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SoCFlow: Efficient and Scalable DNN Training on SoC-Clustered Edge Servers 24

SoCFlow: Efficient and Scalable DNN Training on SoC-Clustere...

引用

29th ACM international conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Xu, Daliang Xu, Mengwei Lou, Chiheng Zhang, Li Huang, Gang Jin, Xin Liu, Xuanzhe Peking Univ Minist Educ Key Lab High Confidence Software Technol Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China State Key Lab Networking & Switching Technol Beijing Peoples R China Natl Key Lab Data Space Technol & Syst Beijing Peoples R China

ISBN: (纸本)9798400703720

SoC-Cluster, a novel server architecture composed of massive mobile system-on-chips (SoCs), is gaining popularity in industrial edge computing due to its energy efficiency and compatibility with existing mobile applications. However, we observe that the deployed SoC-Cluster servers are not fully utilized, because the hosted workloads are mostly usertriggered and have significant tidal phenomena. To harvest the free cycles, we propose to co-locate deep learning tasks on them. We present SoCFlow, the first framework that can efficiently train deep learning models on SoC-Cluster. To deal with the intrinsic inadequacy of commercial SoC-Cluster servers, SoCFlow incorporates two novel techniques: (1) the group-wise parallelism with delayed aggregation that can train deep learning models fast and scalably without being influenced by the network bottleneck;(2) the data-parallel mixed-precision training algorithm that can fully unleash the heterogeneous processors' capability of mobile SoCs. We have fully implemented SoCFlow and demonstrated its effectiveness through extensive experiments. The experiments show that SoCFlow significantly and consistently outperforms all baselines regarding the training speed while preserving the convergence accuracy, e.g., 1.6x-740x convergence speedup with 32 SoCs. Compared to commodity GPU (NVIDIA V100) under the same power budget, SoCFlow achieves comparable training speed but reduces energy consumption by 2.31x-10.23x with the same convergence accuracy.

关键词： SoC-Cluster distributed machine learning mixed precision training

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Multi-Cloud Container Orchestration for High-Performance Real-Time Online applications

Multi-Cloud Container Orchestration for High-Performance Rea...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Sezar Jarrous-Holtrup Sergei Gorlatch Michael Dey Folker Schamel University of Muenster Muenster Germany Spinor GmbH Munich Germany

We develop a novel multi-cloud container orchestration architecture for high-performance Real-Time Online Interactive applications (ROIA), with use cases including product configurators, multiplayer online gaming, e-learning and - training. Running the core components of ROIA, e.g., real-time 3D rendering, on a multi-cloud enables access to high-performance resources and prevents proprietary ‘vendor lock-in’. Our container orchestration facilitates: (1) strict Quality of Service (QoS) requirements, (2) secure communication between cluster nodes from different clouds, (3) automatic scalability, and (4) resource usage optimization. We improve previous work by using session slots that set a limit on the number of concurrent user sessions for a service instance without loss of QoS. Our implementation provides a vendor-independent, OpenVPN-based interconnection between cloud nodes, both Linux and Windows, possibly located in different LANs of a multi-cloud. We experimentally evaluate our orchestration approach on a Kubernetes-based cluster using a prototype of an interactive car configurator.

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Exploring MPI Collective I/O and File-per-process I/O for Checkpointing a Logical Inference Task

Exploring MPI Collective I/O and File-per-process I/O for Ch...

引用

35th IEEE international parallel and distributed processing Symposium (IPDPS)

作者： Fan, Ke Micinski, Kristopher Gilray, Thomas Kumar, Sidharth Univ Alabama Birmingham Birmingham AL 35294 USA Syracuse Univ Syracuse NY 13244 USA

ISBN: (纸本)9781665435772

We present a scalable parallel I/O system for a logical-inferencing application built atop a deductive database. Deductive databases can make logical deductions (i.e. conclude additional facts), based on a set of program rules, derived from facts already in the database. Datalog is a language or family of languages commonly used to specify rules and queries for a deductive database. applications built using Datalog can range from graph mining (such as computing transitive closure or k-cliques) to program analysis (control and data-flow analysis). In our previous papers, we presented the first implementation of a data-parallel Datalog built using MPI. In this paper, we present a parallel I/O system used to checkpoint and restart applications built on top of our Datalog system. State of the art Datalog implementations, such as Souffle, only support serial I/O, mainly because the implementation itself does not support many-node parallel execution. Computing the transitive closure of a graph is one of the simplest logical-inferencing applications built using Datalog;we use it as a micro-benchmark to demonstrate the efficacy of our parallel I/O system. Internally, we use a nested B-tree data-structure to facilitate fast and efficient in-memory access to relational data. Our I/O system therefore involves two steps, converting the application data-layout (a nested B-tree) to a stream of bytes followed by the actual parallel I/O. We explore two popular I/O techniques POSIX I/O and MPI collective I/O. For extracting performance out of MPI Collective I/O we use adaptive striping, and for POSIX I/O we use file-per-process I/O. We demonstrate the scalability of our system at up to 4,096 processes on the Theta supercomputer at the Argonne National Laboratory.

关键词： Checkpointing distributed processing Scalability conferences Supercomputers Deductive databases Task analysis

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Towards Optimized Hydrological Forecast Prediction of WRF-Hydro on GPU 25

Towards Optimized Hydrological Forecast Prediction of WRF-Hy...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Liu, Hongrui Lei, Kelun Yang, Hailong Luan, Zhongzhi Qian, Depei School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798350330014

Hydrological models are designed to simulate and predict the dynamic cycling of surface and groundwater. These models represent simplifications of complex hydrological pro-cesses and play an integral role in hydrological research and real-world applications. The WRF Hydro model is widely used due to its multi-scale capabilities for land-atmosphere modeling. Recent studies have focused on enhancing the performance of different modules in WRF through GPU parallel optimization techniques. However, WRF - Hydro has not been able to utilize the GPU for computations which leads to lower computational efficiency when simulating hydrological calculations over large regions for extended periods. In this paper, we first analyze the computational and communication bottlenecks in WRF Hydro. We then propose several methods for performance optimization accordingly. To address the computational bottleneck, we develop efficient GPU-based overland routing and channel routing, eliminating redundant calculations. Additionally, we adopt an improved block partitioning strategy to enhance memory access efficiency on the GPU. To tackle the communicational bottleneck, we adopt a packetized communication strategy to reduce the number of communication operation calls in channel routing and simplify the preprocessing operations for communication. Meanwhile, for overland routing, we employ multi-stream to minimize redundant synchronization operations and overlap communication with computation to reduce communication overhead. The evaluation results demonstrate that WRF -Hydro can achieve significant speedups in several simulation cases after applying our proposed optimizations, resulting in a maximum speedup of 6.67 ×. © 2023 IEEE.

关键词： Graphics processing unit

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A Dynamic distributed Scheduler for DNN Inference on the Edge

A Dynamic Distributed Scheduler for DNN Inference on the Edg...

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parallel Computing and distributed Systems (PCDS), international Symposium on

作者： Fei Hu Shivakant Mishra Department of Computer Science University of Colorado Boulder CO USA

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Edge computing plays a pivotal role in IoT applications that require rapid and secure data processing. How-ever, these applications are typically resource-demanding, and the resources available at the edge are often significantly less than those available in cloud environments. Thus, optimally utilizing these constrained resources is key to fulfilling various application requirements such as low latency, privacy, and cost-effectiveness. Given the dynamic and hybrid characteristics of IoT environments, static scheduling approaches frequently fail to adequately address these complex and varying demands. This paper presents the design, implementation, and evaluation of a dynamic distributed scheduler tailored for DNN inferences on edge computing platforms, based on sophisticated sub-model profiling techniques. This scheduler's core feature is its ability to continuously monitor the state of the IoT infrastructure, dynam-ically adapting the distribution of computing tasks according to real-time environmental and network conditions. Diverging from previous research that typically segments DNN models into two parts, this study proposes a more granular approach that splits models into multiple segments, thereby maximizing the utilization of diverse edge computing resources. The implementation of this scheduler, including integration with a GPU, demonstrates its effectiveness across various DNN models, highlighting its practical utility in leveraging edge computing capabilities.

关键词： Adaptation models Processor scheduling Computational modeling Graphics processing units Prototypes Dynamic scheduling Real-time systems Internet of Things Resource management Edge computing

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Improving Carbon Emission Predictions with NLP and LSTM Integration

Improving Carbon Emission Predictions with NLP and LSTM Inte...

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Information Science, parallel and distributed Systems (ISPDS), international conference on

作者： Shuang Guo Junfei Chen Business School Hohai University Nanjing China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

In recent years, the urgency of addressing climate change has intensified, necessitating accurate predictions of carbon emissions to inform policy decisions and mitigation strategies. This paper presents an innovative approach to improving carbon emission prediction models by integrating Natural Language processing (NLP) techniques with Long Short-Term Memory (LSTM) networks. Initially, a baseline LSTM model is developed to forecast carbon emissions using historical emission data. Subsequently, we incorporate NLP-processed information extracted from relevant textual sources such as government reports, news articles, and scientific papers. Key features, including keywords, thematic distributions, and sentiment scores, are derived from the text and combined with numerical data to create a multi modal input for the LSTM model. Experimental results demonstrate that the inclusion of NLP-enhanced features significantly enhances the model’s predictive accuracy. This study underscores the potential of leveraging textual data to enrich traditional prediction models, providing a more comprehensive tool for understanding and forecasting carbon emission trends.

关键词： Accuracy Government Carbon dioxide Predictive models Feature extraction Natural language processing Data models

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CNS Lock: Compact NUMA-aware Lock with a Standard Interface

CNS Lock: Compact NUMA-aware Lock with a Standard Interface

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Brahmaiah Gandham Praveen Alapati École Centrale School of Engineering Mahindra University Hyderabad India

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

In recent years, multi-core systems with NonUniform Memory Access (NUMA) architectures are becoming prevalent. Multithreaded applications that execute on NUMA architectures play a pivotal role in getting better performance. The difficulties associated with multi-threaded applications are more pronounced because of compatibility issues with synchronization objects and the underlying NUMA architectures. Locks are fundamental synchronization primitives to avoid data races and achieve high performance in multi-core systems. With the increasing performance demands, locks have been refined and optimized with the architectural features of the NUMA systems. This paper proposes a novel synchronization object, namely CNS Lock (Compact NUMA-aware lock with a Standard interface) for NUMA systems. The CNS lock utilizes a single queue irrespective of the number of NUMA nodes and minimizes the number of atomic operations required for lock acquisition and release by prioritizing threads based on their NUMA node affinity. It considers the current thread’s NUMA node number to get the efficiency of a NUMA-aware design. CNS lock provides a standard interface to facilitate seamless integration into existing applications without knowing the internals. The CNS Lock strategically optimizes lock handovers and minimizes memory latency by employing a unified queue system across NUMA nodes. Our experimental results show that the CNS lock significantly outperforms the state-of-the-art locking mechanisms and makes it one of the essential tools of systems with NUMA architectures for better synchronization.

关键词： Multicore processing Instruction sets Memory architecture Handover Synchronization Spinning Resource management Standards

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Druto: Upper-Bounding Silent Data Corruption Vulnerability in GPU applications

Druto: Upper-Bounding Silent Data Corruption Vulnerability i...

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international Symposium on parallel and distributed processing (IPDPS)

作者： Md Hasanur Rahman Sheng Di Shengjian Guo Xiaoyi Lu Guanpeng Li Franck Cappello University of Iowa IA USA Argonne National Laboratory IL USA Amazon Web Services CA USA University of California Merced CA USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Due to the increasing scale of high-performance computing (HPC) systems, transient hardware faults have become a major reliability concern. Consequently, Silent Data Corruptions (SDCs) due to these faults have been a common insidious consequence in GPU applications. Developers often measure the application resilience with a set of program test inputs available in the benchmark suite, assuming the resilience would not fluctuate much among different inputs. However, we observe that this assumption often results in an over-optimistic evaluation for GPU applications. As a result, the subsequent SDC protection following the evaluation can hardly meet the expected reliability bar in the production environment, where applications would run with potentially arbitrary input values. To this end, we propose Druto – a compiler-based automated technique that searches for inputs to incrementally approach the upper bound of a GPU application’s SDC probability. We develop Druto based on the property that the resilience profiles of a small group of representative threads in a GPU kernel can approximately rank various inputs in terms of the overall SDC probability. Therefore, Druto strategically steers the search towards new program inputs that efficiently portray the overall SDC probability. Evaluation shows that the SDC probability derived from Druto’s input generation is as much as 74× higher than that from existing techniques. Moreover, existing techniques cannot find our generated inputs even given 5× more search time.

关键词： Upper bound High performance computing Graphics processing units Production Hardware Reliability Kernel

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Facilitating Decentralized and Opportunistic Learning in Pervasive Computing

Facilitating Decentralized and Opportunistic Learning in Per...

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20th IEEE international conference on Pervasive Computing and Communications (IEEE PerCom)

作者： Lee, Sangsu Julien, Christine Zheng, Xi Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Macquarie Univ Dept Comp N Ryde NSW Australia

ISBN: (纸本)9781665416474

We explore algorithms and develop tools to support the collaboration between devices to build personalized deep learning models for pervasive computing applications. In our work, we first investigate whether individual devices belonging to different users can learn robust models that are personalized to their user's own experiences while preserving the privacy of the data that resides on the devices. We term this approach Opportunistic Collaborative Learning (OppCL), and we build a resilient learning algorithm that ameliorates the negative effects when coping with unpredictable mobility patterns. Moreover, we extend OppCL from networks of homogeneous devices to heterogeneous ones by employing lossy compression techniques for deep learning models. Furthermore, we develop a programming environment for developers to test decentralized learning algorithms in scale.

关键词： pervasive computing federated learning collaborative deep learning distributed machine learning

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Comparative Study of Map Reduce Task Scheduling Optimization techniques

Comparative Study of Map Reduce Task Scheduling Optimization...

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Evolutionary Algorithms and Soft Computing techniques (EASCT), international conference on

作者： Vishal Kumar Sumit Kushwaha Computer Applications Department Chandigarh University Gharuan Mohali India

As data processing capabilities improved, the flow of data increased significantly, necessitating the use of distributed systems and a new programming framework, MapReduce, to manage this massive amount of information. Any job requiring a large amount of data must be divided into subtasks for parallel execution. In order for all subtasks to be executed concurrently need to be scheduled parallelly on available set of virtual machines. This is not an easy task; it requires consideration of a number of critical parameters such as make-span, Execution cost, risk of execution, energy consumption and so on. The optimization of these parameters is critical for effective scheduling. The most effective hybrid scheduling techniques have been developed and implemented on a single platform with a diverse set of virtual machines. On a single platform, it compares all well-known and popular scheduling techniques. All experimental values for various parameters of interest for all best-ever scheduling techniques will be recorded for analysis, and results representing parameter-wise best-available scheduling techniques will be presented. This comparative study aids in the direction of future research and serves as a guide for progress from the current state of the research.

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