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Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs

Optimizing Huffman Decoding for Error-Bounded Lossy Compress...

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international Symposium on parallel and distributed processing (IPDPS)

作者： Cody Rivera Sheng Di Jiannan Tian Xiaodong Yu Dingwen Tao Franck Cappello Department of Computer Science University of Alabama Tuscaloosa AL USA Mathematics and Computer Science Division Argonne National LaboratoryLemontILUSA School of Electrical Engineering and Computer Science Washington State University Pullman WA USA School of EECS Washington State University Pullman WA USA

More and more HPC applications require fast and effective compression techniques to handle large volumes of data in storage and transmission. Not only do these applications need to compress the data effectively during simulation, but they also need to perform decompression efficiently for post hoc analysis. SZ is an error-bounded lossy compressor for scientific data, and cuSZ is a version of SZ designed to take advantage of the GPU's power. At present, cuSZ's compression performance has been optimized significantly while its decompression still suffers considerably lower performance because of its sophisticated loss-less compression step-a customized Huffman decoding. In this work, we aim to significantly improve the Huffman decoding performance for cuSZ, thus improving the overall decompression performance in turn. To this end, we first investigate two state-of-the-art GPU Huffman decoders in depth. Then, we propose a deep architectural optimization for both algorithms. Specifically, we take full advantage of CUDA GPU architectures by using shared memory on decoding/writing phases, online tuning the amount of shared memory to use, improving memory access patterns, and reducing warp divergence. Finally, we evaluate our optimized decoders on an Nvidia V100 GPU using eight representative scientific datasets. Our new decoding solution obtains an average speedup of 3.64× over cuSZ's Huffman decoder and improves its overall decompression performance by 2.43× on average.

关键词： distributed processing Analytical models Memory management Graphics processing units distributed databases Throughput Data models

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Modeling Adversarial Strategies in GPU Side-Channel Attacks: A Game-Theoretic Perspective

Modeling Adversarial Strategies in GPU Side-Channel Attacks:...

引用

Computing for Sustainability and Intelligent Future (COMP-SIF), international conference on

作者： Nelson Lungu Suchismita Rout Bibhuti Bhusan Dash Sudhir Kumar Behera Utpal Chandra De Sudhansu Shekhar Patra Information Communication Technology National Institute of Public Administration Lusaka Zambia School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar Odisha India School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar Odisha India Department of Information Technology Dr. Ambedkar Memorial Institute of Information technology & Management Science (DAMITS) Rourkela Odisha India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

The use of Graphics processing Units (GPUs) in safety-critical applications is increasing; yet, their highly parallel designs provide weaknesses that facilitate side-channel attacks, enabling the theft of confidential information. Prior research has shown that time, contention, power, and access pattern side-channels are viable against GPU workloads. Nonetheless, their current protections remain insufficient to comprehensively safeguard real-world shader operations. A novel game-theoretical framework is proposed to model and evaluate the intricate dynamics between attackers and defenders in GPU side-channel assaults. The interactions are reformulated as a two-player, non-cooperative game, and the best tactics for both players have been established under various payment models and danger situations. Experimental results obtained from commercial GPUs corroborate that the suggested technique effectively represents hostile dynamics in reality, necessitating the development of resilient countermeasures. This project seeks to bridge the gap between theoretical security analysis and practical GPU protection methods. We provide a robust foundation for designing appropriate and secure GPU architectures for continuously changing side channel threadings. Human-centric approaches to driving work emphasise the need of considering human elements like as perception, judgement, and decision-making to effectively assess adversary techniques in cybersecurity. The game theoretical model offers a structured approach for forecasting probable assault vectors, assessing defence methods, and formulating effective counters customised to the adversary.

关键词： Computational modeling Decision making Graphics processing units Side-channel attacks Games Predictive models Vectors Protection Sustainable development Forecasting

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Cross-subject Drowsiness Recognition Model Based on the Multi-scale parallel Convolution of EEG Channels

Cross-subject Drowsiness Recognition Model Based on the Mult...

引用

IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Songquan Li Rongbo Zhu Jun Yuan Shaohua Wan College of Informatics Huazhong Agricultural University Wuhan China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Wuhan China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

Cross-subject electroencephalogram (EEG) drowsiness recognition is currently one of the most efficient methods. However, the traditional cross-subject approaches overlook the correlation between channel sub-features and effectively utilize the potential connection between frequency features and drowsiness, making it difficult to extract shared features from differentiated subjects. This paper proposes a novel deep learning model called CPCNN to address the challenging of cross-subject drowsiness recognition without calibration and improve accuracy. CPCNN obtains delta frequency band features related to drowsiness from sub-features in the channel dimension, effectively combining the advantages of delta frequency bands in EEG with the richness of channel features. To capture the common features of drowsiness, a channel slicing module is proposed, which divides the spatial and channel features extracted by the deep separable convolution module into multiple sub-features along the channel dimension. In addition, to further improve cross-subject recognition performance, a multi-scale parallel group convolution is designed to capture four key frequency sub-feature groups within the delta frequency band. The experimental results show that CPCNN boosts the recognition performance, significantly outperforms all strong baselines. On balanced and unbalanced public datasets, the average accuracy of CPCNN is 81.49% and 78.26%, respectively, surpassing the excellent baseline method by 3.14% and 0.56%.

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2021 5th international conference on Computer, Communication, and Signal processing, ICCCSP 2021

2021 5th International Conference on Computer, Communication...

引用

5th international conference on Computer, Communication, and Signal processing, ICCCSP 2021

ISBN: (纸本)9781665432771

The proceedings contain 53 papers. The topics discussed include: anomaly detection and improvement of clusters using enhanced k-means algorithm;parallel exploitation of 2D LiDAR simultaneous localization and mapping;computer aided mass detection in mammograms;stock data analysis with UIpath automation;an improved content based image retrieval system using unsupervised deep neural network and locality sensitive hashing;mood recognition in online sessions using machine learning in real-time;symptoms based early clinical diagnosis of COVID-19 cases using hybrid and ensemble machine learning techniques;IoT and machine learning based smart grid system;and dual-cavity backed substrate integrated waveguide slot antenna for 5G applications.

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval

Ranking on Heterogeneous Manifold for Multimodal Information...

引用

IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Zhenkun Jin Xingshi Wan Xin Nie Xinlei Zhou Yuanyuan Yi Gefei Zhou Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan China School of Journalism and Information Communication Huazhong University of Science and Technology Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR.

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IHFBF: A High-Performance Blockchain Framework for Improving Hyperledger Fabric Permissioned Chain

IHFBF: A High-Performance Blockchain Framework for Improving...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Min Xu Xiaoding Wang Hui Lin College of Computer and Cyber Security Fujian Normal University Fuzhou China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Permissioned blockchain frameworks typically employ efficient Byzantine fault-tolerant consensus protocols, making them appealing for the deployment of fast transaction applications among a large number of mutually distrustful participants. However, existing permissioned blockchain frameworks typically use sequential serial workflows to invoke the consensus protocol and execute transactions for the application, resulting in significantly lower performance for these applications when deployed in traditional systems. Therefore, a new permissioned blockchain framework is needed to improve transaction processing efficiency and enhance system performance for practical blockchain technology applications. We propose IHFBF (Improved Hyperledger Fabric Blockchain Framework), an improved permissioned blockchain framework that employs a predictive transaction sorting method by selecting a node within the consensus nodes to act as a sorter. This enables parallel execution of the consensus protocol and transactions, resulting in improved overall system performance. However, if the sorter is a malicious node, it can severely impact system performance. To address this, IHFBF uses a view-change method based on a deny-list approach, which effectively guides all participants and replaces or denies malicious participants. Compared to other three fast permissioned blockchain frameworks, IHFBF’s parallel workflow framework reduces latency and exhibits better throughput in the presence of malicious participants, resulting in efficient system performance.

关键词： Fault tolerance Privacy distributed ledger System performance Fault tolerant systems Throughput Fabrics

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Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

引用

IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Zhenkun Jin Shuai Huang Xin Nie Xinlei Zhou Yuanyuan Yi Gefei Zhou Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Journalism and Information Communication Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models.

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Routing of Strategic Passengers in a Transportation Station 1

引用

26th international conference, on Analytical and Stochastic Modelling techniques and applications (ASMTA)

作者： Logothetis, Dimitrios Economou, Antonis Natl & Kapodistrian Univ Athens Dept Math Athens 15784 Greece

ISBN: (数字)9783030918255

ISBN: (纸本)9783030918255;9783030918248

We study the routing decisions of passengers in a transportation station, where various types of facilities arrive with limited seating availability. The passengers' arrivals occur according to a Poisson process, the arriving instants of the transportation facilities form independent renewal processes and the seating availability at the successive visits of the facilities correspond to independent random variables, identically distributed for each facility. We analyze the strategic passenger behavior and derive the equilibrium strategies. We also discuss the associated social welfare optimization problem.

关键词： Queueing Strategic customers Equilibrium strategies Optimal policies Social welfare Transportation station parallel queues Routing Network

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RDMA-Based Apache Storm for High-Performance Stream Data processing 17th

RDMA-Based Apache Storm for High-Performance Stream Data P...

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17th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2020

作者： Zhang, Ziyu Liu, Zitan Jiang, Qingcai Wu, Zheng Chen, Junshi An, Hong University of Science and Technology of China Hefei China

ISBN: (纸本)9783030794774

Apache Storm is a scalable fault-tolerant distributed real-time stream-processing framework widely used in big data applications. For distributed data-sensitive applications, low-latency, high-throughput communication modules have a critical impact on overall system performance. Apache Storm currently uses Netty as its communication component, an asynchronous server/client framework based on TCP/IP protocol stack. The TCP/IP protocol stack has inherent performance flaws due to frequent memory copying and context switching. The Netty component not only limits the performance of the Storm but also increases the CPU load in the IPoIB (IP over InfiniBand) communication mode. In this paper, we introduce two new implementations for Apache Storm communication components with the help of RDMA technology. The performance evaluation on Mellanox QDR Cards (40 Gbps) shows that our implementations can achieve speedup up to 5 × compared with IPoIB and 10 × with 1 Gigabit Ethernet. Our implementations also significantly reduce the CPU load and increase the throughput of the system. © 2021, IFIP international Federation for Information processing.

关键词： Storms

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A Joint distributed Computation Offloading and Resource Allocation Scheme for MEC Networks

A Joint Distributed Computation Offloading and Resource Allo...

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international conference on Computer and Communications (ICCC)

作者： Enchang Sun Mengsi Li Xiaoxuan Dong Dongying Zhang Faculty of Information Technology Beijing University of Technology Beijing China Beijing Dublin International College Beijing University of Technology Beijing China Information Technology Support Center Beijing University of Technology Beijing China

Multi-access edge computing (MEC) technology is a potential solution to the conflict between resource-constrained unmanned aerial vehicles (UAVs) and various emerging computationally intensive, delay-sensitive applications of UAVs. In this paper, we study the joint optimization problem of distributed computation offloading and resource allocation in MEC-supported UAV application scenarios, where the task of each UAV can be divided and offloaded to multiple servers deployed in ground wireless access points (WAPs) for parallel processing. To enhance the quality of user experience (QoE) while avoiding excessive optimization, we propose a new user satisfaction function to evaluate the task processing level of MEC network, which respectively scores the actual task processing delay and energy consumption based on different criteria determined by task requirements. We aim to trade off delay and energy consumption by jointly optimizing server selection, computing resource allocation, transmission power control, and task allocation to achieve maximum user satisfaction. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem. To reduce the complexity of the joint optimization problem, it is decomposed into three sub-problems based on the divide-and-conquer approach: server computing resource allocation, transmission power control, and task offloading decision. We propose a three-layer intelligent optimization (TIO) algorithm, which employs the Lagrange multiplier method, the AdaDelta gradient descent method, and our proposed multivariate decoupling-particle swarm optimization (MD-PSO) algorithm to solve the above three sub-problems alternately for gradually approaching the maximum user satisfaction. The numerical results show that our proposed algorithm is significantly superior to the benchmark algorithms in terms of solving speed, accuracy, and stability.

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