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Towards Fine-grained parallelism in parallel and distributed Python Libraries

Towards Fine-grained Parallelism in Parallel and Distributed...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Kerney, Jamison Raicu, Joan Raicu, John Chard, Kyle IIT Coll Comp Chicago IL 60616 USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA

ISBN: (纸本)9798350364613;9798350364606

There is a growing need, for example in machine learning and analytics, to decompose applications into smaller schedulable units. Such decomposition can improve performance, reduce energy consumption, and increase resource utilization. Unfortunately, enabling fine-grained parallelism comes with significant overheads and requires improvements at all layers of the programming stack. We consider the challenges of supporting fine-grained parallelism in the increasingly popular Python-based programming libraries. Specifically, we focus on Parsl, a Python library that is widely used to parallelize the execution of fine-grained Python functions. Parsl's Python-based runtime supports a maximum throughput of around 1200 tasks per second insufficient to meet modern application needs. We perform a comprehensive analysis of Parsl and identify areas that prohibit it from achieving higher throughput. We first profile Parsl components and identify that, with fine-grained tasks workers are often not saturated. We find that tasks spend a majority of their time in the components between the scheduler and worker, however, we also learned that the scheduler is capable of submitting thousands of tasks per second. We then focused on developing new optimizations and implementing crucial components in C to improve throughput. Our new implementation increases Parsl's throughput 6 fold.

关键词： Python

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A parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery

A Parallel Workflow for Polar Sea-Ice Classification using A...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Iqrah, Jurdana Masuma Wang, Wei Xie, Hongjie Prasad, Sushil K. Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA Univ Texas San Antonio Dept Earth & Planetary Sci San Antonio TX USA

ISBN: (纸本)9798350364613;9798350364606

The observation of the advancing and retreating pattern of polar sea ice cover stands as a vital indicator of global warming. This research aims to develop a robust, effective, and scalable system for classifying polar sea ice as thick/snow-covered, young/thin, or open water using Sentinel-2 (S2) images. Since the 52 satellite is actively capturing high-resolution imagery over the earth's surface, there are lots of images that need to be classified. One major obstacle is the absence of labeled 52 training data (images) to act as the ground truth. We demonstrate a scalable and accurate method for segmenting and automatically labeling S2 images using carefully determined color thresholds. We employ a parallel workflow using PySpark to scale and achieve 9-fold data loading and 16-fold map-reduce speedup on auto-labeling S2 images based on thin cloud and shadow filtered color-based segmentation to generate label data. The auto-labeled data generated from this process are then employed to train a U-Net machine learning model, resulting in good classification accuracy. As training the U-Net classification model is computationally heavy and time-consuming, we distribute the U-Net model training to scale it over 8 GPLJs using the Horovod framework over a DGX cluster with a 7.2 lx speedup without affecting the accuracy of the model. Using the Antarctic's Ross Sea region as an example, the U-Net model trained on autolabeled data achieves a classification accuracy of 98.97% for auto-labeled training datasets when the thin clouds and shadows from the S2 images are filtered out.

关键词： Polar Sea Ice Sentinel-2 Sea Ice Classification Auto-labeling parallel Processing distributed Deep Learning Synchronous Data parallel

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20th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2024

20th IFIP WG 10.3 International Conference on Network and Pa...

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20th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2024

ISBN: (纸本)9789819628636

The proceedings contain 76 papers. The special focus in this conference is on Network and parallel Computing. The topics include: AsymFB: Accelerating LLM Training Through Asymmetric Model parallelism;DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Friendly Data Communication and parallelism Strategies;Diagnosability of the Lexicographic Product of Paths and Complete Bipartite Graphs Under PMC Model;DTuner: A Construction-Based Optimization Method for Dynamic Tensor Operators Accelerating;Efficient Implementation of the LOBPCG Algorithm on a CPU-GPU Cluster;HP-CSF: An GPU Optimization Method for CP Decomposition of Incomplete Tensors;JediGAN: A Fully Decentralized Training of GAN with Adaptive Discriminator Averaging and Generator Selection;optimizing Vo-Viso: A Modified Methodology to parallel Computing with Isolating Data in Memristor Arrays;parallel Computation of the Combination of Two Point Operations in Conic Curves Cryptosystem over GF(2n) Using Tile Self-assembly;parallel Construction of Independent Spanning Trees on 3-ary n-cube Networks;SpecInF: Exploiting Idle GPU Resources in distributed DL Training via Speculative Inference Filling;swDarknet: A Heterogeneous parallel Deep Learning Framework Suitable for SW26010 Pro Processor;VConv: Autotiling Convolution Algorithm Based on MLIR for Multi-core Vector accelerators;ACH-Code: An Efficient Erasure Code to Reduce Average Repair Cost in Cloud Storage systems of Multiple Availability Zones;CMS: A Computility Resource Status Management and Storage Framework;fast Memory Disaggregation with SwiftSwap;HASLB: Huge Page Allocation Strategy Optimized for Load-Balance in parallel Computing Programs;lightFinder: Finding Persistent Items with Small Memory;miDedup: A Restore-Friendly Deduplication Method on Docker Image Storage systems;SPLR: A Selective Packet Loss Recovery for Improved RDMA Performance;a Cluster-Based Platoon Formation Scheme for Realistic Automated Vehicle Platooning;AnaNET: Anatomical Network fo

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20th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2024

20th IFIP WG 10.3 International Conference on Network and Pa...

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20th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2024

ISBN: (纸本)9789819628292

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An Auto-parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-parallel distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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27th Euromicro international conference on parallel, distributed and Network-Based Processing PDP 2019

27th Euromicro International Conference on Parallel, Distrib...

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Euromicro conference on parallel, distributed and Network-Based Processing

The following topics are dealt with: parallel processing; cloud computing; multiprocessing systems; learning (artificial intelligence); parallel programming; data analysis; resource allocation; graphics processing uni...

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A Mathematical Model and a Convergence Result for Totally Asynchronous Federated Learning

A Mathematical Model and a Convergence Result for Totally As...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： El-Baz, Didier Luo, Jia Mo, Hao Shi, Lei Univ Toulouse LAAS Toulouse France Chongqing Res Inst Chongqing Peoples R China Beijing Univ Technol Beijing Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Beijing Peoples R China

ISBN: (纸本)9798350364613;9798350364606

A totally asynchronous gradient algorithm, with fixed step size is proposedfor federated learning. A mathematical model is presented and a convergence result is established. The convergence result is based on the concept of macro iterations sequence. The interest of the contribution is to show that the asynchronous federated learning method converges when gradients of loss functions are updated by workers without order nor synchronization and with possible unbounded delays.

关键词： machine learning federated learning convex optimization gradient algorithms asynchronous iterative algorithms distributed computing

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PASCI : A Scalable Framework for Heterogeneous parallel Calculation of Dynamical Electron Correlation 24

PASCI : A Scalable Framework for Heterogeneous Parallel Calc...

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53rd international conference on parallel Processing (ICPP)

作者： Jin, Runfeng Liang, Wenhao Zhang, Haoyuan Song, Yinxuan Luo, Zhen Ma, Haibo Ma, Yingjin Jin, Zhong Chinese Acad Sci Univ Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Shandong Univ Sch Chem & Chem Engn Qingdao Inst Theoret & Computat Sci Qingdao Peoples R China Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China

ISBN: (纸本)9798400717932

Accurately calculating the electronic structure of strongly correlated chemical systems necessitates a detailed description of both static and dynamical electron correlations, posing a significant challenge in ab initio quantum chemistry. Although the high memory and computational demands generally limit these calculations to relatively modest systems, the advanced computational capabilities of modern GPUs provide new avenues to expand these limits. However, complex control flows inherent to computation notably impair performance on GPUs. Furthermore, the significant disparity in computational load across different branches leads to load imbalance, challenging the large-scale simulations. In this work, we introduce PASCI, a heterogeneous parallel computing framework designed to quickly and efficiently parallelize the computation of dynamical correlation energy based on determinants. The features of the PASCI framework include (1) a divergence-avoiding GPU algorithm, (2) a three-level load-mapping strategy to ensure load balance across processors, GPU warps, and GPU threads, (3) performance models for memory footprint and computation, and (4) seamless integration with existing quantum chemistry software. Experimental results using an NVIDIA A100 GPU demonstrate that our new GPU algorithm achieves an average 6.6x (up to 13.8x) peak performance increase and 2-4 orders of magnitude speedup in practical usage compared to its original GPU implementation. Moreover, PASCI exhibits excellent scalability, highlighting its potential as a powerful high-performance computing tool in complex quantum chemistry research.

关键词： Quantum Chemistry Computation Load Balancing distributed Heterogeneous systems GPU Warp Divergence

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MUSE: A Programmable Metadata Load Estimation Interface for Ceph File System 29

MUSE: A Programmable Metadata Load Estimation Interface for ...

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

作者： Shao, Xinyang Li, Cheng Xu, Yinlong University of Science and Technology of China Anhui Hefei China Ustc Anhui Province Key Laboratory of High Performance Computing Anhui Hefei China

ISBN: (纸本)9798350330717

CephFS represents a prominent distributed file system that utilizes directory fragment migration to achieve improved runtime balance. However, its imprecise imbalance model and subtree selection algorithms can result in suboptimal performance. Our prior work, Lunule, enhances CephFS by introducing an imbalance factor model and a workload-aware load estimation policy. Nevertheless, Lunule's built-in workload-aware planner still relies on a unified formula with adjustable coefficients, representing a one-size-fits-all approach. In this study, we introduce MUSE, a novel and user-friendly programmable interface that specifically focuses on subtree migration planning. MUSE effectively separates the complex and challenging task of evaluating subtree loads for different workloads, enabling designers to manipulate expected loads in the subsequent epoch. This facilitates the selection of appropriate subtrees for migration and opens up possibilities for implementing true isolated workloadaware policies. Through the utilization of two small Lua scripts, we demonstrate that MUSE achieves comparable load balancing effects and performance to CephFS and Lunule in workloads characterized by temporal and spatial locality. © 2023 IEEE.

关键词： distributed file system Load balance Metadata

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Deep Learning Model for Railroad Structural Health Monitoring via distributed Acoustic Sensing 26

Deep Learning Model for Railroad Structural Health Monitorin...

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26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing, SNPD-Winter 2023

作者： Rahman, Md Arifur Taheri, Hossein Kim, Jongyeop Georgia Southern University Manufacturing Engineering Department StatesboroGA30460 United States Georgia Southern University Information Technology Department StatesboroGA United States

ISBN: (纸本)9798350345865

Railway infrastructure plays a vital role in modern transportation systems, facilitating the efficient movement of people and goods. However, the integrity and performance of railroad structures are subject to various external forces and aging processes, which necessitate continuous monitoring to ensure safety and operational efficiency. This research focused on the structural health monitoring of the railroad using distributed Acoustic Sensing (DAS) data collected from a High Tonnage Loop (HTL). An investigation on applying a deep learning model, long-shot-term memory (LSTM), and gated recurrent Unit(GRU) is presented to identify and classify railroad conditions. © 2023 IEEE.

关键词： Railroads

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