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Leaf: Learning-based Stream-level Fair scheduling for Deep Learning Accelerators

Leaf: Learning-based Stream-level Fair Scheduling for Deep L...

引用

international Conference on parallel and distributed systems (ICPADS)

作者： Yida Wang Bojun Cao Mengjuan Gao Qinwen Shi Yuanchao Xu College of Information Engineering Capital Normal University Beijing China State Key Lab of Processors ICT CAS Beijing China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Deep learning accelerators, as the cornerstone of machine learning systems, expedite neural network training and inference. With their computational power escalating annually, multi-tasking becomes imperative to harness their full potential. However, similar to other parallel processing systems, deep learning accelerators confront the problem of performance fluctuations which could result in unpredictable kernel latency, suboptimal resource utilization, and exacerbated tail latency. This paper identifies the unfairness in stream-level scheduling as the root cause of these performance fluctuations. To mitigate this issue, we introduce Leaf, an innovative learning-based stream-level fair scheduling method that dynamically learns and adapts scheduling policies by leveraging feature vectors extracted from enqueued kernels and accelerator status. To tackle the issue of scalability and latency constraints inherent in stream-level scheduling, we devise a scalable scheduling framework and an online scheduler switching mechanism for Leaf. Preliminary implementation on a commercial grade deep learning accelerator demonstrates that Leaf can significantly reduce kernel latency variation by $10 \sim 20$ times, sustain high and stable resource utilization, and markedly decrease workload runtime by mitigating tail latency, outperforming the accelerator’s native scheduler.

关键词： Deep learning Fluctuations Runtime Scalability Tail Switches Feature extraction resource management Kernel Hardware acceleration

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Harnessing Nature Inspired Baya Weaver Nesting Techniques for Efficient and Sustainable resource scheduling in Edge Computing

Harnessing Nature Inspired Baya Weaver Nesting Techniques fo...

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international Conference on parallel distributed and Grid Computing (PDGC)

作者： Dinesh Prasad Sahu Rajnish Chaturvedi Hardeo Kumar Thakur Nidhi Nidhi SCSET Bennett University Greater Noida India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

This paper presents an innovative resource scheduling algorithm for edge computing, inspired by the nest-making process of the baya weaver *** proposed Baya Weaver-inspired algorithm leverages the hierarchical and adaptive nest-weaving strategies of Baya Weaver birds to design an efficient resource scheduling mechanism for edge computing environments. Unlike traditional algorithms, it dynamically prioritizes tasks and allocates resources based on real-time workloads and energy constraints, ensuring high system reliability and sustainability. A mathematical model of the baya weaver construction is incorporated into the algorithm used to design construction behavior, and to reflect the performance of a distributed and cooperative system, capable of adapting to environmental changes. A comparison is made with five conventional algorithms: FCFS, Round Robin, Min-Min, Max-Min and Energy-Aware scheduling with QoS parameters inclusive of task completion time, energy consumption, resource utilization, system reliability, throughputs and scheduling efficiency. The results show that the proposed algorithm gives better performances than the standard approaches and guarantees considerable enhancements to the task throughput as well as the sustainability of the system, qualifying as an appropriate model for current edge computing paradigms.

关键词： Heuristic algorithms Quality of service Throughput scheduling Real-time systems resource management Sustainable development Round robin Standards Edge computing

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Fairness scheduling for Tasks with Different Real-time Level on Heterogeneous systems

Fairness Scheduling for Tasks with Different Real-time Level...

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international Conference on parallel and distributed systems (ICPADS)

作者： Shifan Shao Shouzhen Gu Biao Sun Edwin H.-M. Sha Qingfeng Zhuge East China Normal University

For a real-time task-intensive systems, the fairness of task execution in dynamic scheduling is an important research area. However, many exist scheduling algorithms are unable to guarantee that tasks can be completed by the deadline and executed with a fair priority. In this paper, we proposed an efficient Multi-DAG real-time scheduling algorithm, HSDFW, which employs a fair priority calculation method to enable tasks with different real-time levels can be completed by the deadline, and a rejection policy to improve the performance of schedule. We proposed an INLP model and an evaluation simulator to verify the efficiency of HSDFW algorithm. The evaluation results show that our proposed algorithm has excellent performance in terms of average scheduling length and resource utilization.

关键词： Schedules scheduling algorithms Heuristic algorithms Dynamic scheduling Real-time systems resource management Task analysis

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workshop on resource Arbitration for Dynamic Runtimes (RADR) 36

Workshop on Resource Arbitration for Dynamic Runtimes (RADR)

引用

36th IEEE international parallel and distributed Processing Symposium workshops, IPDPSW 2022

作者： Beckman, Pete Jeannot, Emmanuel Perarnau, Swann Northwestern University Argonne National Laboratory ArgonneIL United States TADaaM Team Inria Talence France Argonne National Laboratory ArgonneIL United States

ISBN: (纸本)9781665497473

The question of efficient dynamic allocation of compute-node resources, such as cores, by independent libraries or runtime systems can be a nightmare. Scientists writing application components have no way to efficiently specify and compose resource-hungry components. As application software stacks become deeper and the interaction of multiple runtime layers compete for resources from the operating system, it has become clear that intelligent cooperation is needed. resources such as compute cores, in-package memory, and even electrical power must be orchestrated dynamically across application components, with the ability to query each other and respond appropriately. A more integrated solution would reduce intra-application resource competition and improve performance. Furthermore, application runtime systems could request and allocate specific hardware assets and adjust runtime tuning parameters up and down the software stack. The goal of this workshop is to gather and share the latest scholarly research from the community working on these issues, at all levels of the HPC software stack. This includes thread allocation, resource arbitration and management, containers, and so on, from runtime-system designers to compilers. We will also use panel sessions and keynote talks to discuss these issues, share visions, and present solutions. © 2022 IEEE.

关键词： Application programs

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Long-life Sensitive Modulo scheduling with Adaptive Loop Expansion

Long-life Sensitive Modulo Scheduling with Adaptive Loop Exp...

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international Conference on parallel and distributed systems (ICPADS)

作者： Hongli Zhong Zhong Liu College of Computer National University of Defense Technology Changsha China

This paper presents a novel modulo scheduling method, which is called Expanded Iterative Modulo scheduling (EIMS). EIMS integrates an adaptive loop expansion mechanism, and the overhead of analyzing data dependencies depends only on the initial loop, which means that it expands the search space with lower overhead compared to other methods that separate loop unrolling and scheduling. EIMS focuses on the criticality of operations and makes those interdependent operations as close as possible, thereby reducing register requirements. Notably, Long-lived Transfer Mechanism (LTM) is proposed to address scheduling failures caused by long-lived variables, which has not been mentioned in previous papers. The paper describes the technique and evaluates it on the MT-3000, achieving over 18x performance improvement on 7 classical assemblies and better resource utilization against other methods.

关键词： Adaptive scheduling Processor scheduling Software Registers resource management VLIW Iterative methods

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Rapid Development of OS Support with PMCSched for scheduling on Asymmetric Multicore systems 28th

Rapid Development of OS Support with PMCSched for Schedul...

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28th international European Conference on parallel and distributed Computing , Euro-Par 2022

作者： Bilbao, Carlos Saez, Juan Carlos Prieto-Matias, Manuel Facultad de Informática Universidad Complutense de Madrid Madrid Spain

ISBN: (纸本)9783031312083

Asymmetric multicore processors (AMPs) couple high-performance big cores and power-efficient small ones, all exposing a shared instruction set architecture to software, but with different microarchitectural features. The energy efficiency benefits of AMPs together with the general-purpose nature of the various cores, have led hardware manufactures to build commercial AMP-based products, first for the mobile and embedded domains, and more recently for the desktop market segment, as with the Intel Alder Lake processor family. This indicates that AMPs may become a solid and more energy efficient replacement to symmetric multicores in a wide range of application domains. Previous research has demonstrated that the system software can substantially improve scheduling –critical to get the most out of heterogeneous cores– by leveraging hardware facilities that are directly managed by the OS, such as performance monitoring counters, or the recently introduced Intel Thread Director technology. Unfortunately, the OS-level support enabling to access scheduling-relevant hardware support may take a long time to be adopted in operating systems, or may come in forms that make its utilization challenging from specific levels of the system software stack, especially in production systems. To fill this gap, we propose the PMCSched framework, which enables the creation of custom OS support on Linux to aid in the design of novel scheduling and resource-management policies for multicores implemented at different layers of the system software, but without requiring to patch the kernel. To demonstrate the potential of our framework, we implement a set of OS-level schedulers for AMPs, that make use of custom OS extensions to access scheduling-relevant hardware facilities in an x86 AMP processor. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Lakes

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Proceedings of the 2023 IEEE international Conference on Computer, Information, and Telecommunication systems, CITS 2023

Proceedings of the 2023 IEEE International Conference on Com...

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2023 IEEE international Conference on Computer, Information, and Telecommunication systems, CITS 2023

ISBN: (纸本)9798350336092

The proceedings contain 33 papers. The topics discussed include: deep learning polar convolutional parallel concatenated (DL-PCPC) channel decoding for 6G communications;cyber deception system based on Monte Carlo simulation in the mobile edge computing (MEC);lightweight hash function based on Fermat number transform (FNT);automatic recognition for LPI radar waveform using sequence-based deep learning model;a transfer learning based CSI indoor localization using GASF image construction;profit aware resource allocation in fog computing: a Stackelberg game approach;proposal for a security model applying artificial intelligence for administrative management in a higher education institution;on implementation of sparse finite impulse response secondary path for active noise control;high layer coding for video transmission;deep neural network for user attribute recognition in metaverse hand images;lifetime estimation and reparameterization of GPS devices for tracking applications;a reparameterization and monitoring platform for energy efficient GPS trackers in behavioral analysis of wildlife;and scheduling delay-sensitive bag-of-task jobs with imprecise computations on distributed resources.

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Proceedings - 23rd IEEE/ACM international Symposium on Cluster, Cloud and Internet Computing, CCGrid 2023

Proceedings - 23rd IEEE/ACM International Symposium on Clust...

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23rd IEEE/ACM international Symposium on Cluster, Cloud and Internet Computing, CCGrid 2023

ISBN: (纸本)9798350301199

The proceedings contain 59 papers. The topics discussed include: rethinking design paradigm of graph processing system with a CXL-like memory semantic fabric;a case study of data management challenges presented in large-scale machine learning workflows;an asynchronous dataflow-driven execution model for distributed accelerator computing;an empirical study of container image configurations and their impact on start times;how workflow engines should talk to resource managers: a proposal for a common workflow scheduling interface;Layercake: efficient inference serving with cloud and mobile resources;optimal sizing of a globally distributed low carbon cloud federation;predicting the performance-cost trade-off of applications across multiple systems;a deep learning pipeline parallel optimization method;HDFL: a heterogeneity and client dropout-aware federated learning framework;heterogeneous federated learning using dynamic model pruning and adaptive gradient;and measuring the impact of gradient accumulation on cloud-based distributed training.

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BachLedger: Orchestrating parallel Execution with Dynamic Dependency Detection and Seamless scheduling

BachLedger: Orchestrating Parallel Execution with Dynamic De...

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international Conference on parallel and distributed systems (ICPADS)

作者： Yi Yang Guangyong Shang Guangpeng Qi Zhen Ma Yaxiong Liu Jiazhou Tian Aocheng Duan Meng Zhang Jingying Li Xuan Ding School of Software Tsinghua University Beijing China Inspur Yunzhou Industrial Internet Co. Ltd Jinan Shandong China School of Cyber Engineering Xidian University Xi’an Shaanxi China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Blockchain technology inherently necessitates redundant computation to achieve consensus among untrusted parties because of its fundamental threat model. This requirement, however, compromises system performance and impedes the widespread adoption of blockchain. To leverage existing physical resources, current research on high-performance consortium blockchain algorithms and architectures frequently employs cluster-node architectures to expand the parallel processing capability of traditional single physical nodes. Our investigation reveals a significant trend as the parallel capability of individual nodes improves. The idle time caused by synchronization of all transactions within each block, previously considered negligible, has become increasingly significant. To address this, we present BachLedger, which implements Seamless scheduling to fully utilize inter-block thread idle time, thereby augmenting system resource utilization and achieving overall performance improvements. Our experimental results demonstrate that our algorithm surpasses current state-of-the-art (SOTA) performance levels in high-performance consortium blockchains and effectively resolves the aforementioned synchronization issue. Furthermore, this scheduling algorithm offers enhanced scalability for BachLedger, positioning it as a promising solution for future blockchain implementations.

关键词： Threat modeling scheduling algorithms System performance Scalability Computer architecture parallel processing Dynamic scheduling Blockchains Synchronization resource management

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Optimizing Big Data Analytics Architecture for Edge Computing Using Container Technology

Optimizing Big Data Analytics Architecture for Edge Computin...

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international Conference on parallel and distributed systems (ICPADS)

作者： Hong Peng YunTian Bai GuanYing Kang Yang Li TingWei Chen LiaoNing University ChongShan Middle Road Huanggu District ShenYang LiaoNing China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

This study is dedicated to the integration of big data analytics with edge computing, a critical need driven by the exponential growth of Internet of Things (IoT) technologies and smart device data. We introduce an optimized architecture that integrates Docker containerization with Apache Hadoop YARN, designed specifically for decentralized, resource-constrained edge environments. This innovative framework not only facilitates dynamic resource management but also ensures robust fault tolerance under the heterogeneous and often unstable network conditions typical of edge settings. By integrating Docker and Docker Swarm for container orchestration along with YARN for resource scheduling, our architecture significantly enhances data processing localization. This improvement reduces computational latency and boosts system performance. Through empirical evaluation, we demonstrate marked enhancements in task flexibility, resource efficiency, and operational continuity, particularly adept at managing the diversity and sporadic conditions of edge nodes. Additionally, the deployment of advanced load-balancing tools and multiple management nodes within containers enables the system to autonomously reallocate tasks during node failures or resource-constrained periods. Experimental results show that our architecture achieves an average failover recovery time of less than five seconds and maintains an average CPU utilization below 3% for management nodes, demonstrating significant advantages in handling large-scale IoT data at edge nodes.

关键词： Processor scheduling Computer architecture Cluster computing Containers Big Data Data processing resource management Internet of Things Yarn Edge computing

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