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IPDPS 2022 PhD Forum

IPDPS 2022 PhD Forum

引用

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

作者： Sanjukta Bhowmick Anne-Cecile Orgerie

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The IEEE Computer Society has created the Technical Consortium on High Performance computing (TCHPC) to advance and coordinate work in the field of high performance computing networking, storage, and analysis concepts, technologies and applications, and to expand the IEEE's role in this interdisciplinary and pervasive field. The Consortium has launched an Education and Outreach Initiative to coordinate activities, information, and best practices around HPC education/outreach across its member technical committees and the broader community. This includes the coordination of student activities across conferences, and the lead chair for this initiative is also chair the IPDPS 2022 PhD Forum.

关键词： Kernel Task analysis distributed processing Conferences Computational efficiency Spatiotemporal phenomena Schedules

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Proof of Clustering: an efficient and reliable blockchain-based clustering framework

Proof of Clustering: an efficient and reliable blockchain-ba...

引用

IEEE/ACM international symposium on Big Data computing (BDC)

作者： Yamina Khenfouci Yacine Challal Laboratoire des Méthodes de Conception de Systèmes (LMCS) Ecole Nationale Supérieure d’Informatique (ESI) Algiers Algeria College of Computing and IT University of Doha for Science and Technology Doha Qatar

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

We propose a novel blockchain-based collaborative clustering framework, designed to enhance the efficiency and reliability of data clustering by decentralizing the clustering process across multiple nodes within the blockchain network. The system incorporates a consensus mechanism called Proof of Clustering to achieve the accuracy and consistency of clustering. This results thereby in ensuring confidence and reliability of the final clusters and prevents any breaches or corruption. To improve the performance of clustering process in the decentralized blockchain environment, data parallelism is employed. Through extensive experimentation and performance evaluation, we demonstrate the effectiveness and efficiency of proof of clustering consensus in various scenarios. Comparative studies with traditional centralized and distributed clustering methods showcase the superiority of our proposed solution by in terms of computational speed and resource utilization. PoC improves the Silhouette Score by 5.05% and reduces the Davies-Bouldin Index by 22.93%, indicating better cluster cohesion and separation in the PoC framework.

关键词： Performance evaluation Data analysis Accuracy Collaboration Machine learning parallel processing Reliability engineering Consensus protocol Indexes Resource management

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parallel Accelerating Ultra-Long Read Alignment by Vertical Partitioning Data

Parallel Accelerating Ultra-Long Read Alignment by Vertical ...

引用

international symposium on parallel Architectures, Algorithms and Programming (PAAP)

作者： Deng Pan Cheng Zhong Danyang Chen Jinxiong Zhang Feng Yang School of Computer Electronics and Information Key Laboratory of Parallel Distributed Computing Technology in Guangxi Universities Guangxi University Nanning Guangxi China School of Computer Electronics and Information Key Laboratory of Parallel Distributed Computing Guangxi University Nanning Guangxi China

ISBN: (纸本)9781665452199

The alignment between sequencing reads and genome is a basic work in biological big data analysis. Each read of the third generation sequencing data is getting longer, and the data size is getting larger. To effectively solve the ultra-long read alignment problem with high requirements for computing and memory capacity, a strategy for vertical partitioning ultra-long reads on hybrid CPU/GPU cluster is proposed, and a heap data structure is used to filter the local aligned results in all computing nodes of the parallel cluster system according to the alignment score to reduce the data transmission size. The methods for early termination and parallel merging-splicing are used to accelerate splicing local aligned results. The local aligned results among all computing nodes are collected and extended to obtain the final alignment results. The experimental results on datasets of simulated and real ultra-long reads show that the proposed parallel alignment algorithm can obtain high alignment accuracy, sensitivity and base-level sensitivity as a whole, and accelerate completing alignment between ultra-long reads and reference genome.

关键词： Sequential analysis Sensitivity Splicing Merging Memory management Genomics Programming

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Scalable Multi-Versioning Ordered Key-Value Stores with Persistent Memory Support

Scalable Multi-Versioning Ordered Key-Value Stores with Pers...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Bogdan Nicolae Argonne National Laboratory USA

Ordered key-value stores (or sorted maps/dictionaries) are a fundamental building block in a large variety of both sequential and parallel/distributed algorithms. However, most state-of-art approaches are either based on ephemeral in-memory representations that are difficult to persist and/or not scalable enough under concurrent access (e.g., red-black trees, skip lists), and/or not lightweight enough (e.g. database engines). Furthermore, there is an increasing need to provide versioning support, which is needed in a variety of scenarios: introspection, provenance tracking, revisiting previous intermediate results. To address these challenges, we propose a new lightweight dictionary data structure that simultaneously provides support for multi-versioning, persistency and scalability under concurrent access. We demonstrate its effectiveness through a series of experiments, in which it outperforms several state-of-art approaches, both in terms of vertical and horizontal scalability.

关键词： Concurrent computing distributed processing Dictionaries Databases Scalability Data structures Planning

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FIRST: Exploiting the Multi-Dimensional Attributes of Functions for Power-Aware Serverless computing

FIRST: Exploiting the Multi-Dimensional Attributes of Functi...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Lu Zhang Chao Li Xinkai Wang Weiqi Feng Zheng Yu Quan Chen Jingwen Leng Minyi Guo Pu Yang Shang Yue Department of Computer Science and Engineering Shanghai Jiao Tong University Tencent

Emerging cloud-native development models raise new challenges for managing server performance and power at microsecond scale. Compared with traditional cloud workloads, serverless functions exhibit unprecedented heterogeneity, variability, and dynamicity. Designing cloud-native power management schemes for serverless functions requires significant engineering effort. Current solutions remain sub-optimal since their orchestration process is often one-sided, lacking a systematic view. A key obstacle to truly efficient function deployment is the fundamental wide abstraction gap between the upper-layer request scheduling and the low-level hardware *** this work, we show that the optimal operating point (OOP) for energy efficiency cannot be attained without synthesizing the multi-dimensional attributes of functions. We present FIRST, a novel mechanism that enables servers to better orchestrate serverless functions. The key feature of FIRST is that it leverages a lightweight Internal Representation and meta-Scheduling (IRS) layer for collecting the maximum potential revenue from the servers. Specifically, FIRST follows a pipeline-style workflow. Its frontend components aim to analyze functions from different angles and expose their key features to the system. Meanwhile, its backend components are able to make informed function assignment decisions to avoid OOP divergence. We further demonstrate the way to create extensions based on FIRST to enable versatile cloud-native power management. In total, our design constitutes a flexible management layer that supports power-aware function deployment. We show that FIRST could allow 94% functions to be processed under the OOP, which brings up to 24% energy efficiency improvements.

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Alya towards Exascale: Optimal OpenACC Performance of the Navier-Stokes Finite Element Assembly on GPUs

Alya towards Exascale: Optimal OpenACC Performance of the Na...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Herbert Owen Dominik Ernst Thomas Gruber Oriol Lemkuhl Guillaume Houzeaux Lucas Gasparino Gerhard Wellein Barcelona Supercomputing Center (BSC) Barcelona Spain National High Performance Computing Center NHR@FAU Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

This paper addresses the challenge of providing portable and highly efficient code structures for CPU and GPU architectures. We choose the assembly of the right-hand term in the incompressible flow module of the High-Performance Computational Mechanics code Alya, which is one of the two CFD codes in the Unified European Benchmark Suite. Starting from an efficient CPU-code and a related OpenACC-port for GPUs we successively investigate performance potentials arising from code specialization, algorithmic restructuring and low-level *** demonstrate that only the combination of these different dimensions of runtime optimization unveils the full performance potential on the GPU and CPU. Roofline-based performance modelling is applied in this process and we demonstrate the need to investigate new optimization strategies if a classical roofline limit such as memory bandwidth utilization is achieved, rather than stopping the process. The final unified OpenACC-based implementation boosts performance by more than 50x on an NVIDIA A100 GPU (achieving approximately 2.5 TF/s FP64) and a further factor of 5x for an Intel Icelake based CPU-node (achieving approximately 1.0 TF/s FP64).The insights gained in our manual approach lays ground implementing unified but still highly efficient code structures for related kernels in Alya and other applications. These can be realized by manual coding or automatic code generation frameworks.

关键词： Codes Scalability Graphics processing units Manuals Programming Energy efficiency Finite element analysis

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ST-GCN: A Spatiotemporal Graph Convolution Neural Network for EEG Motor Imagery Signal Decoding

ST-GCN: A Spatiotemporal Graph Convolution Neural Network fo...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Jingzhou Xu Jun Qi Junqing Zhang Yong Yue Tingting Zhang Jianjun Chen Department of Computing Xi’an Jiaotong-Liverpool University Suzhou China Department of Electrical Engineering and Electronics University of Liverpool Liverpool UK School of Future Technology Fujian Agriculture and Forestry University Fuzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Motor imagery (MI) is a mental process extensively used in the experimental paradigm for brain-computer interfaces (BCIs) across various basic science and clinical research studies. Despite its widespread use, accurately decoding intentions from MI poses significant challenges due to the complex nature of brain patterns and the limited sample sizes typically available for machine learning. This paper introduces a Spatiotemporal Graph Neural Network (ST-GCN) designed for MI classification. First, the spatial-temporal convolution layer is used to extract features from raw EEG data, where mixed depthwise convolution extracts temporal features, followed by spatial filtering convolution that decomposes the EEG signal. A graph convolution module employing the max relative aggregator is then utilized to explore the relationships between the spatially decomposed EEG components. In the final step, under the combined supervision of cross-entropy and our proposed channel selection loss, the ST-GCN achieves feature extraction that enhances interclass dispersion and intraclass compactness. We compare ST-GCN with several benchmark EEG decoding methods on two MI datasets: the BCI Competition III Dataset IVa and the BCI Competition IV Dataset 1. ST-GCN outperforms the deep learning benchmark methods by achieving an accuracy of 78.11% and 71.94%, respectively, in 10-fold cross-validation.

关键词： Convolution Benchmark testing Feature extraction Motors Electroencephalography Graph neural networks Decoding Spatiotemporal phenomena Data mining Biological neural networks

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Constructions of Binary MDS Array Codes with Optimal Cooperative Repair Bandwidth

Constructions of Binary MDS Array Codes with Optimal Coopera...

引用

IEEE international symposium on Information Theory

作者： Lei Li Xinchun Yu Chenhao Ying Liang Chen Yuanyuan Dong Yuan Luo Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Data and Information Tsinghua University Shenzhen International Graduate School Shenzhen China Alibaba Group Hangzhou China

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Erasure codes are widely implemented in distributed storage systems to provide high fault tolerance with small storage overhead. Maximum distance separable codes are an common choice as they achieve the optimal tradeoff between fault tolerance and storage overhead. In this paper, we focus on the repair of multiple erasures of binary MDS array codes. Specifically, we present constructions of binary MDS array codes with optimal cooperative repair bandwidth by stacking multiple Blaum-Roth code instances whose “evaluation points” are judiciously designed. The constructed array codes with length $n$ and dimension $k$ can achieve the optimal cooperative repair bandwidth for $2\leq h\leq n-k$ and $k+1\leq d\leq n-h$ where $h$ and $d$ are the numbers of failed nodes and helper nodes, respectively. As the codes are constructed on a special polynomial ring over binary field, computation operations involved in nodes repair and file reconstruction for these codes are only XORs and cyclic shifts. Moreover, due to the inherent parallel structure of the codes, both the encoding and decoding procedures can be finished in parallel, speeding up the computing process.

关键词： Fault tolerance Codes Stacking Fault tolerant systems Bandwidth Maintenance engineering Polynomials

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Hadar: Heterogeneity-Aware Optimization-Based Online Scheduling for Deep Learning Cluster

Hadar: Heterogeneity-Aware Optimization-Based Online Schedul...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Abeda Sultana Fei Xu Xu Yuan Li Chen Nian-Feng Tzeng School of Computing and Informatics University of Louisiana at Lafayette USA School of Computer Science and Technology East China Normal University China Department of Computer and Information Sciences University of Delaware USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

With the wide adoption of deep neural network (DNN) models for various applications, enterprises, and cloud providers have built deep learning clusters and increasingly deployed specialized accelerators, such as GPUs and TPUs, for DNN training jobs. To arbitrate cluster resources among multi-user jobs, existing schedulers fall short, either lacking fine-grained heterogeneity awareness or hardly generalizable to various scheduling policies. To fill this gap, we propose a novel design of a task-level heterogeneity-aware scheduler, Hadar, based on an online optimization framework that can express other scheduling algorithms. Hadar leverages the performance traits of DNN jobs on a heterogeneous cluster, characterizes the task-level performance heterogeneity in the optimization problem, and makes scheduling decisions across both spatial and temporal dimensions. The primal-dual framework is employed, with our design of a dual subroutine, to solve the optimization problem and guide the scheduling design. Extensive trace-driven simulations with representative DNN models have been conducted to demonstrate that Hadar improves the average job completion time (JCT) by 3× over an Apache YARN-based resource manager used in production. Moreover, Hadar outperforms Gavel [1], the state-of-the-art heterogeneity-aware scheduler, by 2.5× for the average JCT, shortens the queuing delay by 13%, and improves FTF (Finish-Time-Fairness) by 1.5%.

关键词： Deep learning Training Runtime Scheduling algorithms Prototypes Artificial neural networks Optimal scheduling

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Teaching Cloud computing: Motivations, Challenges and Tools 34

Teaching Cloud Computing: Motivations, Challenges and Tools

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Anglano, Cosimo Canonico, Massimo Guazzone, Marco Univ Piemonte Orientale Comp Sci Inst DiSIT Vercelli Italy

ISBN: (纸本)9781728174457

Teaching Cloud computing is becoming crucial since this recent computing paradigm is used in many fields and it is changing the way we use the applications and the technology. As a matter of the fact, most of the applications that we use everyday through the web are based on cloud services. Unfortunately, the difficulty to set up a real testbed for students and, at the same time, the lack of an easy, open and collaborative educational material freely available make teaching Cloud computing a hard task. In this paper we discuss the state of the art concerning teaching Cloud computing and we propose education materials and tools that make Cloud computing easy to use even for students/educators without any computer science skills.

关键词： Cloud computing Educational resources cloud tools

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