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distributed Training of Neural Radiance Fields: A Performance Characterization

Distributed Training of Neural Radiance Fields: A Performanc...

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2024 IEEE international symposium on Performance Analysis of systems and Software, ISPASS 2024

作者： Zhao, Adrian Zhang, Louis Durvasula, Sankeerth Chen, Fan Jain, Nilesh Panneer, Selvakumar Vijaykumar, Nandita University of Toronto Canada Intel Labs United States Vector Institute Canada

ISBN: (纸本)9798350376388

Implicit neural representation is an emerging method that leverages deep neural networks and learned parameters to represent 3D scenes efficiently and accurately. Neural radiance field (NeRF) is a state-of-art implicit representation that achieves photorealistic 3D reconstruction with compact neural network models. However, as the complexity and scale of the scene increase, training NeRF models with a single GPU proves insufficient for achieving fast training and high-quality reconstruction. To address this challenge, prior works proposed distributed NeRF training methods. This is the first work to conduct a detailed evaluation of two major distributed NeRF training methods and their tradeoffs: distributed data parallel (DDP) and spatial segmentation (SS). We find that DDP training requires cross-device synchronization during training, while SS training incurs additional fusion overhead during inference. Our analysis also reveals that sampling input images is a common key bottleneck in distributed NeRF training. At the beginning of each training iteration, the CPU generates input batches for all GPUs in the cluster by sampling all images in the dataset, causing significant stalls that constitute up to 43.3% of the total training time. To alleviate this bottleneck, we propose a pipelined input sampling strategy that precomputes input samples on the CPU concurrently with model training on the GPUs. Our evaluation demonstrates an average speedup in training time by 1.95×( up to 2.24×). © 2024 IEEE.

关键词： Deep neural networks

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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...

引用

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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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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Toward High-Performance Blockchain System by Blurring the Line between Ordering and Execution 24

Toward High-Performance Blockchain System by Blurring the Li...

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2024 international Conference for High Performance Computing, Networking, Storage and Analysis

作者： Ryu, Donghyeon Park, Chanik POSTECH Comp Sci & Engn Dept Pohang South Korea

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

The primary bottleneck of blockchain is shifting from consensus to execution due to recent advances in DAG-based consensus algorithms supporting over 100k TPS. Many blockchain systems segregate execution from ordering, missing the opportunity to harness potential parallelism in consensus-produced batches. In this paper, we propose a new deterministically orderable concurrency control algorithm, OptME, which improves the performance of execution phase by exploiting inherent parallelism among transactions. This algorithm analyzes transaction dependencies to extract parallelism, and determines the total order of transaction execution. OptME consists of three steps: (1) building a transaction dependency graph, (2) generating a parallel execution schedule, and (3) executing transactions based on the schedule. We employ several optimizations, including parallel dependency graph construction, early abort detection, and efficient reordering with an optimistic assumption. Our evaluation demonstrates that OptME achieves up to 350k TPS and outperforms a state-of-the-art concurrency control algorithm, even under high contention scenarios.

关键词： distributed databases Blockchains Smart contracts Concurrency control Scheduling algorithms

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CODC-pyParaQC: A design and implementation of parallel quality control for ocean observation big data 22

CODC-pyParaQC: A design and implementation of parallel quali...

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

作者： Yuan, Huifeng Li, Tianyan Jin, Zhong Cheng, Lijing Tan, Zhetao Zhang, Bin Wang, Yanjun Computer Internet Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Institute of Atmospheric Physics Beijing China Chinese Academy of Sciences Institute of Oceanography Qingdao China

ISBN: (纸本)9798331509712

High-quality ocean observation is essential for research and applications in ocean exploration and climate change. With moving into the era of big data in recent years, it becomes crucial to process these massive raw observations accurately and efficiently. This paper addressed issues encountered in processing ocean big data within traditional delayed-mode quality control systems, including substantial serial I/O workloads and frequent context switching. A parallel quality control scheme named CODC-pyParaQC was proposed by constructing computing process groups. It retains the advantages of the existed delayed-mode quality control system (e.g. CODC-QC) while improving the efficiency of the quality control procedure, solving the feasibility of a large-scale parallel computation of the quality control scheme and realizing the (near) real-time quality control of massive ocean observation profiles. The results showed that the efficiency of single-node quality control has been improved by about 10 times. Leveraging the computing power of supercomputers and employing multi process groups for cross-node parallel computation, we have developed a fast and efficient (near) real-time quality control procedure. This system processed approximately 22,548,733 temperature profiles from the world ocean database (1940-2023) in about 6.5 hours. Our new quality control scheme can ensure the computing capability necessary for establishing a high-quality ocean observation profile database. © 2024 IEEE.

关键词： Efficiency

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Fast distributed Polynomial Multiplication Algorithm for Lattice-based Cryptographic Decryption In Blockchain systems 22

Fast Distributed Polynomial Multiplication Algorithm for Lat...

引用

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

作者： Zhao, Hongjian Tao, Yunting Kong, Fanyu Zhang, Guoyan Zhang, Hanlin Yu, Jia Shandong University School of Software Jinan China Binzhou Polytechnic College of Information Engineering Binzhou China Shandong University Shandong Sansec Information Technology Co. Ltd School of Software Jinan China Shandong University School of Cyber Science and Technology Qingdao China Qingdao University College of Computer Science and Technology Qingdao China

ISBN: (纸本)9798331509712

Lattice-based Post-Quantum Cryptography (PQC) can effectively resist the quantum threat to blockchain's underlying cryptographic algorithms. Blockchain node decryption is one of the most commonly used cryptographic computations in blockchain systems, and polynomial multiplication, a time-consuming operation for decryption, is one of the factors limiting blockchain efficiency. This paper proposes a novel distributed computing algorithm for polynomial multiplication, applicable in blockchain decryption. By splitting polynomials into lower-degree terms and delegating tasks to distributed nodes, our approach reduces computation time. A novel verification strategy based on the Karatsuba algorithm ensures result accuracy. The experimental results demonstrate that our proposed scheme improves the execution efficiency of NTT and INTT operations by approximately 47.8% and 52.4%, and reduces Kyber decryption time by up to 23.5%. © 2024 IEEE.

关键词： Encryption algorithms

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Optimization Design Algorithm for Dual-Active Bridge Converters Using parallel Power Modules 13

Optimization Design Algorithm for Dual-Active Bridge Convert...

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13th international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Porras Fernandez, David A. Gomez Jimenez, Roderick A. Fantino, Roberto A. Balda, Juan C. Univ Arkansas Dept Elect Engn Fayetteville AR 72701 USA Univ Nacl Sur UNS Alfredo Desages DIEC UNS CONICET Inst Invest Ingn Elect IIIE Bahia Blanca Buenos Aires Argentina

ISBN: (数字)9781665466189

ISBN: (纸本)9781665466189

This work presents an optimization algorithm for the design of isolated Dual-Active Bridge (DAB) converters using parallel power modules, in which volume, power density and number of switching devices are selected to achieve an specific optimal point of operation for giving input/output voltages and power. This point of operation is selected based on the maximum switching frequency that can be used for possible maximum power and minimum volume to obtain the desired power density. The maximum power can be selected based on the number of switching devices that are needed for a giving output power and the volume is based on the design of the magnetic components, such as inductors and transformer. The proposed design algorithm selects the magnetic components and number or switching devices based on an optimal point of operation that maximizes efficiency and minimizes volume to obtain the best volume-to-losses relation for a giving switching frequency.

关键词： Power system measurements Density measurement Switching frequency Multichip modules Magnetic devices Bridge circuits Switches

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Integrating interactive performance analysis in Jupyter Notebooks for parallel programming education

Integrating interactive performance analysis in Jupyter Note...

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

作者： Oden, Lena Noelp, Klaus Brauner, Philipp Univ Hagen Comp Engn Hagen Germany Rhein Westfal TH Aachen Human Comp Interact Ctr Aachen Germany

ISBN: (纸本)9798350364613;9798350364606

Understanding the performance behavior of parallel applications is important in many ways, but doing so is not easy. Most open source analysis tools are written for the command line. We are building on these proven tools to provide an interactive performance analysis experience within Jupyter Notebooks when developing parallel code with MPI, OpenMP, or both. Our solution makes it possible to measure the execution time, perform profiling and tracing, and visualize the results within the notebooks. For ease of use, it provides both a graphical JupyterLab extension and a C++ API. The JupyterLab extension shows a dialog where the user can select the type of analysis and its parameters. Internally, this tool uses Score -P, Scalasca, and Cube to generate profiling and tracing data. This tight integration gives students easy access to profiling tools and helps them better understand concepts such as benchmarking, scalability and performance bottlenecks. In addition to the technical development, the article presents hands-on exercises from our well-established parallel programming course. We conclude with a qualitative and quantitative evaluation with 19 students, which shows a positive effect of the tools on the students' perceived competence.

关键词： Jupyter parallel programming performance analysis interactive programming high perfonnance computing

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parallel Integrity Authentication Data Structure Construction for Encrypted Range Queries 21

Parallel Integrity Authentication Data Structure Constructio...

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21st IEEE international symposium on parallel and distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud Computing, 16th IEEE international Conference on Social Computing and Networking and 13th international Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Zhaokang Pan, Jiahui Zhou, Lu Zhang, Zhonghui Ji, Caocong Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350329223

With the rapid growth of cloud computing, outsourcing databases to cloud servers is becoming increasingly popular. Query integrity authentication is an effective technique to obtain reliable query results from untrusted clouds. ServeDB (Wu et al., ICDE 2019) is a state-of-the-art system that provides integrity authentication for encrypted range queries. However, ServeDB is a serial system. The high construction cost of its authentication data structure SVETree is the main performance bottleneck that limits its scalability to large datasets. In this study, we overcome the scalability limitation by parallelizing the SVETree construction workflow using the MapReduce framework. We propose the link-free storage layout to store the tree-based SVETree structure in a distributed key-value storage. The parallel SVETree construction algorithm reduces the communication latency of the key-value storage with the batch put/get optimization. The algorithm balances the workload of different tree nodes with the record-centric parallelization optimization. Furthermore, it avoids triggering out-of-core shuffles with the multi-round in-memory shuffle technique. The experimental results show that the parallel algorithm running on 128 cores achieves a 52.7 × speedup over the original serial algorithm. The parallel algorithm exhibits near-linear data and machine scalability. © 2023 IEEE.

关键词： MapReduce

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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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