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A Contribution Assessment Method Based on Model Performance Gains in Federated Learning

A Contribution Assessment Method Based on Model Performance ...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Zixuan Zhao Jun Liu Yuyin Li Gaoyang Liu Yamin Li Yang Yang Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Federated Learning is a distributed model training system that uses the computing resources and private data of participants to collaboratively train Machine Learning models. In order to incentivize data holders to actively participate in Federated Learning, a crucial issue is how to fairly assess each participant’s contribution. One class of the mainstream methods estimates client contributions by calculating the correlation between local and global model parameters, substantially reducing the reliance on large computing resources and test datasets. However, these methods overlook an implicit key factor, leading to inaccuracy in assessing the contribution of the participants. We propose FedCA, which provides a fairness assessment of the client’s contribution based on the performance gain of the global model in each communication round. The experimental results demonstrate that FedCA accurately identifies the contributions of clients with varying data qualities and effectively approximates the optimal fairness method of Shapley values. The source code for this paper is available at https://***/paper-liu/***.

关键词： Training distributed processing Correlation Federated learning Computational modeling Source coding Data integrity distributed databases Performance gain Data models

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Evaluating Energy Efficiency of GPUs using Machine Learning Benchmarks

Evaluating Energy Efficiency of GPUs using Machine Learning ...

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

作者： Brett Foster Shubbhi Taneja Joseph Manzano Kevin Barker Department of Computer Science Worcester Polytechnic Institute High Performance Computing Group Pacific Northwest National Laboratory

As we enter the exascale era, the energy efficiency and performance of High-Performance computing (HPC) systems, especially running Machine Learning (ML) applications, are becoming increasingly important. Nvidia recently released its 9th-generation HPC-grade Graphics Processing Unit (GPU) microarchitecture, Ampere, claiming significant improvements over the previous generation’s Volta architecture. In this paper, we perform fine-grained power collection and assess the performance of these two HPC architectures’ performance by profiling ML benchmarks. In addition, we analyze various hyperparameters, primarily the batch size and the number of GPUs, to determine their impact on these systems’ performance and power efficiency. While Ampere is 3.16x more energy-efficient than Volta in isolation, this is counteracted by the PCIe interconnects of the A100s as the ML tasks are parallelized to run on more GPUs.

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A parallel Meta-Solver for the Multi-Objective Set Covering Problem

A Parallel Meta-Solver for the Multi-Objective Set Covering ...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Marshall, Ryan J. Weerasena, Lakmali Skjellum, Anthony Univ Tennessee UTC SimCtr Chattanooga TN 37996 USA Univ Tennessee Dept Math Chattanooga TN USA

ISBN: (纸本)9781665435772

The multi-objective set covering problem (MOSCP) appears in many different real-world applications. We implemented a meta-solver in C++ that introduces shared-memory concurrency using OpenMP. It incorporates a commonly used Mixed Integer Problem (MIP) solver to find initial solutions with a linear programming (LP) solver that enumerates possible solutions over a tree of subproblems using a local branch approach. Adhering to a finite cutoff value, solutions are ordered as they are passed back up the tree to produce the set of Pareto fronts. In this paper, we present a serial version of the meta-solver with a novel search procedure that outperforms a previous implementation, and when parallelization techniques are applied, a 9-12x speedup is achieved with the possibility of further improvement for large problems.

关键词： Multi-objective parallel computing Algorithm Approximation Set Cover

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A Hybrid Memory Data Placement Strategy for Edge computing

A Hybrid Memory Data Placement Strategy for Edge Computing

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Binghui Lin Jianxun Zhang Xinyu Qiao College of Information Technology Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the booming development of fields such as cloud computing, big data, and artificial intelligence, the productivity of data have experienced explosive growth, prompting the expansion of edge computing. However, as data-intensive applications continue to increase, memory systems based solely on Dynamic Random Access Memory (DRAM) are no longer able to meet the demands of edge computing for high memory footprints, low access latency, and low energy consumption. In this paper, we organize DRAM as a cache for Non-Volatile Memory (NVM) and propose a utility-based hybrid memory data placement (UDP-HM) strategy, to address energy efficiency issues. UDP-HM places data in a hybrid memory system by calculating page utility. A series of simulation experiments are conducted to verify and evaluate the proposed hybrid memory data placement strategy. Under different workload intensities, compared to UH-MEM and RBLA, UDP-HM shows an average increase of 8.71% and 9.21% respectively in DRAM cache read/write operations, an average increase of 10.18% and 13.94% respectively in latency, an average decrease of 35.24% and 37.18% respectively in energy consumption, and an average decrease of 26.48% and 31.68% respectively in energy-delay product.

关键词： Productivity Energy consumption Nonvolatile memory Computational modeling Memory management Memory architecture Random access memory Energy efficiency Explosives Edge computing

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SmartShip: Data Decoding Optimization for Onboard AI Anomaly Detection

SmartShip: Data Decoding Optimization for Onboard AI Anomaly...

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international symposium on parallel and distributed computing

作者： Pavle Ivanovic Alexander Windmann Philipp Neumann Chair for High Performance Computing Helmut Schmidt University Hamburg Germany Chair for Computer Science in Mechanical Engineering Helmut Schmidt University Hamburg Germany Deutsches Elektronen-Synchroton Research Group High Performance Computing and Data Science Universität Hamburg Hamburg Germany

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

Maritime rescue operations rely on efficient and resilient sea vessels for responding to offshore emergencies. To ensure the availability and prompt deployment of rescue cruisers, system operators need to recognize (and resolve) the failure patterns before they occur onboard. However, many SAR organizations do not have sufficient computing resources for advanced anomaly detection and future fault prediction. This paper details our performant and cost-effective solution for sensor data acquisition, CAN bus decoding, and AI analysis, deployable on typical maritime edge devices. The main focus of our work is enhancing the decoding speed, which is a prerequisite for efficient AI analyses and anomaly detection. Besides elaboration on different optimization strategies, we provide the actual use case for AI anomaly detection based on anomaly scores of electrical, fuel, and cooling system sensors. As a result, we improved the decoding speed by more than two orders of magnitude, allowing edge outlier detection in minutes rather than days.

关键词： Performance evaluation System testing Image edge detection Data acquisition Decoding Pattern recognition Artificial intelligence Anomaly detection Optimization Tuning

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SparkTH: NUMA-Aware and I/O-Efficient Spark for ARM-Based Many-Core Supercomputing System

SparkTH: NUMA-Aware and I/O-Efficient Spark for ARM-Based Ma...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Nan Hu Minguang Xiao Zhiguang Chen Yutong Lu Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

ARM-based many-core processors in supercomputers enable exascale-level data analysis by leveraging the massive number of cores within a single chip. However, most existing big data frameworks, designed for distributed environments, fail to effectively harness the full potential of advanced supercomputing systems. Two significant issues impeding data processing efficiency are the remote memory access between NUMA nodes and the disregard for architectures within compute nodes. Furthermore, the explosive growth of intermediate data results in a severe mismatch between I/O capabilities and computation performance due to frequent read and write operations. In this paper, we present SparkTH, a NUMA-aware and I/O-efficient framework for big data processing on many-core supercomputing systems. SparkTH incorporates a NUMA resource management layer to fully utilize many-core resources and cuts the number of I/O operations in half in parallel file systems. We evaluated SparkTH on two ARM-based many-core systems, and it outperformed the original Spark by up to 2.1× on typical big data benchmarks and 8.7× on scientific computing applications.

关键词： Program processors Scientific computing File systems Memory management Big Data Benchmark testing Supercomputers Service-oriented architecture Sparks Resource management

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distributed Tracing for InterPlanetary File System

Distributed Tracing for InterPlanetary File System

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parallel computing and distributed Systems (PCDS), international symposium on

作者： Sushant Kumar Gupta Marshall David Miller Rachel Han Haorui Guo Dept. of Computer Science Stanford University Palo Alto USA

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

The InterPlanetary File System (IPFS) is on its way to becoming the backbone of the next generation of the web. However, it suffers from several performance bottlenecks, particularly on the content retrieval path, which are often difficult to debug. This is because content retrieval involves multiple peers on the decentralized network and the issue could lie anywhere in the network. Traditional debugging tools are insufficient to help web developers who face the challenge of slow loading websites and detrimental user experience. This limits the adoption and future scalability of IPFS. In this paper, we aim to gain valuable insights into how content retrieval requests propagate within the IPFS network as well as identify potential performance bottlenecks which could lead to opportunities for improvement. We propose a custom tracing framework that generates and manages traces for crucial events that take place on each peer during content retrieval. The framework leverages event semantics to build a timeline of each protocol involved in the retrieval, helping developers pinpoint problems. Additionally, it is resilient to malicious behaviors of the peers in the decentralized environment. We have implemented this framework on top of an existing IPFS implementation written in Java called Nabu. Our evaluation shows that the framework can identify network delays and issues with each peer involved in content retrieval requests at a very low overhead.

关键词： Java Scalability Semantics Loading distributed databases parallel processing User experience InterPlanetary File System Next generation networking Faces

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LBCB: One-sided RDMA-based distributed B+ tree Index with Low Bandwidth Consumption

LBCB: One-sided RDMA-based Distributed B+ tree Index with Lo...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Jibo Liu Rui Xi Qinzhen Cao Xiaowen Nie Zhuohan Hou University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Disaggregated memory architecture segregates computing and memory resources into distinct pools interconnected by a high-speed one-sided RDMA (Remote Direct Memory Access) network, enhancing memory utilization, reducing costs, and facilitating elastic scaling of computing and memory resources. However, optimizing index structures to maximize the benefits of this framework poses significant challenges. Despite progress in B+ tree indexes for disaggregated memory systems, they still suffer from severe write and read amplification issues, which impede latency and throughput *** this paper, we propose LBCB, a B+ tree index for disaggregated memory, which significantly reduces the bandwidth consumption of index operations. First, LBCB introduces an RDMA friendly B+ tree leaf node structure, which improves concurrency while reducing the bandwidth consumption. Second, LBCB designs a logical fusion FAA lock that synchronizes more information with a single RDMA communication, significantly reducing the number of RDMA network round trips. Finally, LBCB optimistically compresses the critical path of index operations, further optimizing latency and throughput. Evaluation results show that compared with the state-of-the-art distributed B+ tree index, the write performance of LBCB is improved by 1.42 times.

关键词： Concurrent computing Memory management Memory architecture Bandwidth Writing Throughput Indexes Synchronization Logic Indexing

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distributed On-Demand Deployment for Transparent Access to 5G Edge computing Services

Distributed On-Demand Deployment for Transparent Access to 5...

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

作者： Josef Hammer Hermann Hellwagner Institute of Information Technology Alpen-Adria-Universität Klagenfurt Austria

Multi-access Edge computing (MEC) is a central piece of 5G telecommunication systems and is essential to satisfy the challenging low-latency demands of future applications. MEC provides a cloud computing platform at the edge of the radio access network. Our previous publications argue that edge computing should be transparent to clients, leveraging Software-Defined Networking (SDN). While we introduced a solution to implement such a transparent approach, one question remained: How to handle user requests to a service that is not yet running in a nearby edge cluster? One advantage of the transparent edge is that one could process the initial request in the cloud. However, this paper argues that on-demand deployment might be fast enough for many services, even for the first request. We present an SDN controller that automatically deploys an application container in a nearby edge cluster if no instance is running yet. In the meantime, the user’s request is forwarded to another (nearby) edge cluster or kept waiting to be forwarded immediately to the newly instantiated instance. Our performance evaluations on a real edge/fog testbed show that the waiting time for the initial request – e.g., for annginx-based service – can be as low as 0.5 seconds – satisfactory for many applications.

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CKSM: An Efficient Memory Deduplication Method for Container-based Cloud computing Systems

CKSM: An Efficient Memory Deduplication Method for Container...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Yunfei Gu Yihui Lu Chentao Wu Jie Li Minyi Guo Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Memory deduplication techniques are widely used to improve memory utilization in cloud computing platforms, and they can be categorized into virtualized and containerized environments. In virtualized environments, prevalent memory deduplication approaches often rely on scanning the virtual address space of different processes. However, the complexity of virtual address spaces can reduce scanning efficiency in containerized environments. Additionally, the many-to-one mapping between virtual and physical pages can decrease the efficiency of merging *** solve the above problems, we proposed a Container-based Kernel Samepage Merging method called CKSM. This method leverages potential duplicate candidates and efficiently performs merging operations. It employs layered sampling to construct the priority of physical pages. Additionally, a physical page scanning mechanism is designed to directly obtain valid pages within the system. CKSM uses the physical page merge mechanism to merge all virtual pages at once and release the corresponding memory directly. We conduct several experiments to demonstrate the efficiency of CKSM. It reduces the scanning overhead by up to 80.99% and increases page comparison efficiency by up to 42.51%. Besides, CKSM achieves an average of 3.02×memory usage reduction compared to UKSM and 2.79×response speedup compared to KSM in the containerized environment. In cloud computing emulation, CKSM has been proven to be optimal in high-density deployment.

关键词： Cloud computing distributed processing Linux Merging Memory management Emulation Containers

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