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Elasticity in a Task-based Dataflow Runtime Through Inter-node GPU Work Stealing

Elasticity in a Task-based Dataflow Runtime Through Inter-no...

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IEEE/ACM international symposium on Cluster computing and the Grid (CCGRID)

作者： Joseph John Josh Milthorpe National Computational Infrastructure Canberra Australia School of Computing Australian National University Canberra Australia Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Most contemporary HPC programming models assume an inelastic runtime in which the resources allocated to an application remain fixed throughout its execution. Conversely, elastic runtimes can expand and shrink resources based on availability and/or dynamic application requirements. In this paper, we implement elasticity for PaRSEC, a task-based dataflow runtime, using inter-node GPU work stealing. In addition to supporting elasticity, we demonstrate that inter-node GPU work stealing can enhance the performance of imbalanced applications by up to 45%.

关键词： Cloud computing Runtime High performance computing Graphics processing units distributed databases Elasticity Programming parallel processing Dynamic scheduling Resource management

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Image Compression and Reconstruction Based on Quantum Network

Image Compression and Reconstruction Based on Quantum Networ...

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

作者： J. Xun Qin Liu Shan Huang Andi Chen W. Shengjun National Laboratory of Solid State Microstructure School of Physics and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing China Institute for Brain Sciences and Kuang Yaming Honors School Nanjing University Nanjing China Hefei National Laboratory University of Science and Technology of China Hefei China

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Quantum network is an emerging type of network structure that leverages the principles of quantum mechanics to transmit and process information. Compared with classical data reconstruction algorithms, quantum networks make image reconstruction more efficient and accurate. They can also process more complex image information using fewer bits and faster parallel computing capabilities. Therefore, this paper will discuss image reconstruction methods based on our quantum network and explore their potential applications in image processing. We will introduce the basic structure of the quantum network, the process of image compression and reconstruction, and the specific parameter training method. Through this study, we can achieve a classical image reconstruction accuracy of 97.57%. Our quantum network design will introduce novel ideas and methods for image reconstruction in the future.

关键词： Training distributed processing Image coding Accuracy Conferences Quantum mechanics Reconstruction algorithms

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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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Extending Sparse Tensor Accelerators to Support Multiple Compression Formats 35

Extending Sparse Tensor Accelerators to Support Multiple Com...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Qin, Eric Jeong, Geonhwa Won, William Kao, Sheng-Chun Kwon, Hyoukjun Srinivasan, Sudarshan Das, Dipankar Moon, Gordon E. Rajamanickam, Sivasankaran Krishna, Tushar Georgia Tech Atlanta GA 30332 USA Intel Labs Bangalore Karnataka India Korea Aerosp Univ Goyang South Korea Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA

ISBN: (纸本)9781665440660

Sparsity, which occurs in both scientific applications and Deep Learning (DL) models, has been a key target of optimization within recent ASIC accelerators due to the potential memory and compute savings. These applications use data stored in a variety of compression formats. We demonstrate that both the compactness of different compression formats and the compute efficiency of the algorithms enabled by them vary across tensor dimensions and amount of sparsity. Since DL and scientific workloads span across all sparsity regions, there can be numerous format combinations for optimising memory and compute efficiency. Unfortunately, many proposed accelerators operate on one or two fixed format combinations. This work proposes hardware extensions to accelerators for supporting numerous format combinations seamlessly and demonstrates similar to 4x speedup over performing format conversions in software.

关键词： Deep learning distributed processing Tensors Computational modeling Memory management Hardware Software

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E2C: A Visual Simulator to Reinforce Education of Heterogeneous computing Systems

E2C: A Visual Simulator to Reinforce Education of Heterogene...

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

作者： Ali Mokhtari Drake Rawls Tony Huynh Jeremiah Green Mohsen Amini Salehi High Performance Cloud Computing (HPCC) Laboratory School of Computing and Informatics University of Louisiana at Lafayette LA USA

Heterogeneity has been an indispensable aspect of distributed computing throughout the history of these systems. ln particular, with the increasing popularity of accelerator technologies (eg., GPUs and TPUs) and the emergence of domain-specific computing via ASlCs and FPGA, the matter of heterogeneity and understanding its ramifications on the system performance has become more critical than ever before. However, it is challenging to effectively educate students about the potential impacts of heterogeneity on: (a) the performance of distributed systems; and (b) the logic of resource allocation methods to efficiently utilize the resources. Making use of the real infrastructure (such as those offered by the public cloud providers) for benchmarking the performance of heterogeneous machines, for different applications, with respect to different obiectives, and under various workload intensities is cost- and time-prohibitive. Moreover, not all students (globally and nationally) have access or can afford such real infrastructure. To reinforce the quality of learning about various dimensions of heterogeneity, and to decrease the widening gap in education, we develop an open-source simulation tool, called E2C, that can help students researchers and practitioners to study any type of heterogeneous (or homogeneous) computing system and measure its performance under various system configurations. To make the learning curve shallow, E2C is equipped with an intuitive graphical user interface (GU1) that enables its users to easily examine system-level solutions (scheduling, load balancing, scalability, etc.) in a controlled environment within a short time and at no cost. In particular, E2C is a discrete event simulator that offers the following features: (i) simulating a heterogeneous computing system; (ii) implementing a newly developed scheduling method and plugging it into the system, (iii) measuring energy consumption and other output-related metrics; and (iv) powerful visual

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distributed Training of Embeddings using Graph Analytics 35

Distributed Training of Embeddings using Graph Analytics

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Gill, Gurbinder Dathathri, Roshan Maleki, Saeed Musuvathi, Madan Mytkowicz, Todd Saarikivi, Olli Katana Graph Inc Austin TX 78705 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665440660

Many applications today, such as natural language processing, network and code analysis, rely on semantically embedding objects into low-dimensional fixed-length vectors. Such embeddings naturally provide a way to perform useful downstream tasks, such as identifying relations among objects and predicting objects for a given context. Unfortunately, training accurate embeddings is usually computationally intensive and requires processing large amounts of data. This paper presents a distributed training framework for a class of applications that use Skip gram like models to generate embeddings. We call this class Any2Vec and it includes Word2Vec (Gensim), and Vertex2Vec (DeepWalk and Node2Vec) among others. We first formulate Any2Vec training algorithm as a graph application. We then adapt the state-of-the-art distributed graph analytics framework, D-Galois, to support dynamic graph generation and re-partitioning, and incorporate novel communication optimizations. We show that on a cluster of 32 48-core hosts our framework GraphAny2Vec matches the accuracy of the state-of-the-art shared-memory implementations of Word2Vec and Vertex2Vec, and gives geo-mean speedups of 12 x and 5x respectively. Furthermore, GraphAny2Vec is on average 2x faster than DMTK, the state-of-the-art distributed Word2Vec implementation, on 32 hosts while yielding much better accuracy.

关键词： Machine Learning Graph Analytics distributed computing

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Grouping Synchronous to Eliminate Stragglers with Edge computing in distributed Deep Learning 19

Grouping Synchronous to Eliminate Stragglers with Edge Compu...

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

作者： Gui, Zhiyi Yang, Xiang Yang, Hao Li, Wei Zhang, Lei Qi, Qi Wang, Jingyu Sun, Haifeng Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Huawei Beijing Peoples R China

ISBN: (纸本)9781665435741

With the development of artificial intelligence(AI) applications, a large number of data are generated from mobile or IoT devices at the edge of the network. Deep learning tasks are executed to obtain effective information in the user data. However, the edge nodes are heterogeneous and the network bandwidth is limited in this case, which will cause general distributed deep learning to be inefficient. In this paper, we propose Group Synchronous parallel (GSP), which uses a density-based algorithm to group edge nodes with similar training speeds together. In order to eliminate stragglers, group parameter servers are responsible for coordinating communication of nodes in the group with Stale Synchronous parallel and aggregating the gradients of these nodes. And a global parameter server is responsible for aggregating the gradients from the group parameter servers to update the global model. To save network bandwidth, we further propose Grouping Dynamic Sparsification (GDS). It adjusts the gradient sparsification rate of nodes dynamically based on GSP so as to differentiates the communication volume and makes the training speed of all nodes tend to be the same. We evaluate GSP and GDS's performance on LeNet-5, ResNet, VGG, and Seq2Seq with Attention. The experimental results show that GSP speedups the training by 45% similar to 120% with 16 nodes. GDS on top of GSP can make up for some test accuracy loss, up to 0.82% for LeNet-5.

关键词： distributed deep training gradient compression parameter server gradient sparsification

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distributed-memory multi-GPU block-sparse tensor contraction for electronic structure 35

Distributed-memory multi-GPU block-sparse tensor contraction...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Herault, Thomas Robert, Yves Bosilca, George Harrison, Robert J. Lewis, Cannada A. Valeev, Edward F. Dongarra, Jack J. Univ Tennessee ICL Knoxville TN 37996 USA ENS Lyon Lyon France SUNY Stony Brook IACS Stony Brook NY USA Sandia Ntl Lab Livermore CA USA Virignia Tech Dept Chem Blacksburg VA USA

ISBN: (纸本)9781665440660

Many domains of scientific simulation (chemistry, condensed matter physics, data science) increasingly eschew dense tensors for block-sparse tensors, sometimes with additional structure (recursive hierarchy, rank sparsity, etc.). distributed-memory parallel computation with block-sparse tensorial data is paramount to minimize the time-to-solution (e.g., to study dynamical problems or for real-time analysis) and to accommodate problems of realistic size that are too large to lit into the last/device memory of a single node equipped with accelerators. Unfortunately, computation with such irregular data structures is a poor match to the dominant imperative, bulk-synchronous parallel programming model. In this paper, we focus on the critical element of block-sparse tensor algebra, namely binary tensor contraction, and report on an efficient and scalable implementation using the task-focused PaRSEC runtime. High performance of the block-sparse tensor contraction on the Summit supercomputer is demonstrated for synthetic data as well as for real data involved in electronic structure simulations of unprecedented size.

关键词： electronic structure tensor contraction block-sparse matrix multiplication distributed memory multi-GPU nodes PaRSEC

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P-FLEE: An Efficient parallel Algorithm for Simulating Human Migration

P-FLEE: An Efficient Parallel Algorithm for Simulating Human...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Anastasiadis, Petros Gogolenko, Sergiy Papadopoulou, Nikela Lawenda, Marcin Arabnejad, Hamid Jahani, Alireza Mahmood, Imran Groen, Derek Natl Tech Univ Athens Comp Syst Lab Athens Greece High Performance Comp Ctr Stuttgart Stuttgart Germany Poznan Supercomp & Networking Ctr Poznan Poland Brunel Univ London Dept Comp Sci Uxbridge Middx England

ISBN: (纸本)9781665435772

With over 79 million people forcibly displaced, forced human migration becomes a common issue in the modern world and a serious challenge for the global community. The Flee is a validated agent-based social simulation framework for forecasting the population displacements in the armed conflict settings. In this paper, we present two schemes to parallelize Flee, analyze computational complexity of those schemes, and outline results for benchmarks of our parallel codes with the real-world and synthetic scenarios on four state-of-the-art systems including a new European pre-exascale system, Hawk. On all testbeds, we evidenced high scalability of our codes. It exceeds more than 16,384 cores in our largest benchmark with 100 million agents on Hawk. parallelization schemes discussed in this work, can be extrapolated to a wide range of ABSS applications with frequent agent movement and lesser impact of direct communications between agents.

关键词： migration forcibly displaced people agent-based social simulation (ABSS) parallel and distributed agent-based systems (PDABS) high-performance computing (HPC)

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AIO: Automating I/O Optimization Pipeline for Data-Intensive Applications in HPC

AIO: Automating I/O Optimization Pipeline for Data-Intensive...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Wanxin Wang Huijun Wu Lihua Yang Zhangyu Liu Bin Zheng School of Computer and Communication Engineering Changsha University of Science and Technology College of Computer Science and Technology National University of Defense Technology

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

High-performance computing (HPC) systems has entered the exascale era, but I/O performance has lagged behind due to storage hardware limitations, creating a "storage wall effect" that hinders HPC systems full potential. Modern HPC storage systems employ a layered storage architecture with local node storage, burst buffers, and global parallel file systems to improve I/O performance. However, application workloads vary, and fast storage layers may not always outperform global parallel file systems, while a static storage software stack configuration may not achieve optimal performance across different applications. Traditional I/O optimization is time-consuming, laborintensive, and prone to human error. To address this issue, this paper proposes AIO, a data-driven I/O optimization method. AIO automatically selects the optimal storage hierarchy and software stack based on the task's I/O patterns, and further enhances performance by identifying and applying the most efficient configuration settings. We evaluated AIO with eight applications in a cluster that features shared burst buffers, local burst buffers, and a global shared file system. The experiments demonstrate that AIO effectively optimizes I/O performance, achieving an overall speedup of 2.74.

关键词： distributed processing File systems High performance computing Pipelines Full stack Optimization methods Computer architecture Hardware

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