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RainCloud: Decentralized Coordination and Communication in Heterogeneous IoT Swarms 1

RainCloud: Decentralized Coordination and Communication in H...

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1st international symposium on parallel computing and distributed Systems, PCDS 2024

作者： Loisel, Filip Zeqo, Geri Morichetta, Andrea Lackinger, Anna Dustdar, Schahram DSG@TU Wien Auxo GmbH Austria Distributed Systems Group Tu Wien Austria

ISBN: (纸本)9798350349658

The increasing volume and complexity of IoT systems demand a transition from the cloud-centric model to a decentralized IoT architecture in the so called computing Continuum, with no or minimal reliance on central servers. This paradigm shift, however, raises novel research concerns for decentralized coordination, calling for accurate policies. However, building such strategies is not trivial. Our work aims to relieve the DevOps engineers from this concern and propose a solution for autonomous, decentralized task allocation at runtime for IoT systems. To this end, we present a semantic communication approach and an ad-hoc lightweight coordination strategy based on Ant Colony Optimization (ACO). We compare the ACO strategy with Random Search and Gossip protocol-based algorithms. We conduct accurate experiments with up to a hundred nodes in both a static and a dynamic environment, i.e., with device outages. We show that ACO finds a matching node with the smallest hops and messages sent. While the Gossip strategy can allocate the most tasks successfully, ACO scales better, thus being a promising candidate for decentralized task coordination in IoT clusters. © 2024 IEEE.

关键词： Ant colony optimization

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FASOP: Fast yet Accurate Automated Search for Optimal parallelization of Transformers on Heterogeneous GPU Clusters 24

FASOP: Fast yet Accurate Automated Search for Optimal Parall...

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33rd international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Hwang, Sunyeol Lee, Eungyeong Oh, Hongseok Yi, Youngmin Univ Seoul Seoul South Korea

ISBN: (纸本)9798400704130

Transformer-based large language models have recently shown remarkable performance, but their significantly large parameters require efficient training, which is commonly realized by utilizing both data- and model-parallel deep learning on a GPU cluster. To minimize the training time, the optimal degrees of data and model parallelisms and the optimal model partitioning should be searched. When heterogeneous GPU clusters are used to utilize as many GPUs as possible, it becomes more challenging. In this work, we propose a framework named FASOP that automatically and rapidly finds the (near-)optimal degrees of parallelisms and model partitioning of Transformer-based models on heterogeneous GPU clusters, with an accurate estimation of pipelining latency and communications. Moreover, it can search for optimal cluster configurations that minimize the training time while satisfying the cost of GPU clusters. The proposed model partitioning algorithm in FASOP is three orders of magnitude faster than Dynamic Programming in the state-of-the-art for GPT-2 1.5B on a mixed set of 32 GPUs with A100 and A10, leading to a few seconds instead of several hours. And, FASOP shows only 8.7% mean absolute error in training time estimation for GPT-2 1.5B. With a fast yet accurate search, FASOP achieved up to 1.37x speedup compared to Megatron-LM.

关键词： Heterogeneous cluster distributed deep learning parallelization strategy Large language model

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CLOUD-BASED FINE-GRAINED parallel OPTIMIZATION ON CPU-GPU HETEROGENEOUS HYPERSPECTRAL IMAGE SUPERPIXEL SPACE-SPECTRUM FUSION CLASSIFICATION ALGORITHMS

CLOUD-BASED FINE-GRAINED PARALLEL OPTIMIZATION ON CPU-GPU HE...

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IEEE international Geoscience and Remote Sensing symposium (IGARSS)

作者： Wang, Hongfei Tao, Zhigang Wu, Zebin Zhang, Yi Zhou, Junlong Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Res Inst Elect Engn NRIEE Nanjing 210000 Peoples R China

ISBN: (纸本)9798350320107

To meet the need for efficient execution of hyperspectral remote sensing image classification algorithms, this paper proposes a fine-grained parallel optimization method for a CPU-GPU heterogeneous hyperspectral image superpixel spectral fusion classification algorithm based on cloud computing. Ray is used as the distributed computing engine to fully utilize the logical control ability and large-scale parallel computing ability of the CPU-GPU heterogeneous platform. We first decouple the superpixel spectral fusion classification algorithm, analyze the data dependence and computing characteristics of sub-tasks, use the GPU to accelerate the algorithm, and then further extend the algorithm to the CPU-GPU heterogeneous platform. At the same time, we establish a scheduling model for algorithm task scheduling problems, specifying the value of the parallelism degree for the algorithm in a fine-grained manner. It is verified by experiments that the parallelization method proposed in this paper can effectively improve the execution efficiency with the premise of the accuracy unreduced.

关键词： Hyperspectral Image Classification superpixel spatial-spectral fusion fine-grained parallel CPU-GPU heterogeneous cloud computing

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Energy Optimization through a Multidimensional distributed Scheduling Approach 23

Energy Optimization through a Multidimensional Distributed S...

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23rd international symposium on parallel and distributed computing, ISPDC 2024

作者： Valera, Humberto Giraldo, Leonel Isaac Guerrero Mutnoz, Carlos Alejandro Sivira Marroquin, Alan Alfredo Rojas Damas, Lucas Aguilar Dalmau, Marc Roose, Philippe Cardinale, Yudith Technopôle Domolandes Saint-Geours-de-Maremne France Universidad Simón Bolivar Caracas Venezuela Universidad Católica San Pablo Arequipa Peru Universidad Internacional de Valencia Spain E2S Uppa University of Pau Anglet France

ISBN: (纸本)9798350369199

distributed systems, spanning pervasive, cloud, and edge computing, require robust scheduling for tasks and data management, facing challenges like hardware diversity, QoS, localization, and energy efficiency. Current solutions often lack a comprehensive approach, failing to achieve balanced load management, QoS, and notably in minimizing application energy use, a key modern concern. We propose a distributed scheduler using multidimensional spaces and data structures aimed at comprehensive load balancing, energy efficiency, and QoS. The scheduler indexes devices within the structure based on diverse criteria such as hardware availability, GPS, and energy features. It then organizes applications into abstract graphs, where nodes are containers and their data items (units of data accessed by the containers), and edges are the transfer rates among them. To migrate containers, the scheduler performs range queries within the structure for optimal devices. To organize data items, it finds the barycenter of the devices executing the containers linked to them. We evaluated our scheduler's effectiveness using the PISCO simulator, comparing it against other energy-efficient schedulers. © 2024 IEEE.

关键词： Energy utilization

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Performance Analysis of Image Classification between Edge and Cloud computing 1

Performance Analysis of Image Classification between Edge an...

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1st international symposium on parallel computing and distributed Systems, PCDS 2024

作者： Vechprasit, Natthasak Monsakul, Annop Boonsieng, Pramuk Thai-Nichi Institute of Technology Faculty of Information Technology Bangkok Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand

ISBN: (纸本)9798350349658

Image classification has become a major application in the AI era for connecting the physical and digital worlds. However, it requires intensive graphic processing power. IoT and Edge computing have become popular approaches for distributing and offloading the workload from the cloud to the edge. Many edge devices are powered by an energy-efficient processor that can't execute intensive workloads, but some may be able to. In this paper, we studied the overview of image classification implementation on edge and cloud computing and analyzed the performance to reveal the opportunity to implement a proper system architecture. The edge and cloud computing environments studied in this paper are a smartphone and a cloud GPU instance with sample applications to simulate real-world scenarios. The performance is based on the datasets and the processing environment comprising three factors: ML runtime, hardware, and network. Resulting in six factors: inference time, end-to-end execution time (including network delays), accuracy, confidence score, resource usage, and data transfer. © 2024 IEEE.

关键词： Cloud platforms

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distributed Checkpointing in Dataflow with Static Scheduling 35

Distributed Checkpointing in Dataflow with Static Scheduling

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35th IEEE international symposium on Computer Architecture and High Performance computing (SBAC-PAD)

作者： Alves, Tiago A. O. Univ Estado Rio De Janeiro Rio De Janeiro Brazil

ISBN: (纸本)9798350381603

The Dataflow model, where instructions or tasks are fired as soon as their input data is ready, was proven to be a good fit for parallel/distributed computation. Previous works have presented DFER (Dataflow Error Recovery Model), that allows transient error and recovery in dataflow by adding special tasks and edges to the dataflow graph itself. However, permanent faults or faults that cause a processing element (PE) to become irresponsive are not addressed by DFER. For those cases it is necessary to adopt a checkpointing method. Since the whole purpose of Dataflow is to achieve high levels of parallelism and explore the potential asynchronicity between PEs, it is clear that the checkpointing method adopted must be uncoordinated and distributed. Current algorithms for distributed checkpointing rely solely on guaranteeing that causality between checkpoints can be trackable. In the context of Dataflow with static scheduling, i.e. when the dataflow graph is partitioned among the available PEs at compile-time, causality trackability is not sufficient as we will show. Since static scheduling of dataflow graphs is very important in various scenarios, it calls for a new algorithm for distributed checkpointing that can be adopted for the execution of statically scheduled dataflow graphs. In this paper we describe why the ability to track causality is not enough for statically scheduled dataflow and introduce a new algorithm for distributed checkpointing specifically tailored for such model of execution.

关键词： dataflow distributed checkpointing fault tolerance parallel programming

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Porting a Computational Fluid Dynamics Code with AMR to Large-scale GPU Platforms 37

Porting a Computational Fluid Dynamics Code with AMR to Larg...

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37th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Davis, Joshua H. Shafner, Justin Nichols, Daniel Grube, Nathan Martin, Pino Bhatele, Abhinav Univ Maryland Dept Comp Sci College Pk MD 20742 USA Univ Maryland Dept Aerosp Engn College Pk MD 20742 USA

ISBN: (纸本)9798350337662

Accurate modeling of turbulent hypersonic flows has tremendous scientific and commercial value, and applies to atmospheric flight, supersonic combustion, materials discovery and climate prediction. In this paper, we describe our experiences in extending the capabilities of and modernizing CRoCCo, an MPIbased, CPU-only compressible computational fluid dynamics code. We extend CRoCCo to support block-structured adaptive mesh refinement using a highly-scalable AMR library, AMReX, and add support for a fully curvilinear solver. We also port the computational kernels in CRoCCo to GPUs to enable scaling on modern exascale systems. We present our techniques for overcoming performance challenges and evaluate the updated code, CRoCCo v2.0, on the Summit system, demonstrating a 6x to 44x speedup over the CPU-only version.

关键词： hypersonics computational fluid dynamics adaptive mesh refinement GPU computing

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Accelerating Function-Centric Applications by Discovering, Distributing, and Retaining Reusable Context in Workflow Systems 24

Accelerating Function-Centric Applications by Discovering, D...

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33rd international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Thanh Son Phung Thomas, Colin Ward, Logan Chard, Kyle Thain, Douglas Univ Notre Dame Notre Dame IN 46556 USA Argonne Natl Lab Lemont IL USA Univ Chicago Chicago IL USA

ISBN: (纸本)9798400704130

Workflow systems provide a convenient way for users to write large-scale applications by composing independent tasks into large graphs that can be executed concurrently on high-performance clusters. In many newer workflow systems, tasks are often expressed as a combination of function invocations in a high-level language. Because necessary code and data are not statically known prior to execution, they must be moved into the cluster at runtime. An obvious way of doing this is to translate function invocations into self-contained executable programs and run them as usual, but this brings a hefty performance penalty: a function invocation now needs to piggyback its context with extra code and data to a remote node, and the remote node needs to take extra time to reconstruct the invocation's context before executing it, both detrimental to lightweight short-running functions. A better solution for workflow systems is to treat functions and invocations as first-class abstractions: subsequent invocations of the same function on a worker node should only pay for the cost of context setup once and reuse the context between different invocations. The remaining problems lie in discovering, distributing, and retaining the reusable context among workers. In this paper, we discuss the rationale and design requirement of these mechanisms to support context reuse, and implement them in TaskVine, a data-intensive distributed framework and execution engine. Our results from executing a large-scale neural network inference application and a molecular design application show that treating functions and invocations as first-class abstractions reduces the execution time of the applications by 94.5% and 26.9%, respectively.

关键词： Workflow Systems Serverless computing distributed Storage Burst Buffers

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HM2: Efficient Host Memory Management for RDMA-Enabled distributed Systems 23

HM2: Efficient Host Memory Management for RDMA-Enabled Distr...

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32nd international symposium on High-Performance parallel and distributed computing (HPDC) part of the ACM Federated computing Research Conference (FCRC)

作者： Tang, Chen Chu, Zhaole Jin, Peiquan Luo, Yongping Guo, Kuankuan Univ Sci Technol China Hefei Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798400701559

Remote direct memory access (RDMA) supports zero-copy networking by transferring data from clients directly to host memory, eliminating the need to copy data between clients' memory and the data buffers in the hosting server. However, the hosting server must design efficient memory management schemes to handle incoming clients' data. In this paper, we propose a high-performance host memory management scheme called HM2 for RDMA-enabled distributed systems. We present a new buffer structure for incoming data from clients. In addition, we propose efficient data processing methods to reduce network transfers between clients and servers. We conducted a preliminary experiment to evaluate HM2, and the results showed HM2 achieved higher throughput than existing schemes, including L5 and FaRM.

关键词： RDMA memory management data buffer

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A Communication-efficient Collaborative Task Offloading Method Based on distributed computing Cloud 22

A Communication-efficient Collaborative Task Offloading Meth...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Zhang, Ke Zong, Ruixing Song, Yingjie Zhou, Yong Xiao, Xiong Hunan University Changsha China

ISBN: (纸本)9798331509712

An interconnected distributed computing cloud, comprising network-linked computational nodes, facilitates the allocation of computational resources across diverse locations. The strategic offloading of computationally demanding tasks from resource-constrained nodes to proximate nodes endowed with ample resources can enhance the resource utilization within the computing cloud. Given the interdependencies among segmented tasks, improper task partitioning may engender supplementary overheads. Furthermore, the process of task offloading introduces additional communication overhead. Consequently, the judicious division of task components and the selection of appropriate offloading destinations assume paramount significance in mitigating collaborative computing complexity and communication overhead. To address these challenges, we propose a communication-efficient collaborative task offloading methodology, which entails the segmentation and offloading of computation-intensive tasks from the source node to one or more high-performance nodes to fully leverage device resources. Additionally, we devise a subtask placement strategy predicated on minimizing communication distances, whereby multiple subtasks are allocated to distinct nodes based on the calculated communication latencies between the source node and various computing nodes. Experiments demonstrate that our task offloading method exhibits the lowest overall loss. © 2024 IEEE.

关键词： distributed cloud

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