arXiv - CS - Performance最新文献_第2页

Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study 工业领域的应用联合模型个性化：比较研究

arXiv - CS - Performance Pub Date : 2024-09-10 DOI: arxiv-2409.06904

Ilias Siniosoglou, Vasileios Argyriou, George Fragulis, Panagiotis Fouliras, Georgios Th. Papadopoulos, Anastasios Lytos, Panagiotis Sarigiannidis

{"title":"Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study","authors":"Ilias Siniosoglou, Vasileios Argyriou, George Fragulis, Panagiotis Fouliras, Georgios Th. Papadopoulos, Anastasios Lytos, Panagiotis Sarigiannidis","doi":"arxiv-2409.06904","DOIUrl":"https://doi.org/arxiv-2409.06904","url":null,"abstract":"The time-consuming nature of training and deploying complicated Machine and\u0000Deep Learning (DL) models for a variety of applications continues to pose\u0000significant challenges in the field of Machine Learning (ML). These challenges\u0000are particularly pronounced in the federated domain, where optimizing models\u0000for individual nodes poses significant difficulty. Many methods have been\u0000developed to tackle this problem, aiming to reduce training expenses and time\u0000while maintaining efficient optimisation. Three suggested strategies to tackle\u0000this challenge include Active Learning, Knowledge Distillation, and Local\u0000Memorization. These methods enable the adoption of smaller models that require\u0000fewer computational resources and allow for model personalization with local\u0000insights, thereby improving the effectiveness of current models. The present\u0000study delves into the fundamental principles of these three approaches and\u0000proposes an advanced Federated Learning System that utilises different\u0000Personalisation methods towards improving the accuracy of AI models and\u0000enhancing user experience in real-time NG-IoT applications, investigating the\u0000efficacy of these techniques in the local and federated domain. The results of\u0000the original and optimised models are then compared in both local and federated\u0000contexts using a comparison analysis. The post-analysis shows encouraging\u0000outcomes when it comes to optimising and personalising the models with the\u0000suggested techniques.","PeriodicalId":501291,"journal":{"name":"arXiv - CS - Performance","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195452","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

A Comprehensive Analysis of Process Energy Consumption on Multi-Socket Systems with GPUs 全面分析带 GPU 的多插槽系统的过程能耗

arXiv - CS - Performance Pub Date : 2024-09-08 DOI: arxiv-2409.04941

Luis G. León-Vega, Niccolò Tosato, Stefano Cozzini

{"title":"A Comprehensive Analysis of Process Energy Consumption on Multi-Socket Systems with GPUs","authors":"Luis G. León-Vega, Niccolò Tosato, Stefano Cozzini","doi":"arxiv-2409.04941","DOIUrl":"https://doi.org/arxiv-2409.04941","url":null,"abstract":"Robustly estimating energy consumption in High-Performance Computing (HPC) is\u0000essential for assessing the energy footprint of modern workloads, particularly\u0000in fields such as Artificial Intelligence (AI) research, development, and\u0000deployment. The extensive use of supercomputers for AI training has heightened\u0000concerns about energy consumption and carbon emissions. Existing energy\u0000estimation tools often assume exclusive use of computing nodes, a premise that\u0000becomes problematic with the advent of supercomputers integrating\u0000microservices, as seen in initiatives like Acceleration as a Service (XaaS) and\u0000cloud computing. This work investigates the impact of executed instructions on overall power\u0000consumption, providing insights into the comprehensive behaviour of HPC\u0000systems. We introduce two novel mathematical models to estimate a process's\u0000energy consumption based on the total node energy, process usage, and a\u0000normalised vector of the probability distribution of instruction types for CPU\u0000and GPU processes. Our approach enables energy accounting for specific\u0000processes without the need for isolation. Our models demonstrate high accuracy, predicting CPU power consumption with a\u0000mere 1.9% error. For GPU predictions, the models achieve a central relative\u0000error of 9.7%, showing a clear tendency to fit the test data accurately. These\u0000results pave the way for new tools to measure and account for energy\u0000consumption in shared supercomputing environments.","PeriodicalId":501291,"journal":{"name":"arXiv - CS - Performance","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195453","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

Confidential Computing on nVIDIA H100 GPU: A Performance Benchmark Study 利用 nVIDIA H100 GPU 进行机密计算：性能基准研究

arXiv - CS - Performance Pub Date : 2024-09-06 DOI: arxiv-2409.03992

Jianwei Zhu, Hang Yin, Shunfan Zhou

引用次数: 0

Challenging Portability Paradigms: FPGA Acceleration Using SYCL and OpenCL 挑战可移植性范式：使用 SYCL 和 OpenCL 进行 FPGA 加速

arXiv - CS - Performance Pub Date : 2024-09-05 DOI: arxiv-2409.03391

Manuel de Castro, Francisco J. andújar, Roberto R. Osorio, Rocío Carratalá-Sáez, Diego R. Llanos

{"title":"Challenging Portability Paradigms: FPGA Acceleration Using SYCL and OpenCL","authors":"Manuel de Castro, Francisco J. andújar, Roberto R. Osorio, Rocío Carratalá-Sáez, Diego R. Llanos","doi":"arxiv-2409.03391","DOIUrl":"https://doi.org/arxiv-2409.03391","url":null,"abstract":"As the interest in FPGA-based accelerators for HPC applications increases,\u0000new challenges also arise, especially concerning different programming and\u0000portability issues. This paper aims to provide a snapshot of the current state\u0000of the FPGA tooling and its problems. To do so, we evaluate the performance\u0000portability of two frameworks for developing FPGA solutions for HPC (SYCL and\u0000OpenCL) when using them to port a highly-parallel application to FPGAs, using\u0000both ND-range and single-task type of kernels. The developer's general recommendation when using FPGAs is to develop\u0000single-task kernels for them, as they are commonly regarded as more suited for\u0000such hardware. However, we discovered that, when using high-level approaches\u0000such as OpenCL and SYCL to program a highly-parallel application with no\u0000FPGA-tailored optimizations, ND-range kernels significantly outperform\u0000single-task codes. Specifically, while SYCL struggles to produce efficient FPGA\u0000implementations of applications described as single-task codes, its performance\u0000excels with ND-range kernels, a result that was unexpectedly favorable.","PeriodicalId":501291,"journal":{"name":"arXiv - CS - Performance","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195454","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

Application Research On Real-Time Perception Of Device Performance Status 实时感知设备性能状态的应用研究

arXiv - CS - Performance Pub Date : 2024-09-05 DOI: arxiv-2409.03218

Zhe Wang, Zhen Wang, Jianwen Wu, Wangzhong Xiao, Yidong Chen, Zihua Feng, Dian Yang, Hongchen Liu, Bo Liang, Jiaojiao Fu

{"title":"Application Research On Real-Time Perception Of Device Performance Status","authors":"Zhe Wang, Zhen Wang, Jianwen Wu, Wangzhong Xiao, Yidong Chen, Zihua Feng, Dian Yang, Hongchen Liu, Bo Liang, Jiaojiao Fu","doi":"arxiv-2409.03218","DOIUrl":"https://doi.org/arxiv-2409.03218","url":null,"abstract":"In order to accurately identify the performance status of mobile devices and\u0000finely adjust the user experience, a real-time performance perception\u0000evaluation method based on TOPSIS (Technique for Order Preference by Similarity\u0000to Ideal Solution) combined with entropy weighting method and time series model\u0000construction was studied. After collecting the performance characteristics of\u0000various mobile devices, the device performance profile was fitted by using PCA\u0000(principal component analysis) dimensionality reduction and feature engineering\u0000methods such as descriptive time series analysis. The ability of performance\u0000features and profiles to describe the real-time performance status of devices\u0000was understood and studied by applying the TOPSIS method and multi-level\u0000weighting processing. A time series model was constructed for the feature set\u0000under objective weighting, and multiple sensitivity (real-time, short-term,\u0000long-term) performance status perception results were provided to obtain\u0000real-time performance evaluation data and long-term stable performance\u0000prediction data. Finally, by configuring dynamic AB experiments and overlaying\u0000fine-grained power reduction strategies, the usability of the method was\u0000verified, and the accuracy of device performance status identification and\u0000prediction was compared with the performance of the profile features including\u0000dimensionality reduction time series modeling, TOPSIS method and entropy\u0000weighting method, subjective weighting, HMA method. The results show that\u0000accurate real-time performance perception results can greatly enhance business\u0000value, and this research has application effectiveness and certain\u0000forward-looking significance.","PeriodicalId":501291,"journal":{"name":"arXiv - CS - Performance","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195472","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

Towards a Scalable and Efficient PGAS-based Distributed OpenMP 实现基于 PGAS 的可扩展高效分布式 OpenMP

arXiv - CS - Performance Pub Date : 2024-09-04 DOI: arxiv-2409.02830

Baodi Shan, Mauricio Araya-Polo, Barbara Chapman

{"title":"Towards a Scalable and Efficient PGAS-based Distributed OpenMP","authors":"Baodi Shan, Mauricio Araya-Polo, Barbara Chapman","doi":"arxiv-2409.02830","DOIUrl":"https://doi.org/arxiv-2409.02830","url":null,"abstract":"MPI+X has been the de facto standard for distributed memory parallel\u0000programming. It is widely used primarily as an explicit two-sided communication\u0000model, which often leads to complex and error-prone code. Alternatively, PGAS\u0000model utilizes efficient one-sided communication and more intuitive\u0000communication primitives. In this paper, we present a novel approach that\u0000integrates PGAS concepts into the OpenMP programming model, leveraging the LLVM\u0000compiler infrastructure and the GASNet-EX communication library. Our model\u0000addresses the complexity associated with traditional MPI+OpenMP programming\u0000models while ensuring excellent performance and scalability. We evaluate our\u0000approach using a set of micro-benchmarks and application kernels on two\u0000distinct platforms: Ookami from Stony Brook University and NERSC Perlmutter.\u0000The results demonstrate that DiOMP achieves superior bandwidth and lower\u0000latency compared to MPI+OpenMP, up to 25% higher bandwidth and down to 45% on\u0000latency. DiOMP offers a promising alternative to the traditional MPI+OpenMP\u0000hybrid programming model, towards providing a more productive and efficient way\u0000to develop high-performance parallel applications for distributed memory\u0000systems.","PeriodicalId":501291,"journal":{"name":"arXiv - CS - Performance","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195455","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

ISO: Overlap of Computation and Communication within Seqenence For LLM Inference ISO：用于 LLM 推断的序列内计算与通信的重叠

arXiv - CS - Performance Pub Date : 2024-09-04 DOI: arxiv-2409.11155

Bin Xiao, Lei Su

{"title":"ISO: Overlap of Computation and Communication within Seqenence For LLM Inference","authors":"Bin Xiao, Lei Su","doi":"arxiv-2409.11155","DOIUrl":"https://doi.org/arxiv-2409.11155","url":null,"abstract":"In the realm of Large Language Model (LLM) inference, the inherent structure\u0000of transformer models coupled with the multi-GPU tensor parallelism strategy\u0000leads to a sequential execution of computation and communication. This results\u0000in substantial underutilization of computing resources during the communication\u0000phase. To mitigate this inefficiency, various techniques have been developed to\u0000optimize the use of computational power throughout the communication process.\u0000These strategies primarily involve overlapping matrix computations and\u0000communications, as well as interleaving micro-batches across different\u0000requests. Nonetheless, these approaches either fall short of achieving ideal\u0000overlap or impose certain limitations on their application. To overcome these\u0000challenges, this paper introduces a novel strategy for\u0000computation-communication overlap that operates at the sequence level. This\u0000method not only enhances the degree of overlap but also minimizes the\u0000constraints on its applicability. Experimental evaluations conducted using\u000030b/70b models have demonstrated significant improvements in efficiency.\u0000Specifically, the proposed technique has been shown to reduce time consumption\u0000by approximately 35% on 4090 GPU and by roughly 15% on A800 GPU during the\u0000prefill stage of LLM inference.","PeriodicalId":501291,"journal":{"name":"arXiv - CS - Performance","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256007","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

Scaler: Efficient and Effective Cross Flow Analysis Scaler：高效和有效的横流分析

arXiv - CS - Performance Pub Date : 2024-09-01 DOI: arxiv-2409.00854

StevenJiaxun, Tang, Mingcan Xiang, Yang Wang, Bo Wu, Jianjun Chen, Tongping Liu

引用次数: 0

CASA: A Framework for SLO and Carbon-Aware Autoscaling and Scheduling in Serverless Cloud Computing CASA：无服务器云计算中的 SLO 和碳感知自动扩展与调度框架

arXiv - CS - Performance Pub Date : 2024-08-31 DOI: arxiv-2409.00550

S. Qi, H. Moore, N. Hogade, D. Milojicic, C. Bash, S. Pasricha

{"title":"CASA: A Framework for SLO and Carbon-Aware Autoscaling and Scheduling in Serverless Cloud Computing","authors":"S. Qi, H. Moore, N. Hogade, D. Milojicic, C. Bash, S. Pasricha","doi":"arxiv-2409.00550","DOIUrl":"https://doi.org/arxiv-2409.00550","url":null,"abstract":"Serverless computing is an emerging cloud computing paradigm that can reduce\u0000costs for cloud providers and their customers. However, serverless cloud\u0000platforms have stringent performance requirements (due to the need to execute\u0000short duration functions in a timely manner) and a growing carbon footprint.\u0000Traditional carbon-reducing techniques such as shutting down idle containers\u0000can reduce performance by increasing cold-start latencies of containers\u0000required in the future. This can cause higher violation rates of service level\u0000objectives (SLOs). Conversely, traditional latency-reduction approaches of\u0000prewarming containers or keeping them alive when not in use can improve\u0000performance but increase the associated carbon footprint of the serverless\u0000cluster platform. To strike a balance between sustainability and performance,\u0000in this paper, we propose a novel carbon- and SLO-aware framework called CASA\u0000to schedule and autoscale containers in a serverless cloud computing cluster.\u0000Experimental results indicate that CASA reduces the operational carbon\u0000footprint of a FaaS cluster by up to 2.6x while also reducing the SLO violation\u0000rate by up to 1.4x compared to the state-of-the-art.","PeriodicalId":501291,"journal":{"name":"arXiv - CS - Performance","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195458","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

Application-Driven Exascale: The JUPITER Benchmark Suite 应用驱动的超大规模：JUPITER 基准套件

arXiv - CS - Performance Pub Date : 2024-08-30 DOI: arxiv-2408.17211

Andreas Herten, Sebastian Achilles, Damian Alvarez, Jayesh Badwaik, Eric Behle, Mathis Bode, Thomas Breuer, Daniel Caviedes-Voullième, Mehdi Cherti, Adel Dabah, Salem El Sayed, Wolfgang Frings, Ana Gonzalez-Nicolas, Eric B. Gregory, Kaveh Haghighi Mood, Thorsten Hater, Jenia Jitsev, Chelsea Maria John, Jan H. Meinke, Catrin I. Meyer, Pavel Mezentsev, Jan-Oliver Mirus, Stepan Nassyr, Carolin Penke, Manoel Römmer, Ujjwal Sinha, Benedikt von St. Vieth, Olaf Stein, Estela Suarez, Dennis Willsch, Ilya Zhukov

{"title":"Application-Driven Exascale: The JUPITER Benchmark Suite","authors":"Andreas Herten, Sebastian Achilles, Damian Alvarez, Jayesh Badwaik, Eric Behle, Mathis Bode, Thomas Breuer, Daniel Caviedes-Voullième, Mehdi Cherti, Adel Dabah, Salem El Sayed, Wolfgang Frings, Ana Gonzalez-Nicolas, Eric B. Gregory, Kaveh Haghighi Mood, Thorsten Hater, Jenia Jitsev, Chelsea Maria John, Jan H. Meinke, Catrin I. Meyer, Pavel Mezentsev, Jan-Oliver Mirus, Stepan Nassyr, Carolin Penke, Manoel Römmer, Ujjwal Sinha, Benedikt von St. Vieth, Olaf Stein, Estela Suarez, Dennis Willsch, Ilya Zhukov","doi":"arxiv-2408.17211","DOIUrl":"https://doi.org/arxiv-2408.17211","url":null,"abstract":"Benchmarks are essential in the design of modern HPC installations, as they\u0000define key aspects of system components. Beyond synthetic workloads, it is\u0000crucial to include real applications that represent user requirements into\u0000benchmark suites, to guarantee high usability and widespread adoption of a new\u0000system. Given the significant investments in leadership-class supercomputers of\u0000the exascale era, this is even more important and necessitates alignment with a\u0000vision of Open Science and reproducibility. In this work, we present the\u0000JUPITER Benchmark Suite, which incorporates 16 applications from various\u0000domains. It was designed for and used in the procurement of JUPITER, the first\u0000European exascale supercomputer. We identify requirements and challenges and\u0000outline the project and software infrastructure setup. We provide descriptions\u0000and scalability studies of selected applications and a set of key takeaways.\u0000The JUPITER Benchmark Suite is released as open source software with this work\u0000at https://github.com/FZJ-JSC/jubench.","PeriodicalId":501291,"journal":{"name":"arXiv - CS - Performance","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195459","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