2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)最新文献_第4页

Multi-Cloud Container Orchestration for High-Performance Real-Time Online Applications 用于高性能实时在线应用程序的多云容器编排

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00054

Sezar Jarrous-Holtrup, S. Gorlatch, Michael Dey, Folker Schamel

{"title":"Multi-Cloud Container Orchestration for High-Performance Real-Time Online Applications","authors":"Sezar Jarrous-Holtrup, S. Gorlatch, Michael Dey, Folker Schamel","doi":"10.1109/PDP59025.2023.00054","DOIUrl":"https://doi.org/10.1109/PDP59025.2023.00054","url":null,"abstract":"We develop a novel multi-cloud container orchestration architecture for high-performance Real-Time Online Interactive Applications (ROIA), with use cases including product configurators, multiplayer online gaming, e-learning and - training. Running the core components of ROIA, e.g., real-time 3D rendering, on a multi-cloud enables access to high-performance resources and prevents proprietary ‘vendor lock-in’. Our container orchestration facilitates: (1) strict Quality of Service (QoS) requirements, (2) secure communication between cluster nodes from different clouds, (3) automatic scalability, and (4) resource usage optimization. We improve previous work by using session slots that set a limit on the number of concurrent user sessions for a service instance without loss of QoS. Our implementation provides a vendor-independent, OpenVPN-based interconnection between cloud nodes, both Linux and Windows, possibly located in different LANs of a multi-cloud. We experimentally evaluate our orchestration approach on a Kubernetes-based cluster using a prototype of an interactive car configurator.","PeriodicalId":153500,"journal":{"name":"2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116205566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Summarizing task-based applications behavior over many nodes through progression clustering 通过进程聚类总结基于任务的应用程序在多个节点上的行为

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00014

Lucas Leandro Nesi, V. G. Pinto, L. Schnorr, Arnaud Legrand

{"title":"Summarizing task-based applications behavior over many nodes through progression clustering","authors":"Lucas Leandro Nesi, V. G. Pinto, L. Schnorr, Arnaud Legrand","doi":"10.1109/PDP59025.2023.00014","DOIUrl":"https://doi.org/10.1109/PDP59025.2023.00014","url":null,"abstract":"Visualization strategies are a valuable tool in the performance evaluation of HPC applications. Although the traditional Gantt charts are a widespread and enlightening strategy, it presents scalability problems and may misguide the analysis by focusing on resource utilization alone. This paper proposes an overview strategy to indicate nodes of interest for further investigation with classical visualizations like Gantt charts. For this, it uses a progression metric that captures work done per node inferred from the task-based structure, a time-step clustering of those metrics to decrease redundant information, and a more scalable visualization technique. We demonstrate with six scenarios and two applications that such a strategy can indicate problematic nodes more straightforwardly while using the same visualization space. Also, we provide examples where it correctly captures application work progression, showing application problems earlier and as an easy way to compare nodes. At the same time that traditional methods are misleading.","PeriodicalId":153500,"journal":{"name":"2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124129725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust feature selection for high-dimensional datasets using a GPU-accelerated ensemble of cooperative coevolutionary optimizers 基于gpu加速协同进化优化器的高维数据集鲁棒特征选择

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00052

Marjan Firouznia, Pietro Ruiu, G. Trunfio

引用次数: 0

Pooling critical datasets with Federated Learning 使用联邦学习池化关键数据集

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00057

Yasir Arfat, Gianluca Mittone, Iacopo Colonnelli, Fabrizio D'Ascenzo, Roberto Esposito, Marco Aldinucci

引用次数: 3

Performance Analysis and Optimization of the CUDA Implementation of the Three-Dimensional Subsurface XCA-Flow Cellular Automaton 三维地下xca流元胞自动机CUDA实现的性能分析与优化

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00048

A. De Rango, Luca Furnari, A. Senatore, G. Mendicino, Andrea Giordano, David Macri, G. Utrera, Donato D'Ambrosio

{"title":"Performance Analysis and Optimization of the CUDA Implementation of the Three-Dimensional Subsurface XCA-Flow Cellular Automaton","authors":"A. De Rango, Luca Furnari, A. Senatore, G. Mendicino, Andrea Giordano, David Macri, G. Utrera, Donato D'Ambrosio","doi":"10.1109/PDP59025.2023.00048","DOIUrl":"https://doi.org/10.1109/PDP59025.2023.00048","url":null,"abstract":"We present the results of a performance assessment and optimisation work regarding the CUDA implementation of the three-dimensional XCA-Flow subsurface Extended Cellular Automata model. To this end, we have considered a ten days long simulation already considered in previous works, characterised by a constant infiltration rate and a heterogeneous hydraulic conductivity field, as the benchmark. We ran the experiments on the Nvidia V100 high-performance many-core device. We have analysed essential aspects of the XCA-Flow model by updating its kernels. We applied classical tiling/shared memory techniques to the stencil-based and reduction kernels in the first step. Results suggested applying a thorough analysis of the model. Both theoretical and experimental assessments have driven this analysis, which has pointed out the need to increase the achieved warp occupancy to speed up the computation. The resulting general redesign of the application allowed for a 20.3% mean performance gain (over the CUDA block configurations considered). We also performed two Roofline analyses to characterise the kernels of the original and improved implementations in terms of arithmetic intensity and performance. Besides the improved performance, we have obtained meaningful insights about the CUDA implementation of the XCA-Flow model that could, in principle, allow for further optimisations.","PeriodicalId":153500,"journal":{"name":"2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124244156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High performance deep learning libraries for biomedical applications 用于生物医学应用的高性能深度学习库

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00049

Luca Barillaro, Giuseppe Agapito, M. Cannataro

{"title":"High performance deep learning libraries for biomedical applications","authors":"Luca Barillaro, Giuseppe Agapito, M. Cannataro","doi":"10.1109/PDP59025.2023.00049","DOIUrl":"https://doi.org/10.1109/PDP59025.2023.00049","url":null,"abstract":"Deep learning approaches are a topic of growing interest since they can achieve high precision in machine learning tasks and may be useful in several scenarios, while high performance computing (HPC) is one of the driving factors for deep learning applications since they require massive computational power. One of these scenarios is related to biomedical context since the massive growth of data generated by several medical procedures. Deep learning techniques, applied on these data may be useful both for medical procedures and for further knowledge discovery in specific field (in example gene interaction related to some diseases). Therefore the importance to have a deep learning library tailored for these task is evident. This paper aims to discuss about some libraries specifically designed to provide convenient high performance computing oriented deep learning support to biomedical applications. We describe two libraries developed inside a European project, named the Deep Health Project, to support both deep learning basic operations and computer vision tasks, oriented to a distributed computing fashion and with some special features for managing biomedical data. In addition we highlight some differences and comparisons with popular environments like Keras and Tensorflow by describing a simple use case.","PeriodicalId":153500,"journal":{"name":"2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127536036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FSP: a Framework for Data Stream Processing Applications targeting FPGAs FSP:针对fpga的数据流处理应用框架

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00021

Alberto Ottimo, G. Mencagli, M. Danelutto

引用次数: 0

Using Edge-based Deep Learning Model for Early Detection of Cancer 基于边缘的深度学习模型用于癌症的早期检测

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00046

Luca Barillaro, Giuseppe Agapito, M. Cannataro

{"title":"Using Edge-based Deep Learning Model for Early Detection of Cancer","authors":"Luca Barillaro, Giuseppe Agapito, M. Cannataro","doi":"10.1109/PDP59025.2023.00046","DOIUrl":"https://doi.org/10.1109/PDP59025.2023.00046","url":null,"abstract":"Cancer is one of the most frequent causes of death in the world. Usually, cancer can be easily diagnosed if characteristic symptoms occur. However, many people who are suffering from cancer have no symptoms. Early diagnosis of tumors is essential to contrast their progression, helping to define more effective treatments to provide long-term survival. Early cancer detection is effective if sensible data can be investigated through high-performance technologies like edge computing. Edge computing is a new paradigm for analyzing data as close to the source as possible, avoiding exporting them outside. Hence, edge-based deep learning models can be applied to improve early cancer detection. This paper provides an use case of a classification task on tumor-related data based on the famous UCI machine learning data sets repository using a deep learning approach based on edge computing. In addition, the manuscript provides an overview of the edge computing paradigm, highlighting its advantages and usability. We also described a small experiment with real tumor data to characterize performance considerations. Moreover, the presented model can be used with different data types, such as images, EGC, and ECC signals.","PeriodicalId":153500,"journal":{"name":"2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133030668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revisiting self-adaptation for efficient decision-making at run-time in parallel executions 重新审视自适应，以便在并行执行的运行时进行有效决策

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00015

Adriano Vogel, M. Danelutto, Dalvan Griebler, L. G. Fernandes

{"title":"Revisiting self-adaptation for efficient decision-making at run-time in parallel executions","authors":"Adriano Vogel, M. Danelutto, Dalvan Griebler, L. G. Fernandes","doi":"10.1109/PDP59025.2023.00015","DOIUrl":"https://doi.org/10.1109/PDP59025.2023.00015","url":null,"abstract":"Self-adaptation is a potential alternative to provide a higher level of autonomic abstractions and run-time responsiveness in parallel executions. However, the recurrent problem is that self-adaptation is still limited in flexibility and efficiency. For instance, there is a lack of mechanisms to apply adaptation actions and efficient decision-making strategies to decide which configurations should be conveniently enforced at run-time. In this work, we are interested in providing and evaluating potential abstractions achievable with self-adaptation transparently managing parallel executions. Therefore, we provide a new mechanism to support self-adaptation in applications with multiple parallel stages executed in multi-cores. Moreover, we reproduce, reimplement, and evaluate an existing decision-making strategy in our scenario. The observations from the results show that the proposed mechanism for self-adaptation can provide new parallelism abstractions and autonomous responsiveness at run-time. On the other hand, there is a need for more accurate decision-making strategies to enable efficient executions of applications in resource-constrained scenarios like multi-cores.","PeriodicalId":153500,"journal":{"name":"2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)","volume":"374 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126168391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stratus: A Hardware/Software Infrastructure for Controlled Cloud Research Stratus:用于受控云研究的硬件/软件基础设施

2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) Pub Date : 2023-03-01 DOI: 10.1109/PDP59025.2023.00053

Lucía Pons, S. Petit, Julio Pons, M. E. Gómez, Chaoyi Huang, J. Sahuquillo

{"title":"Stratus: A Hardware/Software Infrastructure for Controlled Cloud Research","authors":"Lucía Pons, S. Petit, Julio Pons, M. E. Gómez, Chaoyi Huang, J. Sahuquillo","doi":"10.1109/PDP59025.2023.00053","DOIUrl":"https://doi.org/10.1109/PDP59025.2023.00053","url":null,"abstract":"Cloud systems deploy a wide variety of shared resources and host a large number of tenant applications. To perform cloud research, a small experimental platform is commonly used, which hides the huge system complexity and provides flexibility. Despite being simpler, this platform should include the main cloud system components (hardware and software) to provide representative results. A wide set of platforms have spread in recent years; however, most of them only include a major cloud component or lack the deployment of virtual machines (VMs) to provide isolation. This paper presents Stratus, an experimental platform that is currently being used to carry out cloud research. To the best of our knowledge, Stratus is the only platform that jointly provides three main features: uses VMs to isolate tenant applications, deploys the three types of cloud nodes (server, client, and storage), and manages all main shared system resources (CPUs, LLC space, memory, network, and disk bandwidth). Moreover, Stratus implements a software manager to ease the research and aid the design of QoS-aware policies. The manager integrates three main functionalities: management and control of the execution of VMs and running applications, monitoring of hardware performance counters and system resource utilization, and partitioning of the main shared system resources by using technologies available in commercial processors.","PeriodicalId":153500,"journal":{"name":"2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129507114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0