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E-VM: An Elastic Virtual Machine Scheduling Algorithm to Minimize the Total Cost of Ownership in a Hybrid Cloud 19

E-VM: An Elastic Virtual Machine Scheduling Algorithm to Min...

引用

19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Shetti, Milan M. Li, Bingzhe Du, David H. C. Rocket Software Waltham MA 02451 USA Oklahoma State Univ Stillwater OK 74078 USA Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781665435741

A hybrid cloud that combines both public and private clouds is becoming more and more popular due to the advantages of improved security, scalability, and guaranteed SLA (Service-Level Agreement) at a lower cost than a separate private or public cloud. The existing studies rarely consider VM migrations in a hybrid cloud environment with dynamically changed VM workloads. From an enterprise's perspective, these migrations are necessary to minimize the cost of utilizing public clouds and guarantee SLAs of VMs in a hybrid cloud environment. In this paper, we propose an elastic VM allocation and migration algorithm for a hybrid cloud, called E-VM, to fully utilize the resources in a private cloud and to minimize the cost of using a public cloud while guaranteeing the SLAs of all VMs. The E-VM considers the bi-direction migration between private and public clouds. Two components, VM-predictor and VMselector, are designed and implemented in E-VM to determine if a migration has to be triggered between private and public clouds and which VMs will be migrated to the opposite cloud, respectively. Moreover, E-VM is designed based on the existing public cloud pricing models and can be easily adapted to any cloud service provider. According to simulator results based on a set of captured industrial VM traces/workloads and additional experiments directly on a real-world hybrid cloud, the proposed E-VM can significantly reduce the total cost of using the public cloud compared to the existing VM migration schemes.

关键词： Cloud computing Costs Web services Scheduling algorithms Scalability Pricing Bidirectional control

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Bandwidth Characterization of DeepSpeed on distributed Large Language Model Training

Bandwidth Characterization of DeepSpeed on Distributed Large...

引用

IEEE international symposium on Performance Analysis of systems and software

作者： Bagus Hanindhito Bhavesh Patel Lizy K. John The University of Texas at Austin Austin Texas USA Dell Technologies Round Rock Texas USA

ISBN: (数字)9798350376388

ISBN: (纸本)9798350376395

The exponential growth of the training dataset and the size of the large language model (LLM) significantly outpaces the incremental memory capacity increase in the graphics pro-cessing units (GPUs). Thousands of GPUs are needed to handle state-of-the-art models, which require building an expensive AI GPU cluster that is out of reach for most researchers. This not only makes the cost to train the model more costly but also signifies the environmental impact. To improve the efficiency and scalability of existing infrastructure to handle increasingly demanding training tasks, Microsoft released DeepSpeed, an open-source optimization library for PyTorch that can easily be integrated into existing training flow with minimal code changes. This paper presents a comprehensive third-party evaluation of DeepSpeed for training GPT-2-like LLM on mainstream GPU clusters that are more accessible to everyone. The evaluation includes memory usage analysis and bandwidth characterization in addition to the achieved model size and the attained compute throughput to help compare horizontal and vertical scaling. First, we examine the DeepSpeed ZeRO in single- and dual-node training against the popular distributed training libraries: PyTorch distributed Data-parallel (DDP) with data parallelism and Megatron-LM with data and model parallelism. While DDP achieves higher throughput due to less communication, the model size is limited to a single GPU memory capacity. In single-node training, Megatron-LM can fit a 4x larger model than the DDP, while ZeRO can handle a model with 0.8x-l.2x size of the Megatron-LM. Both Megatron-LM and ZeRO are reasonably competitive in terms of throughput. However, in dual-node training, Megatron- Lmsees a significant drop in throughput due to the excessive inter-node communication, achieving only 25 %-30 % of the throughput offered by ZeRO. Secondly, we evaluate ZeRO-Offload to consolidate multi-node training into single-node. With CPU offloading, ZeRO-Offloa

关键词： Training Analytical models Nonvolatile memory Computational modeling Graphics processing units distributed databases Bandwidth

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Q2Logic: A Coarse-Grained FPGA Overlay targeting Schrödinger Quantum Circuit Simulations

Q2Logic: A Coarse-Grained FPGA Overlay targeting Schrödinge...

引用

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

作者： Artur Podobas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Quantum computing is emerging as an important (but radical) technology that might take us beyond Moore’s law for certain applications. Today, in parallel with improving quantum computers, computer scientists are relying heavily on quantum circuit simulators to develop algorithms. Most existing quantum circuit simulators run on general-purpose CPUs or GPUs. However, at the same time, quantum circuits themselves offer multiple opportunities for parallelization, some of which could map better to other architecture– architectures such as reconfigurable systems. In this early work, we created a quantum circuit simulator system called Q2Logic. Q2Logic is a coarse-grained reconfigurable architecture (CGRA) implemented as an overlay on Field-Programmable Gate Arrays (FPGAs), but specialized towards quantum simulations. We described how Q2Logic has been created and reveal implementation details, limitations, and opportunities. We end the study by empirically comparing the performance of Q2Logic (running on a Intel Agilex FPGA) against the state-of-the-art framework SVSim (running on a modern processor), showing improvements in three large circuits(#qbit≥27), where Q2Logic can be up-to 7x faster.

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Clustering Information-guided Multi-view Contrastive Graph Clustering

Clustering Information-guided Multi-view Contrastive Graph C...

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international Conference on parallel and distributed systems (ICPADS)

作者： Yaliang Zhao Tianchi Zhang Jinke Wang School of Computer and Information Engineering Henan University Kaifeng China School of Software Henan University Kaifeng China

Nowadays some progress has been made in learning the representation of nodes in unlabeled multi-view for clustering. However, current multi-view graph clustering methods ignore important clustering information during training leading to limited performance. In this paper, we introduce a new framework for multi-view graph clustering, known as IGMC. It utilizes both node attributes and graph topology and uses network topology and the pseudo-labels obtained during the training process as supervisory signals to construct pairs of samples with high discriminative ability and high reliability so that each node corresponds to multiple positive samples. In addition, the clustering quality of the node representations is improved by maximizing the mutual information between the node and the corresponding clusters to emphasize the classes of the nodes. IGMC produces better results than advanced methods on several challenging datasets. The source code is available at https://***/tczgithub/IGMC.

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Multi-Criteria Mesh Partitioning for an Explicit Temporal Adaptive Task-distributed Finite-Volume Solver

Multi-Criteria Mesh Partitioning for an Explicit Temporal Ad...

引用

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

作者： Alice Lasserre Jean-Marie Couteyen-Carpaye Abdou Guermouche Raymond Namyst Inria France Airbus CRT France Université de Bordeaux France

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

The aerospace industry is one of the largest users of numerical simulation, which is an essential tool in the field of aerodynamic engineering, where many fluid dynamics simulations are involved. In order to obtain the most accurate solutions, some of these simulations use unstructured finite volume solvers that cope with irregular meshes by using explicit time-adaptive integration methods. Modern parallel implementations of these solvers rely on task-based runtime systems to perform fine-grained load balancing and to avoid unnecessary synchronizations. Although such implementations greatly improve performance compared to a classical fork-join MPI+OpenMP variants, it remains a challenge to keep all cores busy throughout the simulation loop. In this article, we first investigate the origins of this lack of parallelism. We emphasize that the irregular structure of the task graph plays a major role in the inefficiency of the computation distribution. Our main contribution is to improve the shape of the task graph by using a new mesh partitioning strategy. The originality of our approach is to take the temporal level of mesh cells into account during the mesh partitioning phase. We evaluate our approach by integrating our solution in an ArianeGroup production code used by Airbus. We show that our partitioning method leads to a more balanced task graph. The resulting task scheduling is up to two times faster for meshes ranging from 200,000 to 12,000,000 components.

关键词： Time-frequency analysis Solid modeling Accuracy Shape Computational modeling Fluid dynamics Production

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Research on Pulsed Power Decoupling Topology and Its Control Strategy Using Dual-Inductance Active Capacitor Unit

Research on Pulsed Power Decoupling Topology and Its Control...

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IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Linyan Zhang Ping Yang Wenrong Wu Xingjiang Shen Xi Chen Department of Electrical Engineering Southwest Jiaotong University Chengdu China

The input power of the pulsed power supply system (PPS) is required to be constant, and its output power is pulsed. To balance the instantaneous power difference, the single-inductance active capacitor unit (ACU) is often connected in parallel to the output of the PPS. However, the single-inductance ACU has a transient process of the current reverse of the inductor, which will lead to serious output current spikes of the pre-stage power supply system. In this paper, the dual-inductance ACU is adopted in place of the single-inductance ACU to suppress the transient process of inductor current reversal. In order to improve the dynamic performance and reduce the tracking error of the compensation current of the dual-inductance ACU, a nonlinear feedforward PID control strategy is proposed. Finally, an experimental prototype is built to verify the validity of the proposed pulsed power decoupling topology and its control strategy.

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A Novel Isolated DC-DC Converter with Configurable Structure for Wide Output Voltage Range Operation

A Novel Isolated DC-DC Converter with Configurable Structure...

引用

IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Wei Li Yongzheng Qu Yifang Du Yanbing Zhang Yinghu Liu Yexian County Power Supply State Grid Henan Electric Power Henan China School of Electrical Engineering Xi’an Jiaotong University Xi’an China

Traditional LLC converter is widely used for renewable energy application due to its wide output voltage range. However, LLC converter achieves wide output voltage range by changing the switching frequency, which leads to difficulties in magnetic component design. To overcome this issue, a novel isolated DC-DC converter with configurable structure is proposed. It assembles a H-bridge, a LC resonant tank and two rectifier units. The proposed converter enables four-times adjustment of output voltage by simply arranging the configurable rectifier units in parallel or series in pulse width modulation manner at a fixed switching frequency. Compared with the traditional LLC converter, the proposed converter reduces the switch capacity by 32.8%. The feasibility of the proposal is verified by simulation in MATLAB/Simulink.

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Optimizing Cloud Computing Resource Usage for Hemodynamic Simulation

Optimizing Cloud Computing Resource Usage for Hemodynamic Si...

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international symposium on parallel and distributed Processing (IPDPS)

作者： William Ladd Christopher Jensen Madhurima Vardhan Jeff Ames Jeff R. Hammond Erik W. Draeger Amanda Randles Biomedical Engineering Duke University Durham NC USA HPC Software NVIDIA Helsinki Oy Helsinki Finland Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA USA

Cloud computing resources are becoming an increasingly attractive option for simulation workflows but require users to assess a wider variety of hardware options and associated costs than required by traditional in-house hardware or fixed allocations at leadership computing facilities. The pay-as-you-go model used by cloud providers gives users the opportunity to make more nuanced cost-benefit decisions at runtime by choosing hardware that best matches a given workload, but creates the risk of suboptimal allocation strategies or inadvertent cost overruns. In this work, we propose the use of an iteratively-refined performance model to optimize cloud simulation campaigns against overall cost, throughput, or maximum time to solution. Hemodynamic simulations represent an excellent use case for these assessments, as the relative costs and dominant terms in the performance model can vary widely with hardware, numerical parameters and physics models. Performance and scaling behavior of hemodynamic simulations on multiple cloud services as well as a traditional compute cluster are collected and evaluated, and an initial performance model is proposed along with a strategy for dynamically refining it with additional experimental data.

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AFTP: An Adaptive High Cost-Effectiveness Fault-Tolerant NoC Design Based on Prediction

AFTP: An Adaptive High Cost-Effectiveness Fault-Tolerant NoC...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Xiaowen Chen Ta Tan Chen Li Jianzhuang Lu Qijin Zhu School of Computer National University of Defense Technology Changsha China Key Laboratory of Advanced Microprocessor Chips and Systems Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Network-on-Chip (NoC), known for its high bandwidth and scalability, is extensively utilized in chip multiprocessors. However, as technology advances to the nanometer scale, NoC is becoming increasingly vulnerable to errors caused by crosstalk, radiation, electromagnetic interference, and other factors. In addition to ensuring network reliability, designers must consider the overhead instead of blindly pursuing fault-tolerance capabilities in NoC design. In this paper, we analyze the characteristics of conventional End-to-End (E2E) and Switch-to-Switch (S2S) designs and propose AFTP, an adaptive high cost-effectiveness fault-tolerant NoC design based on prediction. Our design prioritizes stronger protection for head flits and relaxed protection for body and tail flits, and it is capable of dynamically adjusting the decoding times for flits in response to changes in error rate, aiming to achieve a balance between the overhead and the reliability of NoC. Our design demonstrates a significant improvement in cost-effectiveness compared to conventional E2E and S2S designs, achieving a 5.9x and 8.9x improvement, respectively, under common synthetic traffic and PARSEC benchmarks.

关键词： Fault tolerance Error analysis Fault tolerant systems Tail Switches Benchmark testing Reliability engineering Decoding Topology Protection

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Federated Multi-Agent Reinforcement Learning for AoI Minimization in UAV-Enabled IoV-MEC systems

Federated Multi-Agent Reinforcement Learning for AoI Minimiz...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Shoukun Xu Yu Zhang Xueyuan Wang M. Cenk Gursoy Academy of Computer Science and Artificial Intelligence Changzhou University Changzhou China Department of Electrical Engineering and Computer Science Syracuse University Syracuse NY USA

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the advancement of Mobile Edge Computing (MEC), effective solutions for communication scenarios, including the industrial Internet of Things (IoT) and the Internet of Vehicles (IoV), are becoming increasingly feasible. Unmanned aerial vehicles (UAVs) can further enhance flexibility in delivering computational services within MEC contexts. Addressing the urgent need for information freshness, we propose a three-tier IoV-MEC system, supported by multiple UAVs and a cloud center, aiming to minimize the system's average age of information (AoI). We propose a heterogeneous multi-agent reinforcement learning algorithm based on the actor-critic framework, where vehicles act as data sources, UAVs serve as edge devices, and the cloud acts as a control center. All three tiers learn interaction strategies cooperatively based on the observations. To further enhance system performance, we implement an efficient federated learning method, allowing same-tier agents to share learning parameters, thus improving system performance and convergence speed. Extensive simulation results demonstrate that the proposed algorithm outperforms baseline algorithms in terms of average AoI and convergence speed.

关键词： Cloud computing System performance Heuristic algorithms Soft sensors Simulation Reinforcement learning Robustness Real-time systems Vehicle dynamics Convergence

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