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Estimating the Impact of Communication Schemes for distributed Graph Processing 21

Estimating the Impact of Communication Schemes for Distribut...

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21st IEEE international symposium on parallel and distributed computing (ISPDC)

作者： Ye, Tian Kuppannagari, Sanmukh R. De Rose, Cesar A. F. Wijeratne, Sasindu Kannan, Rajgopal Prasanna, Viktor K. Univ Southern Calif Dept Comp Sci Los Angeles CA 90007 USA Pontificia Univ Catolica Rio Grande do Sul Sch Technol Porto Alegre Brazil

ISBN: (数字)9781665488020

ISBN: (纸本)9781665488020

Extreme scale graph analytics is imperative for several real-world Big Data applications with the underlying graph structure containing millions or billions of vertices and edges. Since such huge graphs cannot fit into the memory of a single computer, distributed processing of the graph is required. Several frameworks have been developed for performing graph processing on distributed systems. The frameworks focus primarily on choosing the right computation model and the partitioning scheme under the assumption that such design choices will automatically reduce the communication overheads. For any computational model and partitioning scheme, communication schemes - the data to be communicated and the virtual interconnection network among the nodes - have significant impact on the performance. To analyze this impact, in this work, we identify widely used communication schemes and estimate their performance. Analyzing the trade-offs between the number of compute nodes and communication costs of various schemes on a distributed platform by brute force experimentation can be prohibitively expensive. Thus, our performance estimation models provide an economic way to perform the analyses given the partitions and the communication scheme as input. We validate our model on a local HPC cluster as well as the cloud hosted NSF Chameleon cluster. Using our estimates as well as the actual measurements, we compare the communication schemes and provide conditions under which one scheme should be preferred over the others.

关键词： distributed Graph Processing Performance Estimation Communication Schemes Cluster computing

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Efficient distributed Sequence parallelism for Transformer-based Image Segmentation

Efficient Distributed Sequence Parallelism for Transformer-b...

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IS and T international symposium on Electronic Imaging 2024: High Performance computing for Imaging 2024

作者： Lyngaas, Isaac Meena, Murali Gopalakrishnan Calabrese, Evan Wahib, Mohamed Chen, Peng Igarashi, Jun Huo, Yuankai Wang, Xiao National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Radiology Duke University DurhamNC United States Riken Center for Computational Science Tokyo Japan National Institute of Advanced Industrial Science and Technology Tokyo Japan Department of Computer Science Vanderbilt University NashvilleTN United States

We introduce an efficient distributed sequence parallel approach for training transformer-based deep learning image segmentation models. The neural network models are comprised of a combination of a Vision Transformer encoder with a convolutional decoder to provide image segmentation mappings. The utility of the distributed sequence parallel approach is especially useful in cases where the tokenized embedding representation of image data are too large to fit into standard computing hardware memory. To demonstrate the performance and characteristics of our models trained in sequence parallel fashion compared to standard models, we evaluate our approach using a 3D MRI brain tumor segmentation dataset. We show that training with a sequence parallel approach can match standard sequential model training in terms of convergence. Furthermore, we show that our sequence parallel approach has the capability to support training of models that would not be possible on standard computing resources. © 2024, Society for Imaging Science and Technology.

关键词： Image segmentation

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Deconstructing Alibaba Cloud's Preemptible Instance Pricing 23

Deconstructing Alibaba Cloud's Preemptible Instance Pricing

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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： Davidow, Danielle Movsowitz Ben-Yehuda, Orna Agmon Dunkelmen, Orr Tel Aviv Univ Dept Ind Engn Tel Aviv Israel Univ Haifa Caesarea Rothschild Inst Interdisciplinary Applic Haifa Israel Univ Haifa Dept Comp Sci Haifa Israel

ISBN: (纸本)9798400701559

Alibaba Cloud's preemptible instances are IaaS instances sold using an auction, similar to the spot instances offered by Amazon Web Services (AWS). Clients purchase them by bidding. The instances are terminated when supply is insufficient, or when the client's bid is lower than the "market price"- which Alibaba Cloud publishes ever so often. We collected Alibaba Cloud's preemptible price traces from November 2018 to July 2021. This work analyzes the traces, and shows evidence that they experienced sharp lateral behavioral changes over time. We characterize the traces before and after these events. Our analysis highlights features in the traces that seem to be artificially engineered by an underlying pricing mechanism. Since engineered features may abruptly change, they are particularly interesting to clients planning their bidding strategy, or scholars who study them (e.g., to design new pricing mechanisms on the basis of real-life data).

关键词： Cloud computing Auctions Alibaba Cloud Traces

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Efficient distributed Secure Memory with Migratable Merkle Tree 29

Efficient Distributed Secure Memory with Migratable Merkle T...

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29th IEEE international symposium on High-Performance Computer Architecture (HPCA)

作者： Feng, Erhu Du, Dong Xia, Yubin Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9781665476522

Hardware-assisted enclaves with memory encryption have been widely adopted in the prevailing architectures, e.g., Intel SGX/TDX, AMD SEV, ARM CCA, etc. However, existing enclave designs fall short in supporting efficient cooperation among cross-node enclaves (i.e., multi-machines) because the range of hardware memory protection is within a single node. A naive approach is to leverage cryptography at the application level and transfer data between nodes through secure channels (e.g., SSL). However, it incurs orders of magnitude costs due to expensive encryption/decryption, especially for distributed applications with large data transfer, e.g., MapReduce and graph computing. A secure and efficient mechanism of distributed secure memory is necessary but still missing. This paper proposes Migratable Merkle Tree (MMT), a design enabling efficient distributed secure memory to support distributed confidential computing. MMT sets up an integrity forest for distributed memory on multiple nodes. It allows an enclave to securely delegate an MMT closure, which contains both data and metadata of a subtree, to a remote enclave. By reusing the memory encryption mechanisms of existing enclaves, our design achieves secure data transfer without software re-encryption. We have implemented a prototype of MMT and a trusted firmware for management, and further applied MMT to real-world distributed applications. The evaluation results show that compared with existing systems using the AES-NI instruction, MMT can achieve up to 13x speedup on data transferring, and gain 12%similar to 58% improvement on the end-to-end performance of MapReduce and PageRank.

关键词： MapReduce

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Digital In-Memory computing to Accelerate Deep Learning Inference on the Edge

Digital In-Memory Computing to Accelerate Deep Learning Infe...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Perri, Stefania Zambelli, Cristian Ielmini, Daniele Silvano, Cristina Univ Calabria Arcavacata Di Rende Italy Univ Ferrara Ferrara Italy Politecn Milan Milan Italy

ISBN: (纸本)9798350364613;9798350364606

Deploying Deep Learning (DL) models on edge devices presents several challenges due to the limited set of processing and memory resources, and the bandwidth constraints while ensuring performance and energy requirements. In-memory computing (IMC) represents an efficient way to accelerate the inference of data-intensive DL tasks on the edge. Recently, several analog, digital, and mixed digital-analog memory technologies emerged as promising solutions for IMC. Among them, digital SRAM IMC exhibits a deterministic behavior and compatibility with advanced technology scaling rules making it a viable path for integration with hardware accelerators. This work focuses on discussing the potentially powerful aspects of digital IMC (DIMC) on edge System-on-Chip (SoC) devices. The limitations and ()pen challenges of DIMC are also discussed.

关键词： Deep Learnig Hardware accelerators In-memory computing System-on-chip devices

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Performance Comparison of Speculative Taskloop and OpenMP-for-Loop Thread-Level Speculation on Hardware Transactional Memory 21

Performance Comparison of Speculative Taskloop and OpenMP-fo...

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21st IEEE international symposium on parallel and distributed computing (ISPDC)

作者： Salamanca, Juan Sao Paulo State Univ Unesp DEMAC IGCE Sao Paulo Brazil

ISBN: (数字)9781665488020

ISBN: (纸本)9781665488020

Speculative Taskloop (STL) is a loop parallelization technique that takes the best of Task-based parallelism and Thread-Level Speculation to speed up loops with may loop-carried dependencies that were previously difficult for compilers to parallelize. Previous studies show the efficiency of STL when implemented using Hardware Transactional Memory and the advantages it offers compared to a typical DOACROSS technique such as OpenMP ordered. This paper presents a performance comparison between STL and a previously proposed technique that implements Thread-Level Speculation (TLS) in the for worksharing construct (FOR-TLS) over a set of loops from cbench and SPEC2006 benchmarks. The results show interesting insights on how each technique can be more appropriate depending on the characteristics of the evaluated loop. Experimental results reveal that by implementing both techniques on top of HTM, speed-ups of up to 2.41x can be obtained for STL and up to 2x for FOR-TLS.

关键词： OpenMP taskloop Thread-Level Speculation parallel for Hardware Transactional Memory

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Cuckoo Node Hashing on GPUs 21

Cuckoo Node Hashing on GPUs

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21st IEEE international symposium on parallel and distributed computing (ISPDC)

作者： Javed, Muhammad Zhou, Hao Troendle, David Jang, Byunghyun Univ Mississippi Dept Comp & Informat Sci University MS 38677 USA Penn State Univ Dept Comp Sci & Engn State Coll PA USA

ISBN: (数字)9781665488020

ISBN: (纸本)9781665488020

The hash table finds numerous applications in many different domains, but its potential for non-coalesced memory accesses and execution divergence characteristics impose optimization challenges on GPUs. We propose a novel hash table design, referred to as Cuckoo Node Hashing, which aims to better exploit the massive data parallelism offered by GPUs. At the core of its design, we leverage Cuckoo Hashing, one of known hash table design schemes, in a closed-address manner, which, to our knowledge, is the first attempt on GPUs. We also propose an architecture-aware warp-cooperative reordering algorithm that improves the memory performance and thread divergence of Cuckoo Node Hashing and efficiently increases the likelihood of coalesced memory accesses in hash table operations. Our experiments show that Cuckoo Node Hashing outperforms and scales better than existing state-of-the-art GPU hash table designs such as DACHash and Slab Hash with a peak performance of 5.03 billion queries/second in static searching and 434 billion insertions/second in static building.

关键词： Instruction sets Memory management Buildings Graphics processing units parallel processing Slabs Probes

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Optimizing the Resource and Job Management System of an Academic HPC & Research computing Facility 21

Optimizing the Resource and Job Management System of an Acad...

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21st IEEE international symposium on parallel and distributed computing (ISPDC)

作者： Varrette, Sebastien Kieffer, Emmanuel Pinel, Frederic Univ Luxembourg Luxembourg Fac Sci Technol & Med FSTM 2 Ave Univ L-4365 Esch Sur Alzette Luxembourg

ISBN: (数字)9781665488020

ISBN: (纸本)9781665488020

High Performance computing (HPC) is nowadays a strategic asset required to sustain the surging demands for massive processing and data-analytic capabilities. In practice, the effective management of such large scale and distributed computing infrastructures is left to a Resource and Job Management System (RJMS). This essential middleware component is responsible for managing the computing resources, handling user requests to allocate resources while providing an optimized framework for starting, executing and monitoring jobs on the allocated resources. The University of Luxembourg has been operating for 15 years a large academic HPC facility which relies since 2017 on the Slurm RJMS introduced on top of the flagship cluster Iris. The acquisition of a new liquid-cooled supercomputer named Aion which was released in 2021 was the occasion to deeply review and optimize the seminal Slurm configuration, the resource limits defined and the sustaining fairsharing algorithm. This paper presents the outcomes of this study and details the implemented RJMS policy. The impact of the decisions made over the supercomputers workloads is also described. In particular, the performance evaluation conducted highlights that when compared to the seminal configuration, the described and implemented environment brought concrete and measurable improvements with regards the platform utilization (+12.64%), the jobs efficiency (as measured by the average Wall-time Request Accuracy, improved by 110.81%) or the management and funding (increased by 10%). The systems demonstrated sustainable and scalable HPC performances, and this effort has led to a negligible penalty on the average slowdown metric (response time normalized by runtime), which was increased by 0.59% for job workloads covering a complete year of exercise. Overall, this new setup has been in production for 18 months on both supercomputers and the updated model proves to bring a fairer and more satisfying experience to the end users.

关键词： Slurm Fairsharing Workload analysis

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Redundancy-Free High-Performance Dynamic GNN Training with Hierarchical Pipeline parallelism 23

Redundancy-Free High-Performance Dynamic GNN Training with H...

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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： Xia, Yaqi Zhang, Zheng Wang, Hulin Yang, Donglin Zhou, Xiaobo Cheng, Dazhao Wuhan Univ Sch Comp Sci Wuhan Peoples R China Nvidia Corp Santa Clara CA USA Univ Macau IOTSC Macau Peoples R China

ISBN: (纸本)9798400701559

Temporal Graph Neural Networks (TGNNs) extend the success of Graph Neural Networks to dynamic graphs. distributed TGNN training requires efficiently tackling temporal dependency, which often leads to excessive cross-device communication that generates significant redundant data. However, existing systems are unable to remove the redundancy in data reuse and transfer, and suffer from severe communication overhead in a distributed setting. This paper presents Sven, an algorithm and system co-designed TGNN training library for the end-to-end performance optimization on multi-node multi-GPU systems. Exploiting dependency patterns of TGNN models and characteristics of dynamic graph datasets, we design redundancy-free data organization and load-balancing partitioning strategies that mitigate the redundant data communication and evenly partition dynamic graphs at the vertex level. Furthermore, we develop a hierarchical pipeline mechanism integrating data prefetching, micro-batch pipelining, and asynchronous pipelining to mitigate the communication overhead. As the first scaling study on the memory-based TGNNs training, experiments conducted on an HPC cluster of 64 GPUs show that Sven can achieve up to 1.7x-3.3x speedup over the state-of-art approaches and a factor of up to 5.26x communication efficiency improvement.

关键词： distributed training dynamic GNN pipeline parallelism

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Measuring the Impact of Gradient Accumulation on Cloud-based distributed Training 23

Measuring the Impact of Gradient Accumulation on Cloud-based...

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23rd IEEE/ACM international symposium on Cluster, Cloud and Internet computing (CCGrid)

作者： Huang, Zimeng Jiang, Bo Guo, Tian Liu, Yunzhuo Shanghai Jiao Tong Univ Shanghai Peoples R China Worcester Polytech Inst Worcester MA USA

ISBN: (纸本)9798350301199

Gradient accumulation (GA) is a commonly adopted technique for addressing the GPU memory shortage problem in model training. It reduces memory consumption at the cost of increased computation time. Although widely used, its benefits to model training have not been systematically studied. Our work evaluates and summarizes the benefits of GA, especially in cloud-based distributed training scenarios, where training cost is determined by both execution time and resource consumption. We focus on how GA can be utilized to balance execution time and resource consumption to achieve the lowest bills. Through empirical evaluations on AliCloud platforms, we observe that the total training cost can be reduced by 31.2% on average with a 17.3% increase in training time, when GA is introduced in the large-model and small-bandwidth scenarios with data-parallel training strategies. Besides, taking micro-batch size into optimization can further decrease training time and cost by 21.2% and 24.8% on average, respectively, for hybrid-parallel strategies in large-model and GPU training scenarios.

关键词： gradient accumulation distributed training cloud computing

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