The proceedings contain 80 papers. The special focus in this conference is on Futuristic Trends in Network and Communication Technologies. The topics include: Unmanned Vehicles: Safety Management Systems and Safety Fu...
ISBN:
(纸本)9789811614828
The proceedings contain 80 papers. The special focus in this conference is on Futuristic Trends in Network and Communication Technologies. The topics include: Unmanned Vehicles: Safety Management Systems and Safety Functions;probabilistic Characteristics of a Two-Channel Detector with Two Inertial Single-Photon Photoemission Devices and an Electronic Adder;energy and Spectrum-Aware Cluster-Based Routing in Cognitive Radio Sensor Networks;The Hybrid Approach for the Partitioning of VLSI Circuits;maximization of IoT Network Lifetime Using Efficient Clustering Technique;a Hybrid Metaheuristic to Solve Capacitated Vehicle Routing Problem;Energy Conservation in IOT: A Survey;identification of Implicit Threats Based on Analysis of User Activity in the Internet Space;EERO: Energy Efficient Route Optimization Technique for IoT Network;representing a Quantum Fourier Transform, Based on a Discrete Model of a Quantum-Mechanical System;forecasting Non-Stationary Time Series Using Kernel Regression for Control Problems;A Smart Waste Management System Based on LoRaWAN;simulation of the Semantic Network of Knowledge Representation in Intelligent Assistant Systems Based on Ontological Approach;A Deep Learning Approach for Autonomous Navigation of UAV;framework for processing Medical Data and Secure Machine Learning in Internet of Medical Things;path Planning for Autonomous Robot Navigation: Present Approaches;Development of a Routing Protocol Based on Clustering in MANET;threat Model for Trusted Sensory Information Collection and processing Platform;autonomous Navigation of Mobile Robot with Obstacle Avoidance: A Review;Design of a distributed Debit Management Network of Operating Wells of Deposits of the CMW Region;design of U-Shaped Multiline Microstrip Patch Antenna for Advanced Wireless applications.
Payload anomaly detection can discover malicious beliaviors tiidden in network packets. It is liard to liandle payload due to its various possible characters and complex semantic context, and tlius identifying abnorma...
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GPUs can accelerate hash tables for fast storage and look-ups utilizing their massive parallelism. State-of-the-art GPU hash tables use keys with fixed length like integers for optimal performance. Because strings are...
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ISBN:
(纸本)9783031124266;9783031124259
GPUs can accelerate hash tables for fast storage and look-ups utilizing their massive parallelism. State-of-the-art GPU hash tables use keys with fixed length like integers for optimal performance. Because strings are often used for structures like dictionaries, it is useful to support keys with variable length as well. Modern GPUs enable the combination of CPU and GPU compute power and we propose a hybrid approach, where keys on the GPU have a maximum length and longer keys are processed on the CPU. Therefore we develop a GPU accelerated approach of robin-map and libcuckoo based on string keys. We use OpenCL for GPU acceleration and threads for the CPU. Furthermore, we integrate the GPU approach into our benchmark framework H-2 and evaluate it against the CPU variants and the GPU approach adapted for the CPU. We evaluated our approach in the hybrid context by using longer keys on CPU and shorter keys on GPU. In comparison to the original libcuckoo algorithm our GPU approach achieves a speed-up of 2.1 and in comparison to the robin-map a speed-up of 1.5. For hybrid workloads our approach is efficient if long keys are processed on the CPU and short keys are processed on the GPU. By processing a mixture of 20% long keys on CPU and 80% short keys on GPU our hybrid approach has a 40% higher throughput than the CPU only approach.
The project aims to develop innovative data management solutions (database and file system archive) for the Spoke 3 division of the Italian National Center for HPC, Big Data, and Quantum Computing, rigorously applying...
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ISBN:
(数字)9798331524937
ISBN:
(纸本)9798331524944
The project aims to develop innovative data management solutions (database and file system archive) for the Spoke 3 division of the Italian National Center for HPC, Big Data, and Quantum Computing, rigorously applying the FAIR principles and Open Science methodologies. This system provides a robust and scalable framework designed to address big data challenges in astrophysics, which requires customized data models and management solutions. It focuses on managing observations from the Gaia and Fermi space missions, as well as simulations generated using the Pluto and Ramses codes, with the goal of supporting requests from the astrophysical Italian community on particular scientific *** Gaia data, we identified the HDF5 as a suitable format to improve data access and analysis outside the data reduction pipelines. The HDF5 format allows the nesting of primitive fields, arrays, and complex objects, thereby enhancing accessibility and computational efficiency. For the data management of the FT1 and FT2 data products from the Fermi Observatory, a common data model has been chosen to provide a unique point of access to the scientific data, employing logic for database queries and offering access to data products over a long observing period. Finally, to facilitate user interaction, a custom web application has been developed, providing a user-friendly interface for accessing and querying the archived metadata, additional “Cut & Merge” and “Transits” services, and supporting also secure authentication methods to ensure data privacy and compliance. These comprehensive resources enhance data accessibility and usability through the Spoke3 Archive Infrastructure, empowering the scientific community to explore and analyze astronomical data more effectively.
The solver module of the Astrometric Verification Unit-Global Sphere Reconstruction (AVU-GSR) pipeline aims to find the astrometric parameters of $\sim 10^{8}$ stars in the Milky Way, the attitude and instrumental set...
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ISBN:
(数字)9798331524937
ISBN:
(纸本)9798331524944
The solver module of the Astrometric Verification Unit-Global Sphere Reconstruction (AVU-GSR) pipeline aims to find the astrometric parameters of $\sim 10^{8}$ stars in the Milky Way, the attitude and instrumental settings of the Gaia satellite, and the parametrized post Newtonian parameter $\gamma$ with a resolution of 1 0 - 100 micro – arc seconds. To perform this task, the code, which runs in production on Leonardo CINECA infrastructure, solves a system of linear equations with the iterative LSQR algorithm, where the coefficient matrix is large (10-50 TB) and sparse and the iterations stop when least square convergence is reached. The solver was ported to GPU with CUDA, obtaining a $\sim 14 x$ acceleration factor over an original version CPU-parallelized with OpenMP. This work concentrates on a code section dedicated to covariances calculation, representing an important scientific task for Gaia mission, since the problems unknowns present strong correlations. Given the number of unknowns at mission end, the variances-covariances matrix is expected to occupy $\sim 1$ EB, which represents a substantial “Big Data” issue. To compute a subset of the total covariances, we defined an I/Obased pipeline made of two jobs. The first job, the LSQR, writes the files every $i \operatorname{tnCov} C P$ iterations, and the second job reads them and calculates the corresponding covariances. The two jobs can be launched either in sequence or concurrently. Previous studies demonstrated that the covariances calculation does not significantly slowdown the AVU-GSR production up to $\sim 3 \times 10^{7}$ covariances. Here we investigate the performance of the covariances pipeline as a function of $i t n \operatorname{Cov} C P$. The results show that writing smaller files more frequently or writing larger files less frequently does not affect the global performance of the solver, whose speed only depends on the number of covariances to calculate and of system unknowns.
The proceedings contain 47 papers. The topics discussed include: design a tracing system for a seed supply chain based on blockchain;new S-box transformation Based on chaotic system for image encryption;a cost-efficie...
ISBN:
(纸本)9781728182315
The proceedings contain 47 papers. The topics discussed include: design a tracing system for a seed supply chain based on blockchain;new S-box transformation Based on chaotic system for image encryption;a cost-efficient management protocol for mobile crowd-sensing in urban vehicular scenarios;transmission mode selection for reliable critical data transmission;Iraqi e-learning adoption trials for building sustained developed society;performance measurement of processes and threads controlling, tracking and monitoring based on shared-memory parallelprocessing approach;galactic swarm optimization based adaptive digital image watermarking for optimal positions detection;a proposed image compression technique based on DWT and predictive techniques;security enhancement of AES-CBC and its performance evaluation using the avalanche effect;and distributed transform encoder to improve Diffie-Hellman protocol for big message security.
The ability to scale out training workloads has been one of the key performance enablers of deep learning. The main scaling approach is data-parallel GPU-based training, which has been boosted by hardware and software...
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ISBN:
(纸本)9781450393409
The ability to scale out training workloads has been one of the key performance enablers of deep learning. The main scaling approach is data-parallel GPU-based training, which has been boosted by hardware and software support for highly efficient point-to-point communication, and in particular via hardware bandwidth overprovisioning. Overprovisioning comes at a cost: there is an order of magnitude price difference between "cloud-grade" servers with such support, relative to their popular "consumer-grade" counterparts, although single server-grade and consumer-grade GPUs can have similar computational envelopes. In this paper, we show that the costly hardware overprovisioning approach can be supplanted via algorithmic and system design, and propose a framework called CGX, which provides efficient software support for compressed communication in ML applications, for both multi-GPU single-node training, as well as larger-scale multi-node training. CGX is based on two technical advances: At the system level, it relies on a re-developed communication stack for ML frameworks, which provides flexible, highly-efficient support for compressed communication. At the application level, it provides seamless, parameter-free integration with popular frameworks, so that end-users do not have to modify training recipes, nor significant training code. This is complemented by a layer-wise adaptive compression technique which dynamically balances compression gains with accuracy preservation. CGX integrates with popular ML frameworks, providing up to 3X speedups for multi-GPU nodes based on commodity hardware, and order-of-magnitude improvements in the multi-node setting, with negligible impact on accuracy.
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 computation...
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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.
The project aims to develop innovative data management solutions (database and file system archive) for the Spoke 3 division of the Italian National Center for HPC, Big Data, and Quantum Computing, rigorously applying...
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With the arrival of the edge computing a new challenge arises for cloud applications: How to benefit from geo-distribution (locality) while dealing with inherent constraints of wide-area network links? The admitted ap...
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ISBN:
(纸本)9783030856656;9783030856649
With the arrival of the edge computing a new challenge arises for cloud applications: How to benefit from geo-distribution (locality) while dealing with inherent constraints of wide-area network links? The admitted approach consists in modifying cloud applications by entangling geo-distribution aspects in the business logic using distributed data stores. However, this makes the code intricate and contradicts the software engineering principle of externalizing concerns. We propose a different approach that relies on the modularity property of microservices applications: (i) one instance of an application is deployed at each edge location, making the system more robust to network partitions (local requests can still be satisfied), and (ii) collaboration between instances can be programmed outside of the application in a generic manner thanks to a service mesh. We validate the relevance of our proposal on a real use-case: geo-distributing OpenStack, a modular application composed of 13 million of lines of code and more than 150 services.
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