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12th IEEE international Workshop on Accelerators and Hybrid Emerging systems

Proceedings - 2022 IEEE 36th International Parallel and Dist...

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Proceedings - 2022 IEEE 36th international parallel and distributed Processing symposium Workshops, IPDPSW 2022 2022年 369-370页

作者： Oden, Lena FernUni Hagen Germany

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Optimal Time and Energy-Aware Client Selection Algorithms for Federated Learning on Heterogeneous Resources

Optimal Time and Energy-Aware Client Selection Algorithms fo...

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international symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Alan L. Nunes Cristina Boeres Lúcia M. A. Drummond Laércio L. Pilla Fluminense Federal University Niterói Brazil University of Bordeaux CNRS Bordeaux INP Inria LaBRI Talence France

ISBN: (数字)9798350356168

ISBN: (纸本)9798350356175

Federated Learning systems allow training machine learning models distributed across multiple clients, each one using private local data. Iteratively, the clients send their training contributions to a server, which performs a merge to produce an enhanced global model. Due to resource and data heterogeneity, client selection is crucial to optimize the system efficiency and improve the global model generalization. Selecting more clients is likely to increase the overall energy consumption, while a small number of clients may decline the performance of the trained model or require longer training time. We propose two time- and energy-aware client selection algorithms, MEC and ECMTC, which are proven regarding their optimality and evaluated against state-of-the-art algorithms on an extensive series of experiments in both simulation and HPC platform scenarios. The results indicate the benefits of jointly optimizing the time and energy consumption metrics using our proposals.

关键词： Training Measurement Energy consumption Federated learning High performance computing distributed databases Computer architecture Data models Servers Proposals

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RAPID-AV: Realtime Adaptive and Intelligent Detection For AV’s Against DDoS Attacks

RAPID-AV: Realtime Adaptive and Intelligent Detection For AV...

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international symposium on Advanced Networks and Telecommunication systems (ANTS)

作者： Aditya Kotha U. Venkanna Department of Computer Science and Engineering International Institute of Information Technology Naya Raipur India Department of Computer Science and Engineering National Institute of Technology Warangal India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

Autonomous vehicles (AVs) increasingly rely on vehicle-to-everything (V2X) networks for communication. However, due to the devices’ heterogeneity, they are more susceptible to attacks like distributed denial of service (DDoS), resulting in packet loss and transmission delays. Existing solutions to these issues often create bottlenecks and lack post-attack recovery mechanisms. To solve these problems, this paper proposes RAPID-AV, an ML-based approach that uses programmable P4 switches for real-time detection and mitigation of DDoS attacks in AV networks. The proposed system uses a threefold strategy: collect traffic statistics from AV, use P4-based programmable packet processing for anomaly detection, and use majority voting to mitigate attacks. In our experiments, we found that the decision tree algorithm achieves an accuracy of 94.33% in distinguishing traffic from DDoS traffic. In addition, RAPID-AV demonstrated a nearly 10% reduction in CPU load during attacks and maintained consistent communication between AV and RSU. This approach can help improve AV network security while addressing the limitations of existing solutions.

关键词： Prevention and mitigation Packet loss distributed databases Network security Denial-of-service attack Real-time systems Telecommunications Delays Computer crime Vehicle-to-everything

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A Runtime Manager Integrated Emulation Environment for Heterogeneous SoC Design with RISC-V Cores

A Runtime Manager Integrated Emulation Environment for Heter...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： H. Umut Suluhan Serhan Gener Alexander Fusco Joshua Mack Ismet Dagli Mehmet Belviranli Cagatay Edemen Ali Akoglu Electrical and Computer Engineering Department The University of Arizona Tucson AZ USA Computer Science Department Colorado School of Mines Golden CO USA Electrical and Electronics Engineering Department Ozyegin University Istanbul Turkey

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

RISC-V based architectures have gained attention for deployment on platforms from SoC to HPC scale to meet the demands of a wide range of computational workloads that may require specialized hardware tailored to their specific performance goals. In previous works, we introduced CEDR, an open-source, unified compilation and runtime framework designed to facilitate productive hardware-agnostic application deployment on heterogeneous architectures, and lift the barriers to research on the design and evaluation of scheduling heuristics and accelerators for heterogeneous systems. In this study, we demonstrate the capability of CEDR in supporting heterogeneous systems composed of RISC-V cores and a pool of accelerators. We investigate the impact of employing customized lightweight RISC-V cores for managing accelerators individually. Such management approach, compared to centralized accelerator management model, scales down the stress on the runtime environment where compute resources are shared among multiple applications each with varying degrees of task-level parallelism. We perform evaluations on a heterogeneous SoC emulated on the Xilinx Virtex-7 FPGA based on execution of applications from radar, signal processing, and autonomous vehicles domains. We observe that releasing CPU-based compute cores from accelerator management through lightweight RISC-V cores results with up to 47% reduction in time spent on accelerator management. Since CPU-based compute cores exclusively become available for application task execution and contention on resource management is reduced, we observe speedup of up to 2.2x in application execution time based on experiments conducted with workloads consisting of dynamically arriving real-life application scenarios.

关键词： Runtime environment Processor scheduling Computer architecture parallel processing Radar signal processing Hardware Resource management

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A Variable Sliding Window Algorithm Based on Concept Drift for Frequent Pattern Mining Over Data Streams*

A Variable Sliding Window Algorithm Based on Concept Drift f...

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international Conference on parallel and distributed systems (ICPADS)

作者： Yue Yin Peng Li Jing Chen School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Baotou Teachers’ College of Inner Mongolia University of Science and Technology Baotou China

Sliding window is a widely used model in the process of mining frequent patterns in data streams. In order to determine the exact size of the sliding window, it is necessary to know the time and scale of the change over data streams in advance. However, the prior knowledge is difficult to determine, and the fixed-size sliding window in the traditional algorithms obviously cannot adapt to this change, which have poor performance on the latest concepts of data streams. Based on the above shortcomings, this paper proposes a new frequent pattern mining algorithm based on concept drift using variable sliding window: VSWCDD (Variable Sliding Window-Concept Drift Detection), which is suitable for mining frequent patterns in changing data streams. The window size is dynamically determined by whether the concept drift occurs in the data stream. During the mining process, the mining result variables and the cause variables about the concept drift are monitored simultaneously. When the data stream is stable and there is no concept change, the window size keeps expansion. When the concept drift occurs, the window size changes according to the difference between detection nodes. Extensive experiments on both real and synthetic data show that the VSW-CDD algorithm proposed in this paper can timely detect the concept drift in the data stream and adapt to the new concepts by adjusting the window size. Finally, the latest frequent patterns in the data streams are mined and has a better effect for click data streams on e-commerce sites and medical data. Compared with other algorithms, the algorithm in this paper also has better performance in terms of recall and adaptation.

关键词： Heuristic algorithms distributed databases Data models Data mining Electronic commerce Biomedical monitoring Monitoring

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Cloud-Enabled Blood Bank Management for an Efficient Healthcare System

Cloud-Enabled Blood Bank Management for an Efficient Healthc...

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ACIS international Conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Carmen Cornejo Huatuco Chenyu Zhang Janviben Patel Md Moniruzzaman Ajmery Sultana Department of Computer Science Lakehead University Thunder Bay ON Canada School of Computer Science & Technology Algoma University Brampton ON Canada

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

There is a very serious problem plaguing people right now, and that is the shortage of blood banks around the world. This is because blood is at the heart of a major healthcare challenge, blood plays a vital role as the body’s energy source. To address this issue, this paper describes a post-donation blood quality testing system, as well as a cross-matching testing system for blood type suitability for patients, that utilizes the advanced security features of Amazon Web Services (AWS), rigorously stores all user data, including personal information and blood type, and adds a commitment to protecting the integrity and confidentiality of blood donor data. AWS’s scalability ensures that the system can adapt to the growing demand for blood supply while maintaining performance and security, enhancing trust and transparency between donors, recipients, and healthcare providers. Blood is at the heart of a major healthcare challenge. Blood plays a vital role as the body’s energy source. Hence, we propose a web-based application system that is integrated in real-time with hospital databases in various regions, especially in remote areas, enabling rapid identification of blood needs and effective distribution, greatly reducing emergency response time. Also, the system will effectively help people living in remote areas. This system not only solves today’s blood supply management challenges but also sets a new standard for global healthcare equity and access in the future, ensuring that life-saving blood is available everywhere and to everyone who needs it.

关键词： Heart Cloud computing Hospitals databases Web services Scalability Real-time systems

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On the parallel Reconstruction from Pooled Data

On the Parallel Reconstruction from Pooled Data

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international symposium on parallel and distributed Processing (IPDPS)

作者： Oliver Gebhard Max Hahn-Klimroth Dominik Kaaser Philipp Loick TU Dortmund University Dortmund Germany Universit&#x00E4 t Hamburg Hamburg Germany Goethe University Frankfurt Frankfurt Germany

In the pooled data problem the goal is to efficiently reconstruct a binary signal from additive measurements. Given a signal $\sigma\in \{0, 1\}^{n}$ , we can query multiple entries at once and get the total number of non-zero entries in the query as a result. We assume that queries are time-consuming and therefore focus on the setting where all queries are executed in parallel. For the regime where the signal is sparse such that $\Vert\sigma\Vert_{1}= o(n)$ our results are twofold: First, we propose and analyze a simple and efficient greedy reconstruction algorithm. Secondly, we derive a sharp information-theoretic threshold for the minimum number of queries required to reconstruct σ with high probability. Our first result matches the performance guarantees of much more involved constructions (Karimi et al. 2019). Our second result extends a result of Alaoui et al. (2014) and Scarlett & Cevher (2017) who studied the pooled data problem for dense signals. Finally, our theoretical findings are complemented with empirical simulations. Our data not only confirm the information-theoretic thresholds but also hint at the practical applicability of our pooling scheme and the simple greedy reconstruction algorithm.

关键词： distributed processing Additives distributed databases Reconstruction algorithms

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An Asynchronous Dataflow-Driven Execution Model For distributed Accelerator Computing

An Asynchronous Dataflow-Driven Execution Model For Distribu...

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IEEE/ACM international symposium on Cluster Computing and the Grid (CCGRID)

作者： Philip Salzmann Fabian Knorr Peter Thoman Philipp Gschwandtner Biagio Cosenza Thomas Fahringer Distributed and Parallel Systems Group University of Innsbruck Austria University of Salerno Italy

While domain-specific HPC software packages continue to thrive and are vital to many scientific communities, a general purpose high-productivity GPU cluster programming model that facilitates experimentation for non-experts remains elusive. We demonstrate how Celerity, a high-level C++ programming model for distributed accelerator computing based on the open SYCL standard, allows for the quick development of - and experimentation with - distributed applications. To achieve scalability on large machines, we replace Celerity's existing master/worker scheduling model with a fully distributed scheme that reduces the worst-case scheduling complexity from quadratic to linear while maintaining the existing programming interface. We then show how this declarative, data-flow based API paired with a point-to-point communication model with eager data pushing can effectively expose and leverage opportunities for latency hiding and computation/communication overlapping with minimal or no manual guidance. We demonstrate how Celerity exhibits very good scalability on multiple benchmarks from several scientific domains and up to 128 GPUs.

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In-Memory Indexed Caching for distributed Data Processing

In-Memory Indexed Caching for Distributed Data Processing

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international symposium on parallel and distributed Processing (IPDPS)

作者： Alexandru Uta Bogdan Ghit Ankur Dave Jan Rellermeyer Peter Boncz LIACS Leiden University Databricks UC Berkeley TU Delft CWI

Powerful abstractions such as dataframes are only as efficient as their underlying runtime system. The de-facto distributed data processing framework, Apache Spark, is poorly suited for the modern cloud-based data-science workloads due to its outdated assumptions: static datasets analyzed using coarse-grained transformations. In this paper, we introduce the Indexed DataFrame, an in-memory cache that supports a dataframe abstraction which incorporates indexing capabilities to support fast lookup and join operations. Moreover, it supports appends with multi-version concurrency control. We implement the Indexed DataFrame as a lightweight, standalone library which can be integrated with minimum effort in existing Spark programs. We analyze the performance of the Indexed DataFrame in cluster and cloud deployments with real-world datasets and benchmarks using both Apache Spark and Databricks Runtime. In our evaluation, we show that the Indexed DataFrame significantly speeds-up query execution when compared to a non-indexed dataframe, incurring modest memory overhead.

关键词： distributed processing Runtime Data analysis Cluster computing Relational databases Libraries Concurrency control

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Constructions of Binary MDS Array Codes with Optimal Cooperative Repair Bandwidth

Constructions of Binary MDS Array Codes with Optimal Coopera...

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IEEE international symposium on Information Theory

作者： Lei Li Xinchun Yu Chenhao Ying Liang Chen Yuanyuan Dong Yuan Luo Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Data and Information Tsinghua University Shenzhen International Graduate School Shenzhen China Alibaba Group Hangzhou China

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Erasure codes are widely implemented in distributed storage systems to provide high fault tolerance with small storage overhead. Maximum distance separable codes are an common choice as they achieve the optimal tradeoff between fault tolerance and storage overhead. In this paper, we focus on the repair of multiple erasures of binary MDS array codes. Specifically, we present constructions of binary MDS array codes with optimal cooperative repair bandwidth by stacking multiple Blaum-Roth code instances whose “evaluation points” are judiciously designed. The constructed array codes with length $n$ and dimension $k$ can achieve the optimal cooperative repair bandwidth for $2\leq h\leq n-k$ and $k+1\leq d\leq n-h$ where $h$ and $d$ are the numbers of failed nodes and helper nodes, respectively. As the codes are constructed on a special polynomial ring over binary field, computation operations involved in nodes repair and file reconstruction for these codes are only XORs and cyclic shifts. Moreover, due to the inherent parallel structure of the codes, both the encoding and decoding procedures can be finished in parallel, speeding up the computing process.

关键词： Fault tolerance Codes Stacking Fault tolerant systems Bandwidth Maintenance engineering Polynomials

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