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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

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GPU-Enabled Asynchronous Multi-level Checkpoint Caching and Prefetching 23

GPU-Enabled Asynchronous Multi-level Checkpoint Caching and ...

引用

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

作者： Maurya, Avinash Rafique, M. Mustafa Tonellot, Thierry AlSalem, Hussain J. Cappello, Franck Nicolae, Bogdan Rochester Inst Technol Rochester NY 14623 USA Saudi Aramco Explorat & Petr Engn Adv Res Ctr Dhahran Saudi Arabia Argonne Natl Lab Lemont IL USA

ISBN: (纸本)9798400701559

Checkpointing is an I/O intensive operation increasingly used by High-Performance computing (HPC) applications to revisit previous intermediate datasets at scale. Unlike the case of resilience, where only the last checkpoint is needed for application restart and rarely accessed to recover from failures, in this scenario, it is important to optimize frequent reads and writes of an entire history of checkpoints. State-of-the-art checkpointing approaches often rely on asynchronous multi-level techniques to hide I/O overheads by writing to fast local tiers (e.g. an SSD) and asynchronously flushing to slower, potentially remote tiers (e.g. a parallel file system) in the background, while the application keeps running. However, such approaches have two limitations. First, despite the fact that HPC infrastructures routinely rely on accelerators (e.g. GPUs), and therefore a majority of the checkpoints involve GPU memory, efficient asynchronous data movement between the GPU memory and host memory is lagging behind. Second, revisiting previous data often involves predictable access patterns, which are not exploited to accelerate read operations. In this paper, we address these limitations by proposing a scalable and asynchronous multi-level checkpointing approach optimized for both reading and writing of an arbitrarily long history of checkpoints. Our approach exploits GPU memory as a first-class citizen in the multi-level storage hierarchy to enable informed caching and prefetching of checkpoints by leveraging foreknowledge about the access order passed by the application as hints. Our evaluation using a variety of scenarios under I/O concurrency shows up to 74x faster checkpoint and restore throughput as compared to the state-of-art runtime and optimized unified virtual memory (UVM) based prefetching strategies and at least 2x shorter I/O wait time for the application across various workloads and configurations.

关键词： High-Performance computing (HPC) Graphics Processing Unit (GPU) asynchronous multi-level checkpointing hierarchical cache management prefetching

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Xonar: Profiling-based Job Orderer for distributed Deep Learning 15

Xonar: Profiling-based Job Orderer for Distributed Deep Lear...

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15th IEEE international conference on Cloud computing (IEEE CLOUD) / IEEE World Congress on Services (IEEE SERVICES)

作者： Shin, Changyong Yang, Gyeongsik Yoo, Yeonho Lee, Jeunghwan Yoo, Chuck Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (数字)9781665481373

ISBN: (纸本)9781665481373

Deep learning models have a wide spectrum of GPU execution time and memory size. When running distributed training jobs, however, their GPU execution time and memory size have not been taken into account, which leads to the high variance of job completion time (JCT). Moreover, the jobs often run into the GPU out-of-memory (OoM) problem so that the unlucky job has to restart all over. To address the problems, we propose Xonar to profile the deep learning jobs and order them in the queue. The experiments show that Xonar with TensorFlow v1.6 reduces the tail JCT by 44% with the OoM problem eliminated.

关键词： GPU cloud distributed deep learning parallel training GPU utilization job completion time

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A Deeply Pipelined 64-bit Multiplier for High-Performance RISC-V Processors 6

A Deeply Pipelined 64-bit Multiplier for High-Performance RI...

引用

6th international conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Liu, Wenyi Hu, Feng Li, Guilan Xu, Bangjian Niu, Xin College of Computer Science and Electronic Hunan University Changsha China College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Laboratory Changsha China

ISBN: (纸本)9798331541750

The multiplier is an important component of the processor's computing unit. Multiplication, multiplication, addition, and multiplication and subtraction operations are widely used in various signal processing algorithms. Based on this, this article extends the multiplication instructions of RISC-V, designs a 64-bit multiplier that combines the booth2 algorithm and wallace compression technology, and designs a deep pipeline mechanism for the multiplier to improve performance. Finally, Through logical simulation and emulation, the result output is correct. Comprehensive results show that under the 28nm cmos process, the MAC unit can reach 2.22Ghz. © 2024 IEEE.

关键词： Pipeline processing systems

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DCNN-based load balancing and computation offloading of Intelligent Video Surveillance with Edge computing

DCNN-based load balancing and computation offloading of Inte...

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2023 international conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Koparde, Shweta Sujatha, G. Jangirala, Srinivas Kumar, P. Santhosh Dr. Dy Patil Institute of Technology Computer Engineering Pune India Kommuri Pratap Reddy Institute of Technology Computer Science and Engineering Ghatkeshar India O.P. Jindal Global University Jindal Global Business School Sonipat India Srm Institute of Science and Technology Information Technology College of Engineering and Technology Ramapuram Chennai India

ISBN: (纸本)9798350313413

In this study, we offer a distributed Intelligent Video Surveillance (DIVS) system that is set up in an environment of edge computing and is based on Deep Learning (DL). For the DIVS system, we developed a distributed DL training model and a multi-layer edge computing architecture. In order to lower the high network connection overhead and offer precise and low-latency video analysis solutions, the DIVS system may move computing processes from the network core to the network edges. We implement the proposed DIVS system to address the workload balance, model synchronization, and concurrent training problems. We offer task-level parallel and model-level parallel training approaches to further accelerate the video analysis process. Deep Convolutional Neural Networks with Load Balancing and Computation Offloading (DCNN-LBCO) is a technique for creating a scalable cloud-based online video platform. In order to minimize security concerns, we provide a particular security layer for MEC systems in this study along with a load balancing and computation offloading technique that work together. A load balancing and computation offloading (LBCO) technique is first recommended in order to distribute MDU among sBSs effectively. A specific advanced encryption standard (AES) cryptographic method is also provided, together with an encryption and decryption key based on electrocardiogram (ECG) signals, to prevent data vulnerability during transmission. A load balancing, compute offloading, and security model that is integrated is provided to lessen the system's time and energy requirements. Extensive testing results show that, as compared to local execution, our technique may save between 68.2% and 72.4% of system utilisation, both with and without extra security layers. © 2023 IEEE.

关键词： advanced encryption standard (AES) Deep Convolutional Neural Networks (DCNN) Deep Learning (DL) distributed Intelligent Video Surveillance electrocardiogram (ECG)

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An Efficient distributed Graph Engine for Deep Learning on Graphs

An Efficient Distributed Graph Engine for Deep Learning on G...

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2023 international conference on High Performance computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Deng, Gangda Akgül, Ömer Faruk Zhou, Hongkuan Zeng, Hanqing Xia, Yinglong Li, Jianbo Prasanna, Viktor University of Southern California Los AngelesCA United States Meta Menlo ParkCA United States

ISBN: (纸本)9798400707858

Traditional graph-processing algorithms have been widely used in Graph Neural Networks (GNNs). This combination has shown state-of-the-art performance in many real-world network mining tasks. Current approaches to graph processing in deep learning face two main problems. On the one hand, easy-to-use deep learning libraries lack support for widely used graph-processing algorithms and do not provide low-level APIs for building distributed graph-processing algorithms. On the other hand, existing graph-processing libraries are not user-friendly for deep learning researchers. Their graph primitives are not designed for batch processing, which is essential for deep learning use cases. In this paper, we present an efficient and easy-to-use graph engine that incorporates distributed graph processing into deep learning ecosystems. We develop a distributed graph storage system with an efficient batching technique to minimize communication overhead incurred by Remote Procedure Calls (RPC) between computing nodes. We propose an optimized method for distributed computation of Single Source Personalized PageRank (SSPPR) using the Forward Push algorithm based on lock-free parallel maps. Experimental evaluations demonstrate significant improvement, with up to three orders of magnitude in SSPPR throughput, of our graph engine compared with the tensor-based implementation. Both methods offer necessary usability with tensor operations, which are widely used for graph processing in current deep graph libraries. © 2023 Owner/Author.

关键词： Engines

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SmartPipe: Intelligently Freezing Layers in Pipeline parallelism for distributed DNN Training 29

SmartPipe: Intelligently Freezing Layers in Pipeline Paralle...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Niknami, Nadia Sawwan, Abdalaziz Wu, Jie Temple University Center for Networked Computing United States

ISBN: (纸本)9798350330717

Deep Neural Network (DNN) models have been widely utilized in various applications. However, the growing complexity of DNNs has led to increased challenges and prolonged training durations. Despite the availability of high-performance computing systems, certain DNNs still require several days for successful training. This study aims to address this issue by proposing a method for significantly reducing the training time of deep learning models while maintaining test accuracy. Existing approaches primarily concentrate on optimizing training efficiency through computational and communication overlap/scheduling. In contrast, this research takes a step further by inspiring transfer learning. Transfer learning is a useful way to quickly retrain a model on new data without having to retrain the entire network. During transfer learning, the first layers of the network are frozen while leaving the end layers open to modification. By doing so, computation and communication requirements in these frozen layers are eliminated. This intelligent approach involves freezing some of the specific DNN layers and allocating resources to the remaining active layers during the training process, thereby minimizing DNN training time. To achieve this objective, we propose an intelligently freezing DNN using pipeline parallelism. Through trace-based simulation results, our scheme has demonstrated its effectiveness in efficiently reducing the time cost of a training iteration. © 2023 IEEE.

关键词： DNN training gradient descent intelligent freezing machine learning neural networks pipeline

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A Novel Framework for distributed and Collaborative Federated Learning based on Blockchain and Smart Contracts 3

A Novel Framework for Distributed and Collaborative Federate...

引用

3rd IEEE international conference on Digital Twins and parallel Intelligence, DTPI 2023

作者： Li, Ziye Zhu, Haolin Zhong, Dingzhi Li, Cheng Wang, Bowen Yuan, Yong Renmin University of China School of Mathematics Beijing China

ISBN: (纸本)9798350318470

Blockchain and federated learning, as two key technologies for trusted and privacy-preserving collaboration in distributed environments, have been intensively studied in recent years. Federated learning aims to train a centralized global model from decentralized datasets without leaking user privacy, while blockchain helps establish mutual trusts among multiple clients with technical features such as tamper-proof, anonymity, security and traceability, among others. Specially, blockchain-based smart contracts can perform complex logics and behaviors in an efficient and accurate fashion to schedule collaborations. Therefore, leveraging these advantages, this paper proposed a novel framework for enabling distributed and collaborative federated learning based on blockchain and smart contracts, and discussed its major components, i.e., the blockchain-based distributed architecture, the smart-contract-based scheduling, as well as the incentive mechanism design for federated learning. We also discussed the potential application scenarios of our framework, which can be expected to help establish safer, fairer, smarter, and more efficient collaborations for distributed federated learning. © 2023 IEEE.

关键词： Smart contract

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Efficient Large Models Fine-tuning on Commodity Servers via Memory-balanced Pipeline parallelism 25

Efficient Large Models Fine-tuning on Commodity Servers via ...

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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, Yujie Lai, Zhiquan Liu, Weijie Wang, Wei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350330014

Large models have achieved impressive performance in many downstream tasks. Using pipeline parallelism to fine-tune large models on commodity GPU servers is an important way to make the excellent performance of large models available to the general public. Previous solutions fail to achieve an efficient memory-balanced pipeline parallelism. In this poster, we introduce a memory load-balanced pipeline parallel solution. This solution balances memory consumption across stages on commodity GPU servers via NVLink bridges. It establishes a new pathway to offload data from GPU to CPU by using the PCIe link of adjacent GPUs connected by the NVLink bridge. Furthermore, our method orchestrates offload operations to minimize the offload latency during large model fine-tuning. Experiments demonstrate that our solution can balance the memory footprint among pipeline stages without sacrificing training performance. © 2023 IEEE.

关键词： Program processors

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Quantum distributed Algorithms for Approximate Steiner Trees and Directed Minimum Spanning Trees 4

Quantum Distributed Algorithms for Approximate Steiner Trees...

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4th IEEE international conference on Quantum computing and Engineering, QCE 2023

作者： Kerger, Phillip A. Bernal Neira, David E. Izquierdo, Zoe Gonzalez Rieffel, Eleanor G. Johns Hopkins University Department of Applied Mathematics and Statistics United States Research Institute of Advanced Computer Science Usra United States Nasa Ames Research Center Quantum Artificial Intelligence Laboratory United States

ISBN: (纸本)9798350343236

We present two algorithms in the Quantum CONGEST-CLIQUE model of distributed computation that succeed with high probability;one for producing an approximately optimal Steiner Tree, and one for producing an exact directed minimum spanning tree, each of which uses O(n1/4) rounds of communication and Õ(n9/4) messages, achieving a lower asymptotic round and message complexity than any known algorithms in the classical CONGEST-CLIQUE model. At a high level, we achieve these results by combining classical algorithmic frameworks with quantum subroutines. Additionally, we characterize the constants and logarithmic factors involved in our algorithms as well as related classical algorithms, otherwise obscured by O notation, revealing that advances are needed to render both the quantum and classical algorithms practical. © 2023 IEEE.

关键词： Quantum computers

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Visual and cognitive computing for human-machine interaction

Visual and cognitive computing for human-machine interaction

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

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