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IEEE symposium on Bioinformatics and Bioengineering (BIBE)

作者： Lin Feng Angela Jean Chong Poh Leng Lai Danbo School of Computer Engineering Nanyang Technological University Singapore

Identification of DNA signatures is an important application which has been empowered by the increasing availability of thousands of bacterial and viral genomes through the next-generation sequencing (NGS) technologies. It is computationally challenging. In exploration for the solution, the suffix tree has been proposed as a data structure well suited to analyzing genomic sequences because it enables the storage of long strings in a logical, indexed manner for fast retrieval. We propose a new algorithm for identification of DNA signatures, based on exploitation of the endogenous features of a genomic sequence. these features are revealed in a construction of suffix array (an implementation of the generalized suffix tree). Furthermore, to greatly speed up the time-consuming process, the core algorithm is mapped and routed on to a Field-Programable Gate Array (FPGA) for parallel implementation. In this paper, we will focus primarily on the relationship of the data structure and the features associated. We will then describe the deployment of the hybrid computing system on a Hyper Transport compliant architecture. Illustrations are always given to clarify the technical details.

关键词： Genomics Arrays Vegetation DNA Bioinformatics Field programmable gate arrays Algorithm design and analysis

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Zero-Shot Synth-to-Real Depth Estimation: From Synthetic Street Scenes to Real-World Data

Zero-Shot Synth-to-Real Depth Estimation: From Synthetic Str...

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Brazilian symposium on computer Graphics and Image Processing (SIBGRAPI)

作者： Luís Gustavo Lorgus Decker Jose Luis Flores Campana Marcos Roberto e Souza Helena de Almeida Maia Helio Pedrini Institute of Computing University of Campinas Campinas SP Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

this paper introduces a novel method for estimating depth maps from single images using a convolutional neural network (CNN) architecture. Our approach leverages synthetically generated data that simulates front views from autonomous vehicles. the model employs a ConvNeXt encoder and a U-Net-based decoder, achieving effective depth estimation performance. A scale- and shift-invariant loss function is utilized during training to enhance generalization capabilities. the proposed model demonstrates strong results on real-world datasets without requiring fine-tuning, highlighting the effective transfer of information from synthetic to real-world data. Additionally, we show that high-quality data significantly improves performance compared to larger, low-quality datasets.

关键词： Training Graphics Sensitivity Filtering Data integrity Estimation Feature extraction Data models Convolutional neural networks Synthetic data

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Designing Efficient Irregular Networks for Heterogeneous Systems-on-Chip

Designing Efficient Irregular Networks for Heterogeneous Sys...

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Euromicro symposium on Digital System Design

作者： C. Neeb N. Wehn Erwin-Schrödinger-Strasse University of Kaiserslautern Kaiserslautern Germany

Networks-on-Chip will serve as the central integration platform in future complex SoC designs, composed of a large number of heterogeneous processing resources. Most researchers advocate the use of traditional regular networks like meshes, tori or trees as architectural templates which gained a high popularity in general-purpose parallel computing. However, most SoC platforms are special-purpose tailored to the domain-specific requirements of their application. they are usually built from a large diversity of heterogeneous components which communicate in a very specific, mostly irregular way. In this work, we propose a methodology for the design of customized irregular networks-on-chip, called INoC. We take advantage of a priori knowledge of the applications communication characteristic to generate an optimized network topology and routing algorithm. We show that customized irregular networks are clearly superior to traditional regular architectures in terms of performance at comparable implementation costs for irregular workloads. Even more, they inherently offer true scalability and expansibility which can normally not be accomplished by traditional approaches

关键词： Network topology Network-on-a-chip Routing Costs Parallel processing Design methodology Silicon computer architecture Bandwidth Electronic mail

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On Large-Scale Qualitative Spatio-Temporal Constraint Redundancy Removal

On Large-Scale Qualitative Spatio-Temporal Constraint Redund...

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International Conference on Intelligent System and Knowledge Engineering, ISKE

作者： Qiyuan Hu Yang Gao Zhiguo Long Hongjun Wang Tianrui Li Michael Sioutis School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Faculty of Information Systems and Applied Computer Sciences University of Bamberg Bamberg Germany

ISBN: (纸本)9781665405546

Qualitative spatio-temporal representation and reasoning is an important research direction in the field of artificial intelligence, and qualitative constraint networks are a key tool for representing and reasoning with spatio-temporal knowledge. Previous studies have pointed out that qualitative constraint networks often contain redundant constraints. this not only results in a waste of storage and transmission resources, but can also be a bottleneck in various algorithms and applications that are based on qualitative constraint networks. Existing research proposed several algorithms to deal with redundant constraints, but their time complexities are relatively high, which is very unfavorable when dealing with large-scale data that are common nowadays. this paper proposes a new model based on distributed computation to deal with redundant constraints efficiently. Specifically, this paper theoretically proves the feasibility of the distributed model for this problem, proposes different optimization algorithms for distributed task allocation, and evaluates the performance of the distributed model through a variety of experiments on real-world data sets. the results show that the distributed model can significantly improve the processing efficiency of simplifying representations of qualitative spatio-temporal information on large-scale data sets, and that the distributed solution to the problem of simplifying representations can also improve data privacy, which can contribute to big data processing and data security protection in qualitative spatiotemporal reasoning research.

关键词： Knowledge engineering Computational modeling Redundancy Distributed databases Data models Cognition Spatiotemporal phenomena

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Controlling Hardware Synthesis with Aspects

Controlling Hardware Synthesis with Aspects

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Euromicro symposium on Digital System Design

作者： João M.P. Cardoso Tiago Carvalho José G.F. Coutinho Pedro C. Diniz Zlatko Petrov Wayne Luk Faculdade de Engenharia (FEUP) Universidade do Porto Porto Portugal Department of Computing Imperial College London UK INESC-ID Lisboa Portugal Honeywell Sofia Bulgaria

the synthesis and mapping of applications to configurable embedded systems is a notoriously hard process. Tools have a wide range of parameters, which interact in very unpredictable ways, thus creating a large and complex design space. When exploring this space, designers must understand the interfaces to the various tools and apply, often manually, a sequence of tool-specific transformations making this an extremely cumbersome and error-prone process. this paper describes the use of aspect-oriented techniques for capturing synthesis strategies for tuning the performance of applications' kernels. We illustrate the use of this approach when designing application-specific architectures generated by a high-level synthesis tool. the results highlight the impact of the various strategies when targeting custom hardware and expose the difficulties in devising these strategies.

关键词： Hardware Optimization Software Reactive power Weaving computer architecture Programming

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DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficient Deep Learning Clusters in the Cloud

DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficien...

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IEEE/ACM International symposium on Cluster computing and the Grid (CCGRID)

作者： Yoochan Kim Kihyun Kim Yonghyeon Cho Jinwoo Kim Awais Khan Ki-Dong Kang Baik-Song An Myung-Hoon Cha Hong-Yeon Kim Youngjae Kim Dept. of Computer Science and Engineering Sogang University Seoul Republic of Korea LG Electronics Oak Ridge National Laboratory TN USA ETRI Daejeon Republic of Korea

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Distributed Deep Learning (DDL), as a paradigm, dictates the use of GPU-based clusters as the optimal infrastructure for training large-scale Deep Neural Networks (DNNs). However, the high cost of such resources makes them inaccessible to many users. Public cloud services, particularly Spot Virtual Machines (VMs), offer a cost-effective alternative, but their unpredictable availability poses a significant challenge to the crucial checkpointing process in DDL. To address this, we introduce DeepVM, a novel solution that recommends cost-effective cluster configurations by intelligently balancing the use of Spot and On-Demand VMs. DeepVM leverages a four-stage process that analyzes instance performance using the FLOPP (FLoating-point Operations Per Price) metric, performs architecture-level analysis with linear programming, and identifies the optimal configuration for the user-specific needs. Extensive simulations and real-world deployments in the AWS environment demonstrate that DeepVM consistently outperforms other policies, reducing training costs and overall makespan. By enabling cost-effective checkpointing with Spot VMs, DeepVM opens up DDL to a wider range of users and facilitates a more efficient training of complex DNNs.

关键词： Training Deep learning Checkpointing Measurement Cloud computing Costs Artificial neural networks Linear programming

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Continuous profiling: Where have all the cycles gone?

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Operating Systems Review (ACM) 1997年第5期31卷 1-14页

作者： Anderson, Jennifer M. Berc, Lance M. Dean, Jeffrey Ghemawat, Sanjay Henzinger, Monika R. Leung, Shun-Tak A. Sites, Richard L. Vandevoorde, Mark T. Waldspurger, Carl A. Weihl, William E. DIGITAL's Systems Research Cent

this paper describes the DIGITAL Continuous Profiling Infrastructure, a sampling-based profiling system designed to run continuously on production systems. the system supports multiprocessors, works on unmodified executables, and collects profiles for entire systems, including user programs, shared libraries, and the operating system kernel. Samples are collected at a high rate (over 5200 samples/sec per 333-MHz processor), yet with low overhead (1-3% slowdown for most workloads). Analysis tools supplied with the profiling system use the sample data to produce an accurate accounting, down to the level of pipeline stalls incurred by individual instructions, of where time is being spent. When instructions incur stalls, the tools identify possible reasons, such as cache misses, branch mispredictions, and functional unit contention. the fine-grained instruction-level analysis guides users and automated optimizers to the causes of performance problems and provides important insights for fixing them.

关键词： computer operating systems

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FMIP: Feature Map Importance Pruning for Efficient CNN Optimization

FMIP: Feature Map Importance Pruning for Efficient CNN Optim...

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Algorithms, computing and Artificial Intelligence (ACAI), International Conference on

作者： Ali Muhammad Shaikh Yun-bo Zhao Yu Kang Aakash Kumar University of Science and Technology of China Hefei China Zhongshan Institute of Changchun University of Science and Technology China Zhonhshan China

ISBN: (数字)9798331529314

ISBN: (纸本)9798331529321

Deep convolutional neural networks (CNNs) have reformed numerous fields, comprising computer vision and natural language processing. Nevertheless, CNNs' computational complexity frequently raises obstacles to utilization on resource-constrained devices. Different optimization strategies have been proposed in response to this issue, comprising quantization, knowledge distillation, and pruning. this paper familiarizes a novel pruning method, Feature Map Importance Pruning (FMIP), designed to optimize the performance of deep CNNs while reducing computational costs. the FMIP computes the importance of feature maps within convolutional layers according to the area of their activation values, facilitating a systematic approach to pruning decisions. By detecting and eliminating the redundant feature maps, FMIP can considerably cut the number of parameters and FLOPs in a CNN model without conceding accuracy. this is chiefly advantageous for using CNNs on devices with limited memory and computational power. We evaluated FMIP on various CNN architectures, including VGG16 and ResNet50, using datasets such as CIFAR-10 and ImageNet. Our experiments demonstrated significant model compression while preserving high accuracy. Specifically, FMIP achieved the following results: VGG16 with CIFAR-10 demonstrated an 81.9% reduction in parameters and a 48.6% reduction in FLOPs, with an accuracy of 93.33%. ResNet50 with ImageNet achieved an 83.11% reduction in parameters and a 71.55% reduction in FLOPs, with an accuracy of 92.03%.

关键词： performance evaluation Accuracy Systematics Quantization (signal) Computational modeling computer architecture Streaming media Computational efficiency Convolutional neural networks Residual neural networks

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Windsurfing with APPA: Automating Computational Fluid Dynamics Simulations of Wind Flow using Cloud computing

Windsurfing with APPA: Automating Computational Fluid Dynami...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Anshul Jindal Benedikt Strahm Vladimir Podolskiy Michael Gerndt Chair of Computer Architecture and Parallel Systems Technical University of Munich Garching (near Munich) Germany Independent Researcher Garching (near Munich) Germany

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Computational fluid dynamics (CFD) can serve as a complementary approach to conventional wind tunnel testing to assess the wind flow around tall buildings. Being a clear high performance computing (HPC) task, CFD simulations conventionally run on supercomputers and compute clusters using specialized software such as OpenFOAM. the limited availability and high maintenance costs of supercomputers and clusters force small and medium companies to search for the cost-efficient infrastructure to conduct their simulations with the appropriate performance. the on-demand offer of compute capacity by cloud service providers are well suited this task. However, engineers and researchers require extensive expertise and experience in working with cloud computing in order to benefit from running CFD simulations on a *** contribution of the paper to the outlined problem is two-fold: 1) a unique Automated Parallel Processing Application (APPA) tool that hides the cloud management details from the wind engineer and provides an intuitive user interface; 2) the estimation of the optimal number of cores (vCPUs) for virtual machine instances provided by AWS and Google Cloud based on average run time and total cost metrics for a given number of cells of a CFD-simulation. n1-highcpu-96 Google Cloud VM met both goals: low cost and low runtime per timestep. For the number of vCPUs below 16, the c4.8xlarge AWS VM type has the least runtime per timestep in all the cases. Google Cloud instances with high vCPUs are recommended to run the simulations if budget is a big concern.

关键词： Cloud computing Computational modeling Google Containers Buildings Tools Numerical models

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SMART: Serverless Module Analysis and Recognition Technique for Managed Applications

SMART: Serverless Module Analysis and Recognition Technique ...

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IEEE/ACM International symposium on Cluster computing and the Grid (CCGRID)

作者： Adi Ashkenazi Edita Grolman Aviad Elyashar Dudu Mimran Oleg Brodt Yuval Elovici Asaf Shabtai Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Cyber Labs@BGU Ben-Gurion University of the Negev Israel Department of Computer Science Shamoon College of Engineering Beer-Sheva Israel

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Serverless Function-as-a-Service (FaaS) environments enable developers to build and run cloud applications without the need to manage the underlying servers and computing infrastructure, allowing them to focus on implementing the application logic. Such environments contain numerous functions and dynamic resources, e.g., APIs and databases, making it challenging to gain insight and context of internal events i.e., recognize modules. Module in a serverless application is a set of functions and resources, that represents a functional unit that shares logical context. this paper presents SMART, a method for automatic analysis and recognition of modules for managed serverless applications. the proposed method creates an event-based graph by analyzing the standard serverless logs that document events involving the application’s functions and resources and utilizes well-known community detection algorithms (such as Louvain), with graph centrality metrics (such as degree centrality) to recognize the modules. SMART enables high-level visibility of the application’s structure and logical context which can facilitate security analysis and contribute to improved decision-making of incident response handlers, who typically do not have direct access to the application’s design and code, which can lead to challenges in fully understanding the system’s intricacies. We focused on the popular Amazon Web Services (AWS) Lambda serverless computing platform and evaluated the proposed method on three different demo applications (Airline Booking, VOD, and E-commerce). We compared SMART’s performance to four overlapping community detection algorithms and showed that it outperformed them in the task of module recognition, with a maximum improvement of 61% on the omega index metric compared to the Speaker-Listener Label Propagation algorithm. In addition, we demonstrate that the use of large language models (LLMs) with the knowledge gained by SMART can enrich security analysis insights.

关键词： Measurement Web services Serverless computing computer architecture Security Indexes Electronic commerce Detection algorithms Standards Airline industry

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