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arXiv 2025年

作者： Hunhold, Laslo Parallel and Distributed Systems Group University of Cologne Cologne Germany

Modern microprocessors extend their instruction set architecture (ISA) with Single Instruction, Multiple Data (SIMD) operations to improve performance. The Intel Advanced Vector Extensions (AVX) enhance the x86 ISA and are widely supported in Intel and AMD processors. The latest version, AVX10.2, places a strong emphasis on low-precision, non-standard floating-point formats, including bfloat16 and E4M3/E5M2 float8 (OCP 8-bit Floating Point, OFP8), primarily catering to deep learning applications rather than general-purpose arithmetic. However, as these formats remain within the IEEE 754 framework, they inherit its limitations, introducing inconsistencies and added complexity into the ISA. This paper examines the recently proposed tapered-precision takum floating-point format, which has been shown to offer significant advantages over IEEE 754 and its derivatives as a general-purpose number format. Using AVX10.2 as a case study, the paper explores the potential benefits of replacing the multitude of floating-point formats with takum as a uniform basis. The results indicate a more consistent instruction set, improving readability and flexibility while offering potential for 8- and 16-bit general-purpose SIMD arithmetic. © 2025, CC BY.

关键词： Deep learning

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Towards Smarter Maritime Security: Apriori Algorithm Optimization in Serverless Environments

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Procedia Computer Science 2025年 257卷 452-459页

作者： Mahtab Shahin Saeed Rahimpour Tara Ghasempouri Sanja Bauk Thomas Fahringer Dirk Draheim Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems.

关键词： Cloud-Native Architectures Serverless Computing Data Mining Apriori Algorithm distributed Computing Big Data Maritime

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SmartKV: A cost-effective and low-latency geo-distributed key-value store for the computing continuum

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Future Generation Computer systems 2025年 171卷

作者： Aznar Poveda, Juan Ebner, Maximilian Franz Fahringer, Thomas Najafabadi Samani, Zahra Etheredge, Marlon Pedratscher, Stefan Saurabh, Nishant Distributed and Parallel Systems Group University of Innsbruck Technikerstraße 21a Innsbruck6020 Austria Department of Information and Computing Sciences Utrecht University Princetonplein 5 Utrecht3584 Netherlands

Many data-intensive and distributed applications rely on low-latency and scalable key–value storage systems across the Computing Continuum. Key–value storage systems typically use consistent hashing or hash slot-sharding mechanisms to distribute data across storage nodes, which ensures load balancing but often leads to sub-optimal response times and monetary costs, particularly in geo-distributed systems where nodes might have different unit prices and be widely dispersed. In this paper, we propose SmartKV, a cost-efficient geo-distributed key–value store that optimizes data placement dynamically, abstracting the intricacies of data organization, transfer, access, and processing. SmartKV integrates a decentralized data placement algorithm that optimizes the replication factor and selects suitable locations for key–value pairs and replicas, balancing cost and access latency while keeping optimization overhead low. We employ a realistic cost model based on public and private Cloud and Edge providers that consider data transfer, request, and storage costs. In addition to conventional key–value pairs, SmartKV supports active key–value pairs, which enable the definition of custom data types and the execution of user-defined functions directly on the storage side. This contributes to reducing data transfer costs and round-trip times. We thoroughly evaluate SmartKV across different regions of the Chameleon testbed using several realistic workloads. Results show that the utilized decentralized data placement strategy allows SmartKV to reduce round trip times between 9 and 84% while reducing costs up to 4.84× under different client workloads and consistency models compared to state-of-the-art data placement strategies. © 2025 The Authors

关键词： Cloud storage

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CTXNL: A Software-Hardware Co-designed Solution for Efficient CXL-Based Transaction Processing 25

CTXNL: A Software-Hardware Co-designed Solution for Efficien...

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30th ACM International Conference on Architectural Support for Programming Languages and Operating systems, ASPLOS 2025

作者： Wang, Zhao Chen, Yiqi Li, Cong Guan, Yijin Niu, Dimin Guan, Tianchan Du, Zhaoyang Wei, Xingda Sun, Guangyu School of Integrated Circuits School of Computer Science Peking University Beijing China Peking University School of Integrated Circuits Beijing China Damo Academy Alibaba Group Hupan Lab Hangzhou China Shanghai Jiao Tong University Institute of Parallel and Distributed Systems Seiee Shanghai China School of Integrated Circuits Beijing Advanced Innovation Center for Integrated Circuits Peking University Beijing China

ISBN: (纸本)9798400710797

Transaction processing systems are the crux for modern data-center applications, yet current multi-node systems are slow due to network overheads. This paper advocates for Compute Express Link (CXL) as a network alternative, which enables low-latency and cache-coherent shared memory accesses. However, directly adopting standard CXL primitives leads to performance degradation due to the high cost of maintaining cross-node cache coherence. To address the CXL challenges, this paper introduces CTXNL, a software-hardware co-designed system that implements a novel hybrid coherence primitive tailored to the loosely coherent nature of transactional data. The core innovation of CTXNL is empowering transaction system developers with the ability to selectively achieve data coherence. Our evaluations on OLTP workloads demonstrate that CTXNL enhances performance, outperforming current network-based systems and achieves up to 2.08x greater throughput than vanilla CXL memory sharing architectures across universal transaction processing policies. © 2025 ACM.

关键词： Cache memory

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PreMatch: A Prediction-based Scheduler for Dataflow Asynchronous Processing on the Computing Continuum 25

PreMatch: A Prediction-based Scheduler for Dataflow Asynchro...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

作者： Narges Mehran Zahra Najafabadi Samani Samira Afzal Radu Prodan Frank Pallas Peter Dorfinger Intelligent Connectivity Group Salzburg Research Forschungsgesellschaft mbH Salzburg Salzburg Austria Faculty of Digital and Analytical Sciences Paris Lodron University of Salzburg Salzburg Salzburg Austria Distributed and Parallel Systems Group University of Innsbruck Innsbruck Tyrol Austria Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Overijssel Netherlands Institute of Information Technology Alpen-Adria-University of Klagenfurt Klagenfurt Carinthia Austria

ISBN: (纸本)9798400706295

The popularity of asynchronous data exchange patterns has recently increased, as evidenced by 23% of the communication between microservices in an Alibaba trace analysis. Such workloads necessitate methods for reducing dataflow processing and completion time by forecasting the future requirements of their microservices and (re-)scheduling them. Therefore, we investigate a prediction-based scheduling method of asynchronous dataflow processing applications by considering the stochastic changes due to dynamic user requirements. We present a microservice scaling and scheduling method named PreMatch combining a machine learning (ML) prediction strategy based on gradient boosting with ranking and game theory matching scheduling principles. Firstly, PreMatch predicts the number of microservice replicas, and then, the ranking method orders the microservice replicas and devices based on microservice and transmission times. Thereafter, the PreMatch schedules microservice replicas requiring dataflow processing on computing devices. Experimental analysis of the PreMatch method shows lower completion times on average 13% compared to a related prediction-based scheduling method.

关键词： microservice

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Presenting scientific legacy programs as grid services via program synthesis

Presenting scientific legacy programs as grid services via p...

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e-Science 2006 - 2nd IEEE International Conference on e-Science and Grid Computing

作者： Hofer, Jürgen Fahringer, Thomas Distributed and Parallel Systems Group University of Innsbruck Austria

ISBN: (纸本)0769527345

Extraordinary long lifecycles of many scientific applications commonly surpass multiple generations of Grid technologies. Therefore the smooth adaptation and migration to newer environments remains as interesting research question. This paper presents the Otho Toolkit for synthesis of application-specific Grid service wrappers based on specifications of scientific legacy programs. The services are customised and tailor-made for a specific application, service hosting environment and computational infrastructure and include source code for optional manual refinement. We demonstrate its unique combination of advanced features like support for multiple service platforms, parameter sweeping, iterative and parallel programs, progress-reporting, filestaging and security credential management. Moreover our services reliably identify program termination-causes based on programmatically evaluated post-mortem program states. We applied the Otho Toolkit recursively to itself to synthesise a sophisticated Factory service that creates application-specific Grid services on-demand. © 2006 IEEE.

关键词： Grid computing

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An auto-tuning solution to data streams clustering in OpenCL

An auto-tuning solution to data streams clustering in OpenCL

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14th IEEE Int. Conf. on Computational Science and Engineering, CSE 2011, the 11th International Symposium on Pervasive systems, Algorithms, and Networks, I-SPAN 2011, and the 10th IEEE Int. Conf. on Ubiquitous Computing and Communications, IUCC 2011

作者： Fang, Jianbin Varbanescu, Ana Lucia Sips, Henk Parallel and Distributed Systems Group Delft University of Technology Delft Netherlands

ISBN: (纸本)9780769544779

Due to its applicability to numerous types of data, including telephone records, web documents, and click streams, the data stream model has recently attracted attention. For analysis of such data, it is crucial to process the data in a single pass, or a small number of passes, using little memory. This paper provides an OpenCL implementation for data streams clustering, and then presents several optimizations for it, which make it more efficient in terms of memory usage. In order to maximize performance for different problem sizes and architectures, we also propose an auto-tuning solution. Experimental results show that our fully optimized implementation can perform 2.1x and 1.4x faster than the native OpenCL implementation on NVIDIA GTX480 and AMD HD5870, respectively, it can also achieve 1.4x to 3.3x speedup relative to the original CUDA implementation solution on GTX480. © 2011 IEEE.

关键词： Auto-tuning Clustering Data Streams OpenCL Performance Optimizations

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A comprehensive performance comparison of CUDA and OpenCL

A comprehensive performance comparison of CUDA and OpenCL

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40th International Conference on parallel Processing, ICPP 2011

作者： Fang, Jianbin Varbanescu, Ana Lucia Sips, Henk Parallel and Distributed Systems Group Delft University of Technology Delft Netherlands

ISBN: (纸本)9780769545103

This paper presents a comprehensive performance comparison between CUDA and OpenCL. We have selected 16 benchmarks ranging from synthetic applications to real-world ones. We make an extensive analysis of the performance gaps taking into account programming models, optimization strategies, architectural details, and underlying compilers. Our results show that, for most applications, CUDA performs at most 30% better than OpenCL. We also show that this difference is due to unfair comparisons: in fact, OpenCL can achieve similar performance to CUDA under a fair comparison. Therefore, we define a fair comparison of the two types of applications, providing guidelines for more potential analyses. We also investigate OpenCL's portability by running the benchmarks on other prevailing platforms with minor modifications. Overall, we conclude that OpenCL's portability does not fundamentally affect its performance, and OpenCL can be a good alternative to CUDA. © 2011 IEEE.

关键词： Benchmarking

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Automatic OpenMP loop scheduling: A combined compiler and runtime approach

Automatic OpenMP loop scheduling: A combined compiler and ru...

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8th International Workshop on OpenMP, IWOMP 2012

作者： Thoman, Peter Jordan, Herbert Pellegrini, Simone Fahringer, Thomas University of Innsbruck Distributed and Parallel Systems Group Innsbruck A6020 Austria

ISBN: (纸本)9783642309601

The scheduling of parallel loops in OpenMP has been a research topic for over a decade. While many methods have been proposed, most focus on adapting the loop schedule purely at runtime, and without regard for the overall system state. We present a fully automatic loop scheduling policy that can adapt to both the characteristics of the input program as well as the current runtime behaviour of the system, including external load. Using state of the art polyhedral compiler analysis, we generate effort estimation functions that are then used by the runtime system to derive the optimal loop schedule for a given loop, work group size, iteration range and system state. We demonstrate performance improvements of up to 82% compared to default scheduling in an unloaded scenario, and up to 471% in a scenario with external load. We further show that even in the worst case, the results achieved by our automated system stay within 3% of the performance of a manually tuned strategy. © 2012 Springer-Verlag.

关键词： Scheduling

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Public and private BitTorrent communities: A measurement study 9

Public and private BitTorrent communities: A measurement stu...

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9th International Workshop on Peer-to-Peer systems, IPTPS 2010

作者： Meulpolder, M. D'Acunto, L. Capotă, M. Wojciechowski, M. Pouwelse, J.A. Epema, D.H.J. Sips, H.J. Parallel and Distributed Systems Group Department of Computer Science Delft University of Technology Netherlands

BitTorrent communities, both public and private, are immensely popular in the Internet, with tens of millions of users simultaneously active at any given moment. Public and private BitTorrent communities are managed in different ways - for instance, some private communities enforce sharing ratios, have strict rules for content management, have a certain level of community oversight, and maintain a strong sense of exclusiveness. In this paper, we present the results of extensive measurements of more than half a million peers in five communities, ranging from highly popular and well-known public communities to elite private communities that can only be joined by invitation. We observe that the performance experienced by downloaders in the private communities is by far superior to the performance in the public communities, and we observe significant differences in connectability, seeder/leecher ratio, and seeding duration. Based on our results, we conjecture that when effective ratio enforcement mechanisms are in place, BitTorrent's tit-for-tat mechanism is hardly influential anymore. Our multi-community, multi-swarm measurements are significantly broader and more extensive than any earlier measurement study on BitTorrent. © IPTPS *** right reserved.

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