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DeLiBA-K: Speeding-up Hardware-Accelerated distributed Storage Access by Tighter Linux Kernel Integration and Use of a Modern API

DeLiBA-K: Speeding-up Hardware-Accelerated Distributed Stora...

引用

High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Babar Khan Andreas Koch Embedded Systems and Applications Group TU Darmstadt Germany

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

We present DeLiBA-K, an improved version of the Development of Linux Block I/O Accelerators (DeLiBA) framework. DeLiBA-K operates at the Linux kernel level, bypassing the user-space interactions of DeLiBA-1 and -2 to interact with the block and network I/O kernel stack directly. Another critical feature of DeLiBA-K is implementing and benchmarking the modern io_uring Asynchronous I/O (AIO) API within a 16nm AMD Alveo U280 FPGA I/O framework. This allows for better parallelism and reduced latency in I/O operations. Our results show significant performance gains, up to a 3.2x improvement in I/O operations per second (IOPS) and 3.45x increased throughput for synthetic workloads. Real-world applications see a 30% reduction in execution time for data-intensive tasks. DeLiBA-K has been successfully tested in an industrial environment using real workloads, demonstrating its effectiveness in large-scale enterprise environments.

关键词： Linux High performance computing conferences Performance gain parallel processing Benchmark testing Throughput Kernel Field programmable gate arrays

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Scheduling Linear Workflows with Various Levels of Privacy Requirements in a Fog – Cloud Environment

Scheduling Linear Workflows with Various Levels of Privacy R...

引用

international conference on Computer Theory and applications (ICCTA)

作者： Helen D. Karatza Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350309584

ISBN: (纸本)9798350309591

In complex distributed computing architectures, such as fog and cloud frameworks, there are workloads which consist of jobs characterized by different levels of privacy requirements. In this type of computing environment, it is of paramount importance to employ privacy-aware scheduling schemes, so that appropriate execution of the applications is ensured, and the desired Quality of Service (QoS) is achieved. Based on this direction in this research we study privacy-aware scheduling techniques for linear workflow (LW) applications in a fog-cloud computing platform. Only the fog resources are considered suitable for processing LW jobs with privacy requirements. The remaining LW jobs may run on either cloud or fog resources. A new scheduling technique is employed which applies approximate computations for the sake of jobs requiring to ensure privacy of their data. It is compared with the case where no approximate computations can be tolerated. Extensive simulation experiments are carried out for the evaluation of their performance. The simulations reveal useful insights into how different levels of approximate computations set dynamically at run time affect the performance of the system in each one of the investigated cases.

关键词： Performance evaluation Cloud computing Privacy Data privacy Processor scheduling Quality of service Computer architecture distributed computing

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Improving High Dimensional Data Streams Classification

Improving High Dimensional Data Streams Classification

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international conference on Embedded & distributed Systems (EDiS)

作者： Hadjer Imene Bensaoula Maissa Lounici Sarah Nait Bahloul SIMPA Laboratory Computer Science Department University of Science and Technology of Oran Mohamed Boudiaf USTO-MB Oran Algeria Computer Science Department University of Science and Technology of Oran Mohamed Boudiaf USTO-MB Oran Algeria LSSD Laboratory Computer Science Department University of Science and Technology of Oran Mohamed Boudiaf USTO-MB Oran Algeria

ISBN: (数字)9798331519872

ISBN: (纸本)9798331519889

Traditional data mining approaches can no longer meet the analytical needs of modern Big Data, as many of today's applications generate an infinite amount of dynamic data known as “data streams”, in real time and at rapid rates. In response, data stream mining has emerged as a new paradigm for processing and deriving real-time insights from such data. Data stream mining encompasses a variety of techniques, with stream classification being the focus of much research. Accordingly, this research aims to refine one of the key methods employed in online classification. This study explores the application of Adaptive Random Forests (ARF) to data stream classification, with a focus on the Compressed-Sensing ARF (CS-ARF) variant, which has been specifically designed to tackle the challenges of high-dimensional data streams classification. Based on the CS-ARF implementation, our research aims to further enhance its performance. Experimental results demonstrate promising outcomes, particularly in terms of improved classification accuracy.

关键词： Runtime Accuracy Scalability distributed databases Real-time systems Sensors Data mining Decision trees Random forests Compressed sensing

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MICRoFuzz: An Efficient Fuzzing Framework for Microservices

MICRoFuzz: An Efficient Fuzzing Framework for Microservices

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IEEE/ACM international conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP)

作者： Peng Di Bingchang Liu Yiyi Gao Ant Group Hangzhou China Ant Group Beijing China

ISBN: (数字)9798400705014

ISBN: (纸本)9798350352177

Fuzzing is a widely adopted technique in the software industry to enhance security and software quality. However, most existing fuzzers are specifically designed for monolithic software architectures and face significant limitations when it comes to serving distributed Microservices applications (Apps). These limitations primarily revolve around issues of inconsistency, communication, and applicability which arise due to the differences in monolithic and distributed software architecture. This paper presents a novel fuzzing framework, called MICRO-Fuzz, specifically designed for Microservices. Mocking-Assisted Seed Execution, distributed Tracing, Seed Refresh and Pipeline parallelism approaches are adopted to address the environmental complex-ities and dynamics of Microservices and improve the efficiency of fuzzing. MICROFuzz has been successfully implemented and deployed in Antgroup 1 , a prominent FinTech company. Its per-formance has been evaluated in three distinct industrial scenarios: normalized fuzzing, iteration testing, and taint verification. Throughout five months of operation, MICROFuzz has diligently analyzed a substantial codebase, consisting of 261 Apps with over 74.6 million lines of code (LOC). The framework's effectiveness is evident in its detection of 5,718 potential quality or security risks, with 1,764 of them confirmed and fixed as actual security threats by software specialists. Moreover, MICROFuzz significantly increased line coverage by 12.24% and detected new paths by 38.42% in the iteration testing.

关键词： Industries Software architecture Pipelines Microservice architectures Software quality Fuzzing parallel processing

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Efficient Wavefront parallel processing for HEVC CABAC Decoding 28

Efficient Wavefront Parallel Processing for HEVC CABAC Decod...

引用

28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Habermann, P. Juurlink, B. Chi, C. C. Alvarez-Mesa, M. TU Berlin Embedded Syst Architecture Berlin Germany Spin Digital Video Technol GmbH Berlin Germany

ISBN: (纸本)9781728165820

Context-based Adaptive Binary Arithmetic Coding (CABAC) is the only compute-intensive task in the High Efficiency Video Coding (HEVC) Standard that does not contain significant data-level parallelism. As a result, it is often a throughput bottleneck for the overall decoding process, especially for high-quality videos. Consequently, the use of high-level parallelization techniques is inevitable to reach throughput requirements for CABAC decoding. Multiple high-level parallelization tools are specified in HEVC, amongst which Wavefront parallel processing (WPP) has only small losses in coding efficiency. However, it lacks in parallel efficiency due to a ramp-up and -down in active parallel threads within a frame. This is a serious problem for systems that cannot process multiple frames at the same time due to performance or memory constraints (e.g. mobile devices), and also for low-delay applications such as video conferencing. To address this issue, we present three improved WPP implementations for HEVC CABAC decoding. They differ in the granularity at which dependency checks are performed. The improvement comes from increased parallel efficiency of the WPP implementation while using the same number of threads as conventional WPP. The proposed implementations allow speedups up to 1.83 x with very little implementation overhead.

关键词： HEVC H.265 CABAC Wavefront parallel processing

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Continuous distributed processing of Software Defined Radar

Continuous Distributed Processing of Software Defined Radar

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2021 CIE international conference on Radar, Radar 2021

作者： Li, Bing Qiu, Qiang Gong, Shiqi Liu, Yongjun Lei, Yu Institute of Computing Technology CAS Key Lab of Network Data Science and Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Xidian University National Laboratory of Radar Signal Processing Xi'an China Golaxy Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665498142

Software-defined radar has been an active research field for more than ten years. However, the low performance and low scalability of the traditional processing techniques of SDR make it hard to deal with complex radar tasks. The stream processing engine is a software that supports low latency and high scalability distributed data processing. It is widely used in log, image, and video processing scenarios but rarely used in radar applications. This paper reviews processing software-defined radar data by using the stream processing engine, and we illustrate the differences between processing log data and radar data. Furthermore, we discuss some optimizations for stream processing engines to get better performance in the radar data processing. © 2021 IEEE.

关键词： distributed processing Software-defined radar stream processing engine

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Design of Delay Efficient High Speed Adder Circuit

Design of Delay Efficient High Speed Adder Circuit

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BIT conference (BITCON), international

作者： Goutam Kumar Sushma Kumari Rohit Verma Ajeet Kumar Pandey Arvind Kumar Electronics and Communication Engineering GGSESTC Bokaro India Bokaro Steel City India Electronics and Communication Engineering IIT Dhanbad(ISM) India Bokaro Steel City India Electronics and Communication Engineering BIT Sindri India Bokaro Steel City India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

In the design of complex processor systems, components are broken down into smaller, manageable parts. These parts include essential circuits such as adders, subtractors, multipliers, and various functional units. The core components of these arithmetic systems are primarily adders. The Arithmetic and Logical Unit (ALU) is a crucial processing Unit, with its processing speed being a key factor in the overall performance of a CPU. This speed is influenced by the duration required to process data. In the realm of arithmetic operations, adders play a pivotal role on the key route. The quantity of logic levels taken into consideration while evaluating the performance of adders is a critical metric, area, and critical-path latency. It's crucial to enhance speed without compromising power efficiency. Therefore, a design that is both Elevated performance at low power is crucial. This work utilizes the parallel Prefix Architecture (PPA) to offer a novel approach to solving this problem. For energy-efficient applications and employs the "Hybrid Variable Latency" method to efficiently design adders. The performance of the adder is analyzed using Vivado. This proposed method is significantly faster compared to existing techniques.

关键词： Measurement Design methodology Hybrid power systems Energy efficiency Delays Central processing Unit Logic Adders Arithmetic

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An Agent-Oriented Twinning Architecture for Complex Event-Driven Anomaly Detection in distributed CPS

An Agent-Oriented Twinning Architecture for Complex Event-Dr...

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IEEE international conference on Software Architecture Workshops (ICSAW)

作者： Hussein Marah Lucas Lima Hans Vangheluwe Moharram Challenger University of Antwerp and Flanders Make Antwerp Belgium Universidade Federal Rural de Pernambuco Recife Brazil

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

The augmentation of Cyber-physical Systems (CPSs) with Digital Twins (DTs) through various twinning architectures has significantly changed how complex systems are designed, operated, and maintained. Recent advances in twinning provide high-fidelity digital replicas of physical entities, facilitating enhanced monitoring and predictive services. However, creating effective twinning systems for distributed CPSs is difficult due to complex relationships and convoluted interactions between system components. This makes monitoring and ensuring the system adheres to the requirements more challenging. Machine learning-based techniques have been proposed, but these have scalability and performance limitations. This work proposes a multi-agent-oriented architecture aimed at overcoming these challenges by fostering the integration between CPSs and their DTs. Our architecture enhances system monitoring and anomaly detection by combining intelligent software agents with a complex event-processing engine. This ensures robustness and fault tolerance in CPS applications while improving operational efficiency and accuracy. Besides describing our proposed framework, this paper discusses its implications by means of a multi-robot system for a warehouse, which demonstrates the applicability of our solution and provides some insights into future directions for research in this domain.

关键词： Software architecture Scalability Fault tolerant systems Computer architecture Software agents Robustness Multi-robot systems Monitoring Anomaly detection Multi-agent systems

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parallel Frequent Itemset Mining Algorithm and Optimization Based on Spark

Parallel Frequent Itemset Mining Algorithm and Optimization ...

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IEEE international conference on Artificial Intelligence and Computer applications (ICAICA)

作者： Chunran Liu Tianjin University of Technology Tianjin China

Frequent item set mining is an important task in data mining, which can help us find the variable combinations that occur frequently in the data set. In order to solve the problem of frequent itemset mining in large-scale high-dimensional data, we propose a parallel frequent itemset mining algorithm based on Spark, OSFPG. The algorithm is optimized on the basis of the traditional FP-growth algorithm, and the number of candidate item sets is reduced by constructing a special data structure called FP tree, thus improving the efficiency of frequent item set mining. The algorithm mainly uses the idea of load balancing to optimize the grouping strategy. It also considers two factors of partition computation and FP-Tree size to ensure that the total load between each group is approximately equal. On the basis of Spark based SFPG algorithm, we implement this optimized algorithm. Based on the SFPG algorithm, it is improved to dynamically allocate computing resources and carry out distributed computing, so that the intermediate results generated in the operation process are saved in memory, thus effectively reducing the data consumption. In order to verify the performance of OSFPG algorithm, we apply it to a real data set for experiments. Experimental results show that compared with the traditional FP-Growth algorithm, the proposed algorithm has higher efficiency and lower time complexity when processing large-scale high-dimensional data. In addition, the algorithm can deal with dynamically updated data effectively and has strong practicability.

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SLIDEX: Sliding Window Extension for Image processing 23

SLIDEX: Sliding Window Extension for Image Processing

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Proceedings of the 32nd international conference on parallel Architectures and Compilation techniques

作者： Raúl Taranco José-María Arnau Antonio González Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9798350342543

With the rising need for efficient image processing in emerging applications such as Autonomous Driving (AD) and Augmented/Virtual Reality (AR/VR), many existing solutions do not meet their performance and energy efficiency requirements or are domain-specific and lack generality. In this work, we introduce SLIDEX, a novel ISA extension that leverages Sliding Window processing (SWP) to bridge this gap in the image processing domain. SWP is a novel SIMD model that exposes to the programmer and natively manipulates vector registers as groups of overlapped windows of pixels to exploit the sliding-window dataflow found in convolutions and other stencil operations. SWP amplifies the available Data Level parallelism (DLP) and reduces memory and register file accesses. We evaluated SLIDEX benefits in the critical image processing task of a state-of-the-art visual localization system widely used in AD and AR/VR. SLIDEX obtains an ~1.2× overall speedup and 22% energy reduction.

关键词： convolution

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