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SPECK: Composition of Stream processing applications over Fog Environments 21st

SPECK: Composition of Stream Processing Applications over Fo...

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21st IFIP WG 6.1 international conference on distributed applications and Interoperable Systems (DAIS) Held as Part of the 16th international Federated conference on distributed Computing techniques (DisCoTec)

作者： Battulga, Davaadorj Miorandi, Daniele Tedeschi, Cedric Univ Rennes IRISA CNRS Inria Rennes France U Hopper Trento Italy

ISBN: (纸本)9783030781972;9783030781989

Stream processing (SP), i.e., the processing of data in motion, as soon as it becomes available, is a hot topic in cloud computing. Various SP stacks exist today, with applications ranging from IoT analytics to processing of payment transactions. The backbone of said stacks are Stream processing Engines (SPEs), software packages offering a high-level programming model and scalable execution of data stream processing pipelines. SPEs have been traditionally developed to work inside a single datacenter, and optimised for speed. With the advent of Fog computing, however, the processing of data streams needs to be carried out over multiple geographically distributed computing sites: Data gets typically pre-processed close to where they are generated, then aggregated at intermediate nodes, and finally globally and persistently stored in the Cloud. SPEs were not designed to address these new scenarios. In this paper, we argue that large scale Fog-based stream processing should rely on the coordinated composition of geographically dispersed SPE instances. We propose an architecture based on the composition of multiple SPE instances and their communication via distributed message brokers. We introduce SPECK, a tool to automate the deployment and adaptation of pipelines over a Fog computing platform. Given a description of the pipeline, SPECK covers all the operations needed to deploy a stream processing computation over the different SPE instances targeted, using their own APIs and establishing the required communication channels to forward data among them. A prototypical implementation of SpecK is presented, and its performance is evaluated over Grid'5000, a large-scale, distributed experimental facility.

关键词： Stream processing Deployment Geographically distributed platforms

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Intelligent Cloud Data Indexing and Retrieval Through Random Forest Algorithms

Intelligent Cloud Data Indexing and Retrieval Through Random...

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international conference on Signal processing, Communication, Power and Embedded System (SCOPES)

作者： K. Mohan Raj A. Karthikeyan Department of CSE. Mahendra Engineering College Namakkal Tamil Nadu India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Another significant research issue is that of data indexing and retrieval in cloud computing context given that the virtues of velocity, variety, and volume are escalating pervasively. Indexing techniques have in the past proved difficult for real-time cloud applications requirements. This research focuses on the utilization of developed Random Forest algorithms as a new conception of smart cloud data indexing and search. The reported method exploits the strict parallelism and scaling properties of Random Forest algorithms to design a distributed and robust indexing scheme that can efficiently process lager cloud data. During the indexing phase, feature extraction takes place followed by partitioning of the data, construction of several decision trees for search capability. For data retrieval experiments performed on benchmark cloud data sets, the results indicate that the proposed Random Forest-based indexing achieves average accuracy equal to 92.4%, the efficiency is 15-20% higher than traditional indexing methods. The method also stands high on parameters of data skew and node failure for providing reliable performance with frequent change in cloud-based infrastructure. The introduction of Random Forest algorithms into cloud data indexing serves as a boiling prospect in creating scalability and efficiency in the discovery process amidst the growing big data problem. The developments proposed within this research point to the future developments regarding intelligent cloud data management and create new opportunities for this topic within the field of cloud computing.

关键词： Cloud computing Scalability Signal processing algorithms Signal processing parallel processing Real-time systems Partitioning algorithms Reliability Random forests Indexing

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An Approach for Face Detection and Face Recognition Using OpenCV and Face Recognition Libraries in Python using GPU

An Approach for Face Detection and Face Recognition Using Op...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Himanshu Nainwal Koshti Vanshika Shaileshbhai Computer Science and Engineering S.R.M Institute of Science and Technology Tamil Nadu India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Lots of Face recognition technology has seen significant advancements in these years. It offers many applications such as in security systems, surveillance, and biometric authentication. With all these deep learning techniques spreading around, the performance of face recognition algorithms has got better. But the computational demands still back from getting all the way there. processing large datasets or real-time video streams can be a real challenge sometimes. Anyhow, this project brings right off a new way to face recognition by using GPU support besides Python libraries like OpenCV and face_recognition. By stepping into the world of parallel processing power provided by GPUs, our system just speeds up the process of face recognition by miles compared to the old-fashioned CPU-based setups. The real deal is that GPU support can do parallel computation of some complex neural network operations, speeding up the inference times vastly. This is a huge plus for the applications that crave real-time or like-real-time performance, like surveillance systems and video analytics.

关键词： Technological innovation Face recognition Surveillance Visual analytics Graphics processing units Real-time systems Libraries Security Face detection Python

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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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1st international conference on Advances in Signal processing and Communication Engineering, ICASPACE 2021

1st International Conference on Advances in Signal Processin...

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1st international conference on Advances in Signal processing and Communication Engineering, ICASPACE 2021

ISBN: (纸本)9789811955495

The proceedings contain 44 papers. The special focus in this conference is on Advances in Signal processing and Communication Engineering. The topics include: Single-Precision Floating-Point Multiplier Design Using Quantum-Dot Cellular Automata with Power Dissipation Analysis;compression techniques for Low Power Hardware Accelerator Design: Case Studies;sequence Set Design for Radar System;design of an All Digital Phase-Locked Loop Using Cordic Algorithm;analysis of Deep Learning Algorithms for Image Denoising;an Extensive Survey on Assessment of Multicore Processors for Embedded Systems;image Segmentation techniques and Optimization Algorithms for Lung Cancer Detection;handwritten to Text Document Converter;An Efficient Energy Aware for Reliable Route Discovery Using Energy with Movement Detection Technique in MANET;Lumped Circuit Modeling at Nanoscale (Part-II: Coupling Between Two Nanospheres);design and Implementation of Imprecise Adders for Low-Power applications;speech Processed Public Addressing System;an Approach Towards Data Privacy Issues in distributed Cyber Physical System;Classification of LPI Radar Signals Using Multilayer Perceptron (MLP) Neural Networks;a Systematic Review on Screening of Diabetic Retinopathy and Maculopathy Using Artificial Intelligence;Area Efficient and High-Throughput Radix-4 1024-Point FFT Processor for DSP applications;air Quality Monitoring System Based on Artificial Intelligence;an Improved Technique for Identification of Forgery Image Detection Using Clustering Method;EEG Signal Analysis During Stroop Task for Checking the Effect of Sleep Deprivation;UWB Localization Procedures with Range Control Methods—A Review;deep Learning Model for Multiclass Classification of Diabetic Retinal Fundus Images Using Gradient Descent Optimization;multilevel Authentication to Wireless Sensor Networks Against Malicious Attacks Using Butterfly Method.

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WMDRS: Workload-Aware Performance Model Based Multi-Task Dynamic-Quota Real-Time Scheduling for Neural processing Units

WMDRS: Workload-Aware Performance Model Based Multi-Task Dyn...

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international conference on parallel and distributed Systems (ICPADS)

作者： Chong Liu Yuan Yao Yi Dang Gang Yang Wei Jia Xinyu Tian Xingshe Zhou School of Computer Science Northwestern Polytechnical University Xi’an China Department of Aviation Electronics Xian Aisheng Technology Group Co.Ltd Xi’an China

To further improve the capacity of airborne embedded system for dealing with deep learning (DL) applications and reduce overall power consumption, it is necessary to equip Neural processing Units (NPUs). Comparing with the cloud system, the airborne embedded system usually has a fixed application set, but strict real-time constraints. Unfortunately, the inherent NPU scheduler does not consider the application priority, which cannot provide the sufficient real-time capability for the airborne embedded system. At present, there are few researches on multi-task real-time scheduling for NPUs. Therefore, we propose WMDRS, a workload-aware performance model multi-task dynamic-quota real-time scheduling for Neural processing Units. The NPU performance model based on workload-awareness can accurately predict the remaining execution time of a task, which is running concurrently with other tasks on NPU. The multi-task dynamic-quota real-time scheduling algorithm can provide the approximate preemption by dynamically adjusting NPU computing resources for active applications. In addition, we implement a prototype NPU scheduler without any hardware extension. Furthermore, the proposed NPU performance model and real-time scheduling algorithm are evaluated in realistic application sets. Experimental results demonstrate that WMDRS can achieve low prediction error and high scheduling success ratio.

关键词： Embedded systems Scheduling algorithms Computational modeling Atmospheric modeling Prototypes Predictive models Multitasking

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PhD Forum: Efficient Privacy-Preserving processing via Memory-Centric Computing

PhD Forum: Efficient Privacy-Preserving Processing via Memor...

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Reliable distributed Systems

作者： Mpoki Mwaisela University of Neuchâtel Neuchâtel Switzerland

ISBN: (数字)9798331530037

ISBN: (纸本)9798331530044

Privacy-preserving computation techniques like homomorphic encryption (HE) and secure multi-party computation (SMPC) enhance data security by enabling processing on encrypted data. However, the significant computational and CPU-DRAM data movement overhead resulting from the underlying cryptographic algorithms impedes the adoption of these techniques in practice. Existing approaches focus on improving computational overhead using specialized hardware like GPUs and FPGAs, but these methods still suffer from the same processor-DRAM overhead. Novel hardware technologies that support in-memory processing have the potential to address this problem. Memory-centric computing, or processing-in-memory (PIM), brings computation closer to data by introducing low-power processors called data processing units (DPUs) into memory. Besides its in-memory computation capability, PIM provides extensive parallelism, resulting in significant performance improvement over state-of-the-art approaches. We propose a framework that uses recently available PIM hardware to achieve efficient privacy-preserving computation. Our design consists of a four-layer architecture: (1) an application layer that decouples privacy-preserving applications from the underlying protocols and hardware; (2) a protocol layer that implements existing secure computation protocols (HE and MPC); (3) a data orchestration layer that leverages data compression techniques to mitigate the data transfer overhead between DPUs and host memory; (4) a computation layer which implements DPU kernels on which secure computation algorithms are built.

关键词： Privacy Protocols Program processors Scalability Computer architecture parallel processing Hardware Multi-party computation Reliability Homomorphic encryption

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Quantum-Inspired Data processing for Big Data Analytics

Quantum-Inspired Data Processing for Big Data Analytics

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Sunil Raj Thota Saransh Arora Sandeep Gupta Technology Engineering Andhra University Visakhapatnam AP India Jaypee Institute of Information Technology Noida U.P. India Techieshubhdeep IT Solutions Pvt. Ltd Gwalior M.P. India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Technological advancements in the 21st century have led to the rise of "big data," characterized by datasets so vast and complex that traditional database systems struggle to manage them. This term denotes datasets that are too vast and complex to be processed by traditional database systems. As big data continues to evolve, it introduces significant challenges related to data processing, storage, and analysis. In response, quantum-inspired algorithms have emerged as a promising solution to these challenges. Quantum-inspired algorithms, which draw on principles from quantum computing, present a promising solution to the challenges of big data processing, storage, and analysis. techniques such as Quantum Annealing, Quantum Circuits and Gates, and Quantum parallelism leverage quantum principles like superposition and entanglement to enhance data processing efficiency and speed. These methods offer significant improvements over traditional tools like Hadoop, MapReduce, RapidMiner, and R Programming, which are used for distributed storage, processing, and analytics of large datasets. By integrating quantum-inspired algorithms with existing data processing techniques, this study aims to address the limitations of classical methods and advance the field of big data analytics.

关键词： Data analysis Annealing Quantum entanglement Big Data Programming parallel processing Logic gates Quantum annealing Database systems Quantum circuit

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Efficient Data Confidence Fabrics with Compact Annotations

Efficient Data Confidence Fabrics with Compact Annotations

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international conference on Network Protocols

作者： Asfa Khalid Seán Óg Murphy Cormac Sreenan Utz Roedig School of Computer Science and Information Technology (CSIT) University College Cork Cork Ireland

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

The need to trust data has become a key requirement in modern distributed systems. To facilitate measurable trust and confidence in data and applications spanning heterogeneous systems, the emerging concept of a Data Confidence Fabric (DCF) offers a compelling solution. Data producers and processors provide metadata, known as annotations, recording trust insertion along the data delivery chain. Thus, it is possible to asses the trustworthiness of data before processing it. While a DCF, such as the Alvarium framework, enables this management of trust, there is a cost in terms of the overheads associated with security annotations themselves. To improve efficiency of a DCF, we therefore propose a set of techniques for making annotations more compact and to reduce the number of DCF transactions. Our work shows that transaction efficiency gains of up to 93% in our considered use cases can be achieved.

关键词： Protocols Program processors Annotations distributed ledger distributed databases Data collection Fabrics Recording Security Reliability

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PC-BNA: parallel Convolution Binary Neural Network Accelerator 24

PC-BNA: Parallel Convolution Binary Neural Network Accelerat...

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24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Liu, Haihui Zhang, Yuang Zheng, Xiangwei Yu, Mengxin An, Baoxu Liu, Xiaojie School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9798350319934

Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dataset is obviously low, and the input layer uses 32-bit floating point to avoid excessive precision loss, which requires additional computing units and increases the computational burden. Therefore, it is very important to improve the input layer, save computing resources and reduce precision loss. In this paper, we propose a parallel convolution binary neural network accelerator architecture (PC-BNA). Based on our proposed BNN model, we design an efficient field programmable gate array (FPGA) based accelerator. The input of the first layer is binarized, the traditional binary convolution layer is replaced by a parallel binary convolution, and the network building blocks are improved. The experimental results show that the proposed PC-BNA has higher accuracy and better performance on CIFAR-10. The image recognition accuracy is as high as 91.4%, which is superior to the state-of-art of the BNN accelerator. Compared to the state-of-the-art BNN model, when using the same model size, the use of look-up-table (LUT) saves 9.08%, and the digital signal processor (DSP) saves 27.7%. The results suggest that it is promising for PC-BNA in future high performance mobile artificial intelligence applications. © 2022 IEEE.

关键词： Convolution

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