by webmasters and can usually summarize the target document, it can help to build more accurate and reliable pre-training samples than a specific algorithm. Considering different views of the downstream ad-hoc retrieval, we devise four pre-training tasks based on the hyperlinks. We then pre-train the Transformer model to predict the pair-wise preference, jointly with the Masked Language Model objective. Experimental results on two large-scale ad-hoc retrieval datasets show the significant improvement of our model compared with the existing methods.

关键词： Ad-hoc Retrieval Pre-training Hyperlinks Anchor Texts

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Quantization improvements for LU Decomposition to Solve Linear Equations 19

Quantization improvements for LU Decomposition to Solve Line...

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19th IEEE International Symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Chen, Lin He, Kang Jiang, Hao Barrio, Roberto Liu, Jie Li, Tiejun Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Lab Software Engn Complex Syst Changsha Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China Univ Zaragoza Dept Appl Math E-50009 Zaragoza Spain

ISBN: (纸本)9781665435741

Quantization is an effective method for compressing Deep Neural Networks. Now, it is considered to accelerate the traditional HPC applications. In this article, we present a quantization method (q8gemm) using integer 8-bits(INT8) for matrix multiplication, which is implemented by block techniques to overcome the overflow problem. We choose different scales for one matrix's each row and those for the other matrix's each column to keep the numbers in range. With the intel advanced matrix extension cblas_gemm_s8u8s32, our q8gemm can accelerate panel-matrix multiplication in LU factorization. Furthermore, we propose a new mixed-precision solver (q8gesv(1)) for the linear equations. Using q8gemm in INT8, q8gesv modifies LU factorization in FP32 and then utilizes GMRES iterative refinement in FP 64 to bring the solution back to 64 bits accuracy. Numerical tests show that on two Intel CPU platforms with avx2 and avx512, q8gemm achieves a speedup of 1.313-1.621 over cblas_sgemm of MKL. The execution time of cblas_gemm_s8u8s32 makes up 86.78%-0.85% of the q8gemm. By means of q8gemm, our q8gesv can achieve a speedup of 1.176-1.493 over the optimized Higham's mixed-precision algorithm utilized in the HPL-AI benchmark and a speedup of 1.1-1.459 over the LAPACKE_dsgesv function from MKL LAPACK. Meanwhile, q8gesv runs up to 2.859X faster on Intel(R) Core(TM) i5-10600KF and to 2.224X faster on Intel(R) Xeon(R) Gold 6130 than the LAPACKE_dgesv function from MKL LAPACK.

关键词： GMRES-IR LU Factorization Matrix Multiplication Mixed-precision Quantization

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VarCatcher: A Framework for Tackling Performance Variability of parallel Workloads on Multi-Core

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2017年第4期28卷 1215-1228页

作者： Zhang, Weihua Ji, Xiaofeng Song, Bo Yu, Shiqiang Chen, Haibo Li, Tao Yew, Pen-Chung Zhao, Wenyun Fudan Univ Shanghai Key Lab Data Sci Software Sch Shanghai 200433 Peoples R China Fudan Univ Parallel Proc Inst Shanghai 200433 Peoples R China Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai 200240 Peoples R China Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai 200433 Peoples R China

The non-deterministic nature of multi-threaded workloads running on multi-core platforms often leads to notable performance variability from run to run. Such variability makes experimental results prone to misinterpretations or misguided claims. To deal with such variability, statistical inference methods are usually used to summarize the experimental results with certain confidence levels by running the experiments or measurements a large number of times. However, such statistical results are often too vague or too simplistic. They are not sufficient to help users understand the causes of such variability, and allow more in-depth analysis on the results or reproduce the results for validation during design space exploration. To allow better analyzability and reproducibility, we propose a framework to tackle such variability, called VarCatcher. The key to VarCatcher is to characterize a parallel execution using parallel Characteristics Vector (PCV). A clustering-based approach is then used to group runs with similar execution characteristics that can later be used to analyze results in-depth, to customize different evaluation strategies, reproduce the result for variability, to determine the impact of features, or to assist performance diagnosis. We have built a prototype of VarCatcher that includes a user-level toolset for runtime monitoring and measurements using the Intel Processor Trace feature on commodity Intel processors as well as an architecture extension with very low runtime overheads (around 3 and 0.01 percent accordingly). Several case studies confirm that VarCatcher enables several appealing features such as in-depth result analysis, customized evaluation strategies, and reproducibility.

关键词： Variability parallel application evaluation multi core

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FSS: A Flexible Scaling Scheme for Blockchain Based on Stale Block Rate 13

FSS: A Flexible Scaling Scheme for Blockchain Based on Stale...

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13th IEEE International Conference on Joint Cloud Computing (JCC)

作者： Chen, Ming Shi, Peichang Fu, Xiang Jiang, Feng Gao, Fei Ma, Penghui Kong, Jinzhu Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci Key Lab Software Engn Complex Syst Changsha 410073 Peoples R China Kylin Software Co Ltd Changsha 410073 Peoples R China Zhejiang Lab Hangzhou 311100 Peoples R China

ISBN: (数字)9781665462853

ISBN: (纸本)9781665462853

In blockchain, there has long been a contradiction between the limited ability and the uncertain requirements of processing transactions, which seriously restricts the practical application of blockchain. Therefore, how to improve the scalability of blockchain has become an urgent issue to be solved. Some existing works have achieved blockchain expansion through increasing the upper limit of block size permanently, which makes the trade-off of the "Mundellian Trilemma" in blockchain (i.e. a blockchain system cannot be optimal in all the three dimensions of scalability, security and decentralization at the same time) fixed and thus not adapted to the dynamic environment. In this paper, we propose FSS, a flexible scaling scheme for blockchain based on stale block rate, which dynamically adjusts the upper limit of block size according to the stale block rate, not only expanding the blockchain when allowed, but also shrinking it when necessary. Experimental results indicate that FSS can reasonably improve the scalability of blockchain with required stale block rate.

关键词： blockchain scalability stale block rate

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain 20

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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20th IEEE Int Symposium on parallel and distributed Processing with Applicat / 15th IEEE Int Conf on Social Comp and Networking / 12th IEEE Int Conf on Big Data and Cloud Comp / 12th IEEE Int Conf on Sustainable Comp and Commun

作者： Chen, Ming Shi, Peichang Fu, Xiang Jiang, Feng Gao, Fei Ma, Penghui Kong, Jinzhu Natl Univ Def Mchnol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci Key Lab Software Engn Complex Syst Changsha 410073 Peoples R China Kylin Software Co LTD Changsha 410073 Peoples R China Zhejiang Lab Hangzhou 311100 Peoples R China

ISBN: (纸本)9781665464970

In JointCloud Computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud Computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud Computing, and combines the "double encryptions" and "threshold encryption" technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： blockchain privacy-preserving supervision

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Cloud workload prediction based on workflow execution time discrepancies

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS software TOOLS AND APPLICATIONS 2019年第3期22卷 737-755页

作者： Kecskemeti, Gabor Nemeth, Zsolt Kertesz, Attila Ranjan, Rajiv Liverpool John Moores Univ Dept Comp Sci Liverpool L3 3AF Merseyside England MTA SZTAKI Lab Parallel & Distributed Syst H-1111 Budapest Hungary Univ Szeged Software Engn Dept H-6720 Szeged Hungary Newcastle Univ Sch Comp Newcastle Upon Tyne NE4 5TG Tyne & Wear England

Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase the reliability or performance of their applications, they would need solutions to detect behavioural changes in the underlying system. Existing runtime solutions for such purposes offer limited capabilities as they are mostly restricted to revealing weekly or yearly behavioural periodicity in the infrastructure. This article proposes a technique for predicting generic background workload by means of simulations that are capable of providing additional knowledge of the underlying private cloud systems in order to support activities like cloud orchestration or workflow enactment. Our technique uses long-running scientific workflows and their behaviour discrepancies and tries to replicate these in a simulated cloud with known (trace-based) workloads. We argue that the better we can mimic the current discrepancies the better we can tell expected workloads in the near future on the real life cloud. We evaluated the proposed prediction approach with a biochemical application on both real and simulated cloud infrastructures. The proposed algorithm has shown to produce significantly (similar to 20%) better workload predictions for the future of simulated clouds than random workload selection.

关键词： Workload prediction Cloud computing Simulation Scientific workflow

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