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检索条件"任意字段=3rd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 1997"

共 79 条记录，以下是21-30 订阅

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CMMR: A Composite Multidimensional Models Robustness Evaluation Framework for Deep Learning 1

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23rd international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Liu, Wanyi Zhang, Shigeng Wang, Weiping Zhang, Jian Liu, Xuan Cent South Univ Sch Comp Sci Changsha 410083 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410002 Peoples R China Sci & Technol Parallel & Distributed Proc Lab PDL Changsha 410073 Peoples R China

ISBN: (数字)9789819708086

ISBN: (纸本)9789819708079;9789819708086

Accurately evaluating the defense models against adversarial examples has been proven to be a challenging task. We have recognized the limitations of mainstream evaluation standards, which fail to account for the discrepancies in evaluation results arising from different adversarial attack methods, experimental setups, and metrics sets. To address these disparities, we propose the Composite Multidimensional Model Robustness (CMMR) evaluation framework, which integrates three evaluation dimensions: attack methods, experimental settings, and metrics sets. By comprehensively evaluating the model's robustness across these dimensions, we aim to effectively mitigate the aforementioned variations. Furthermore, the CMMR framework allows evaluators to flexibly define their own options for each evaluation dimension to meet their specific requirements. We provide practical examples to demonstrate how the CMMR framework can be utilized to assess the performance of models in enhancing robustness through various approaches. The reliability of our methodology is assessed through both practical examinations and theoretical validations. The experimental results demonstrate the excellent reliability of the CMMR framework and its significant reduction of variations encountered in evaluating model robustness in practical scenarios.

关键词： Robustness evaluation Adversarial attacks Adversarial machine learning

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Optimizing CSR-Based SpMV on a New MIMD Architecture Pezy-SC3s 1

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23rd international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Guo, Jihu Liu, Jie Wang, Qinglin Zhu, Xiaoxiong Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China

ISBN: (数字)9789819708017

ISBN: (纸本)9789819708000;9789819708017

Sparse matrix-vector multiplication (SpMV) is extensively used in scientific computing and often accounts for a significant portion of the overall computational overhead. Therefore, improving the performance of SpMV is crucial. However, sparse matrices exhibit a sporadic and irregular distribution of non-zero elements, resulting in workload imbalance among threads and challenges in vectorization. To address these issues, numerous efforts have focused on optimizing SpMV based on the hardware characteristics of computing platforms. In this paper, we present an optimization on CSR-Based SpMV, since the CSR format is the most widely used and supported by various high-performance sparse computing libraries, on a novel MIMD computing platform Pezy-SC3s. Based on the hardware characteristics of Pezy-SC3s, we tackle poor data locality, workload imbalance, and vectorization challenges in CSRBased SpMV by employing matrix chunking, applying Atomic Cache for workload scheduling, and utilizing SIMD instructions during performing SpMV. As the first study to investigate SpMV optimization on Pezy-SC3s, we evaluate the performance of our work by comparing it with the CSR-Based SpMV and SpMV provided by Nvidia's CuSparse. Through experiments conducted on 2092 matrices obtained from SuiteSparse, we demonstrate that our optimization achieves a maximum speedup ratio of x17.63 and an average of x1.56 over CSR-Based SpMV and an average bandwidth utilization of 35.22% for large-scale matrices (nnz >= 10(6)) compared with 36.17% obtained using CuSparse. These results demonstrate that our optimization effectively harnesses the hardware resources of Pezy-SC3s, leading to improved performance of CSR-Based SpMV.

关键词： SpMV Optimization CSR-Based SpMV Pezy-SC3s

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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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AIFR: Face Recognition Research Based on Age Factor Characteristics 23rd

AIFR: Face Recognition Research Based on Age Factor Characte...

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23rd international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Zhu, Biaokai Zhang, Zhaojie Jia, Yupeng Hu, Xinru Shen, Yurong Bai, Manwen Song, Jie Li, Ping Liu, Sanman Li, Feng Li, Deng-ao Shanxi Police Coll 799 Qingdong Rd Taiyuan 030401 Shanxi Peoples R China Intelligent Policing Key Lab Sichuan Prov Luzhou 646000 Sichuan Peoples R China Anhui Univ Hefei 230601 Anhui Peoples R China Taiyuan Univ Technol 79 West St Yingze Taiyuan 030024 Peoples R China

ISBN: (纸本)9789819708611;9789819708628

As we all know, the facial appearance change caused by age change leads to the low accuracy of face recognition, which is a significant difficulty in cross-age face recognition tasks. How to overcome the age problem, face feature extraction has become the key. This paper proposes a cross-age face recognition method based on deep learning. This method uses the Arcface loss function to realize cross-age face recognition by improving the residual neural network, combining it with the attention mechanism. Firstly, the face image is enhanced, and the Retinaface algorithm detects the face to complete the look preprocessing. Then the preprocessed face image is extracted by this model to achieve the purpose of cross-age face recognition. In addition, due to the lack of Asian face datasets in public data sets, this paper makes a self-use dataset based on the public data sets. It conducts experiments with FG-NET and CALFW datasets to confirm the universality of this method. The effect of the experimental training set reaches 92.67%, which makes other progress in cross-age face recognition.

关键词： Deep learning Retinaface Arcface loss AIFR (Age-invariant face recognition)

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Performance Evaluation of Spark, Ray and MPI: A Case Study on Long Read Alignment Algorithm 1

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23rd international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Ran, Kun Cui, Yingbo Wang, Zihang Peng, Shaoliang Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Natl Univ Def Technol Sch Comp Changsha 410073 Hunan Peoples R China

ISBN: (数字)9789819707980

ISBN: (纸本)9789819707973;9789819707980

The utilization of large-scale datasets in various fields is increasing due to the advancement of big data technology. Due to limited computing resources, traditional serial frameworks are no longer efficient in processing such massive data. Furthermore, as Moore's Law gradually loses its effect, improving program performance from the hardware level becomes increasingly challenging. Consequently, numerous parallel frameworks with distinct features and architectures have emerged, and selecting an appropriate one can enhance researchers' performance across various tasks. This paper evaluates three prominent parallel frameworks-Spark, Ray, and MPI-and employs minimap2, a third-generation CPU-based sequence alignment tool, as the benchmark program. The experimental results are discussed comprehensively. To evaluate the three frameworks, we devised a parallel algorithm for minimap2 and implemented its parallel versions using Ray and MPI, respectively. Furthermore, we selected IMOS as the Spark version of minimap2. The experiments involved six real datasets and one simulated dataset to evaluate and compare speedup, efficiency, throughput, scalability, peak memory, latency, and load balance. The findings demonstrate that MPI outperforms Apache Spark and Ray in terms of achieving a maximum speedup of 104.019, 81.3% efficiency, 33.510 MB/s throughput, the lowest latency, and better load balance. However, MPI exhibits poor fault tolerance. Apache Spark demonstrated the second-best performance, with a speedup of 88.937, efficiency of 69.5%, throughput of 29.546 MB/s, low latency, and the best load balance. Furthermore, it exhibited good fault tolerance and benefited from a mature ecosystem. Ray achieves a speedup of 76.828, efficiency of 60.0%, and throughput of 25.009 MB/s. However, it experiences high latency fluctuations, possesses less load balance compared to the previous two frameworks, and maintains good fault tolerance. The source code and a comprehensive user manual f

关键词： parallel processing Ray Apache Spark MPI sequence alignment

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Multi-level PWB and PWC for Reducing TLB Miss Overheads on GPUs 21st

Multi-level PWB and PWC for Reducing TLB Miss Overheads on G...

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21st international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Lin, Yang Zhang, Dunbo Jia, Chaoyang Wang, Qiong Shen, Li Natl Univ Def Technol Deya Rd 109 Changsha Hunan Peoples R China

ISBN: (纸本)9783030953881;9783030953874

Nowadays, GPU is becoming popular across a broad range of domains. To provide virtual memory support for most applications at present, GPU introduces the address translation process. However, many applications show an irregular memory access pattern, i.e. accesses are poor structured and often data dependent, which makes performance worse especially with virtual-to-physical address translations. GPU memory management unit (MMU) adopts caching units, e.g. page walk buffer (PWB) and page walk cache (PWC), and schedule strategies to accelerate the address translations after TLB misses. However, limited by the linear table structure of traditional PWB and PWC, they hold too many redundant information, which further limits the performance of irregular applications. Although nonlinear structure can eliminate the redundancy, it requires sequential look-up on PWB and PWC, which brings greater performance loss. In this paper, we propose the multilevel PWB and PWC structure, which features the multi-level structure for eliminating the redundancy in traditional structure and the co-design of PWB and PWC for enabling parallel look-up. Besides, we design four corresponding address translation processes to ensure the efficiency of the new structure. We evaluate our design with real-world benchmarks under GPGPU-Sim simulator. Results show that our design achieves 42.6% IPC improvement with 35.1% less space overheads.

关键词： GPU Address translation IOMMU TLB Page walk cache

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parallel Belief Propagation Optimized by Coloring on GPUs 20th

Parallel Belief Propagation Optimized by Coloring on GPUs

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20th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Hou, Junteng Si, Chengxiang Wang, Shupeng Wu, Guangjun Zhang, Lei Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

Belief propagation (BP) is a message passing algorithm that infers over probabilistic graphical models. Its main computational workload, messages update, is suitable for GPU's massively parallel architecture. However, the efficiency of fully parallel BP is low, and traditional algorithms implemented on GPUs occupy amount of computing and memory resources. In this paper, we propose several GPU-friendly BP algorithms optimized by coloring. Color Wave (CW) algorithm performs multi-step coloring on residuals of non-convergent vertices to quickly obtain multiple disjoint partitions and vertices with the largest residuals in each partition, and then updates batches of messages in a fixed order. These operations are all suitable for parallelization and require little additional memory. To save time in each iteration, the Color Extract (CE) algorithm only update messages on edges with the largest residuals among all adjacent edges. The Random Drop (rd) algorithm steadily increases the convergence degree by progressively reducing the messages update ratio of non-convergent edges. The experiments on different GPUs show that our algorithms perform well throughout the calculation process. Compared with state-of-the-art algorithms, CW algorithm converged most of the messages in previous iterations. The convergence degree of CE is higher than all other algorithms in most calculation processes. rd converges fast and always has a high degree of convergence.

关键词： Belief propagation GPUs parallel optimization Coloring operations Progressively reducing update ratio

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Adaptive Detection Method for Packet-In Message Injection Attack in SDN 19th

Adaptive Detection Method for Packet-In Message Injection At...

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19th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Zhan, Xinyu Chen, Mingsong Yu, Shui Zhang, Yue East China Normal Univ Shanghai Key Lab Trustworthy Comp Room 308Math Bldg3663 North Zhongshan Rd Shanghai 200062 Peoples R China East China Normal Univ Software Hardware Codesign Engn Res Ctr MOE Shanghai Peoples R China Univ Technol Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9783030389611;9783030389604

Packet-In message injection attack is severe in Software Defined Network (SDN), which will cause a single point of failure of the centralized controller and the crash of the entire network. Nowadays, there are many detection methods for it, including entropy detection and so on. We propose an adaptive detection method to proactively defend against this attack. We establish a Poisson probability distribution detection model to find the attack and use the flow table filter to mitigate it. We also use the EWMA method to update the expectation value of the model to adapt the actual network conditions. Our method has no need to send additional packets to request the switch information. The experiment results show that there is 92% true positive rate in case of attack with random destination IP packets injected, and true positive rate is 98.2% under the attack with random source IP packets injected.

关键词： Software-Defined Network Packet-In message injection attack Controller security Adaptive detection

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Fast Segmentation-Based Object Tracking Model for Autonomous Vehicles 20th

Fast Segmentation-Based Object Tracking Model for Autonomous...

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20th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Dong, Xiaoyun Niu, Jianwei Cui, Jiahe Fu, Zongkai Ouyang, Zhenchao Beihang Univ Hangzhou Innovat Inst Chuanghui St 18 Hangzhou 310000 Zhejiang Peoples R China State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp BDB Xueyuan Rd 37 Beijing 100191 Peoples R China Nanhu Lab Jiaxin 314000 Zhejiang Peoples R China Zhengzhou Univ Zhengzhou Univ Res Inst Ind Technol Zhengzhou 450001 Peoples R China

ISBN: (纸本)9783030602390;9783030602383

On-road object tracking is a criticalmodule for both Advanced Driving Assistant System (ADAS) and autonomous vehicles. Commonly, this function can be achieved through single vehicle sensors, such as a camera or LiDAR. Consider the low cost and wide application of optical cameras, a simple image segmentation-based on-road object tracking model is proposed. Different from the detection-based tracking with bounding box, our model improves tracking performance from the following three aspects: 1) the Positional Normalization (PONO) feature is used to enhance the target outline with common convolutional layers. 2) The inter-frame correlation of each target used for tracking relies on mask, this helps the model reducing the influences caused by the background around the targets. 3) By using a bidirectional LSTM module capable of capturing timing correlation information, the forward and reverse matching of the targets in consecutive frames is performed. We also evaluate the presented model on the KITTI MOTS (Multi-Object and Segmentation) task which collected from out door environment for autonomous vehicle. Results show that our model is three times faster than Track RCNN with slightly drop on sMOTSA, and is more suitable for deployment on vehicular low-power edge computing equipment.

关键词： Object tracking Segmentation Bi-LSTM Autonomous vehicle Deep learning

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Typing Everywhere with an EMG Keyboard: A Novel Myo Armband-Based HCI Tool 20th

Typing Everywhere with an EMG Keyboard: A Novel Myo Armband-...

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20th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Fu, Zongkai Li, Huiyong Ouyang, Zhenchao Liu, Xuefeng Niu, Jianwei Beihang Univ State Key Lab Virtual Real Technol & Syst Xueyuan Rd 37 Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Chuanghui St 18 Hangzhou 310000 Zhejiang Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp BDB Xueyuan Rd 37 Beijing 100191 Peoples R China Zhengzhou Univ Zhengzhou Univ Res Inst Ind Technol Zhengzhou 450001 Peoples R China Nanhu Lab Jiaxing 314000 Zhejiang Peoples R China

ISBN: (纸本)9783030602451;9783030602444

To enhance users' experience of inputting characters on mobile devices with small screens, this paper designed a novel virtual keyboard used on mobile devices. In particular, we introduce a novel virtual keyboard based on the MYO armband which is able to capture the electromyogram (EMG) signals of users when typing on any surfaces (such as human body or normal desktop). The actions of the three fingers are mapped to the nine keys of the T9 keyboard. After that, the signals of finger motions are translated into key sequences of the T9 keyboard. However, the identification of continuous finger motions is a critical challenge. To address the challenge, we convert the EMG signals in time domain into a 3D time-frequency map (each channel corresponds to the EMG unit of a frequency-domain feature), and extract the convolutional features with a 4-layer CNN (Convolutional Neural Network) module, an im2col module of Optical Character Recognition (OCR) and a Long Short-Term Memory (LSTM) module, and the final result is achieved as a probability graph of finger gestures. The Connection Temporal Classification (CTC) algorithm is adopted to find the best gesture sequence from the probability map. Experimental results show that our method can effectively identify different key sequences at three different input speeds with an average accuracy of 85.9%, and the integration testing with different volunteers shows that our method can achieve an average typing speed of 15.7 Word-Per-Minute (WPM).

关键词： EMG Keyboard Deep learning CTC Mobile device

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