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An Investigative Study of WebAssembly Performance in Cloud-to-Edge 1

An Investigative Study of WebAssembly Performance in Cloud-t...

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1st international symposium on parallel computing and distributed Systems, PCDS 2024

作者： Kakati, Sangeeta Brorsson, Mats University of Luxembourg SnT Luxembourg

ISBN: (纸本)9798350349658

Developing an application architecture that can fully utilize the resources available is essential for developers as cloud-to-edge computing keeps evolving. With its ability to operate on multiple architectures without interfering with the development process, WebAssembly has become a highly significant solution. This makes it easy for programmers to spread applications across various computer systems. To make the WebAssembly binaries work, a runtime is required. In this paper, the tests are run using the existing Wasmtime runtime and the SightGlass benchmark suite. We compare the cycle counts for the benchmarks' compilation, instantiation, and execution phases using performance counters during the evaluation stage. These tests have shown that WebAssembly is a highly effective tool that can help developers create applications that are both efficient and effective. Using WebAssembly, developers can create applications that can adapt to changing computing environments, making it possible to take full advantage of cloud-to-edge computing. Our experimental study investigated the performance of the wasmtime runtime on X86, ARM, and RISC-V architectures. Through the evaluation, we gained a valuable analysis of Wasm's performance characteristics. By examining architecture-specific outcomes, we demonstrated its capacity to fulfill the demands of a cross-architecture cloud-to-edge application framework. © 2024 IEEE.

关键词： Cloud computing architecture

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Massively parallel Ruling Set Made Deterministic 38

Massively Parallel Ruling Set Made Deterministic

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38th international symposium on distributed computing, DISC 2024

作者： Giliberti, Jeff Parsaeian, Zahra University of Maryland College ParkMD United States University of Freiburg Germany

ISBN: (纸本)9783959773522

We study the deterministic complexity of the 2-Ruling Set problem in the model of Massively parallel Computation (MPC) with linear and strongly sublinear local memory. Linear MPC: We present a constant-round deterministic algorithm for the 2-Ruling Set problem that matches the randomized round complexity recently settled by Cambus, Kuhn, Pai, and Uitto [DISC’23], and improves upon the deterministic O(log log n)-round algorithm by Pai and Pemmaraju [PODC’22]. Our main ingredient is a simpler analysis of CKPU’s algorithm based solely on bounded independence, which makes its efficient derandomization possible. Sublinear MPC: We present a deterministic algorithm that computes a 2-Ruling Set in Õ(√log n) rounds deterministically. Notably, this is the first deterministic ruling set algorithm with sublogarithmic round complexity, improving on the O(log ∆ + log log∗ n)-round complexity that stems from the deterministic MIS algorithm of Czumaj, Davies, and Parter [TALG’21]. Our result is based on a simple and fast randomness-efficient construction that achieves the same sparsification as that of the randomized Õ(√log n)-round LOCAL algorithm by Kothapalli and Pemmaraju [FSTTCS’12]. © Jeff Giliberti and Zahra Parsaeian.

关键词： Consensus algorithm

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Efficient Differentially Private Tensor Factorization in the parallel and distributed computing Paradigm 21

Efficient Differentially Private Tensor Factorization in the...

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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： Zhang, Feng Wang, Hao Guo, Ruixin Xue, Erkang Qu, Guangzhi China University of Geosciences School of Computer Science Wuhan China Oakland University Department of Computer Science and Engineering Rochester United States

ISBN: (纸本)9798350329223

Tensor factorization plays a fundamental role in multiple areas of AI research. Nevertheless, it encounters significant challenges related to privacy breaches and operational efficiency. In this study, we propose a novel approach that addresses both of these issues simultaneously by integrating differential privacy with parallel and distributed computing. To accommodate diverse scenarios, we introduce two models: DPTF-SVRG and ADMM-DPTF, each leveraging specific techniques. DPTF-SVRG is designed for single-GPU environments and utilizes a unique strategy to reduce gradient variance, enabling faster convergence compared to SGD. Moreover, it achieves parallelism on the GPU through a lock-free asynchronous approach. On the other hand, ADMM-DPTF utilizes distributed ADMM to parallelize DPTF-SVRG, enabling multi-GPU parallelism. Experimental results demonstrate that our algorithms outperform existing benchmarks while maintaining differential privacy. © 2023 IEEE.

关键词： Tensors

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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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DEAN: A Lightweight and Resource-efficient Blockchain Protocol for Reliable Edge computing 36

DEAN: A Lightweight and Resource-efficient Blockchain Protoc...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Al-Mamun, Ahdullah Shen, Haoting Zhao, Dongfang Univ Nevada Reno NV 89557 USA

ISBN: (纸本)9781665481069

Edge computing draws a lot of recent research interests because of the performance improvement by offloading many workloads from the remote data center to nearby edge nodes. Nonetheless, one open challenge of this emerging paradigm lies in the potential security issues on edge nodes. This paper proposes a cooperative protocol, namely DEAN, equipped with a unique resource-efficient quorum building mechanism to adopt blockchain seamlessly in an edge computing infrastructure to prevent data manipulation and allow fair data sharing with quick recovery under resource constraints of limited storage, computing, and network capacity. Specifically, DEAN leverages a parallel mechanism equipped with three independent core components, effectively achieving low resource consumption while allowing secured parallel block processing on edge nodes. We have implemented a system prototype based on DEAN and experimentally verified its effectiveness with a comparison with four popular blockchain implementations: Ethereum, Parity, IOTA, and Hyperledger Fabric. Experimental results show that the system prototype exhibits high resilience to arbitrary failures. Performance-wise, DEAN-based blockchain implementation outperforms the state-of-the-art blockchain systems with up to 88.6x higher throughput and 26x lower latency.

关键词： Edge computing Blockchains distributed computing Consensus protocols

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GEOtiled: A ScalableWorkflow for Generating Large Datasets of High-Resolution Terrain Parameters 23

GEOtiled: A ScalableWorkflow for Generating Large Datasets o...

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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： Roa, Camila Olaya, Paula Llamas, Ricardo Vargas, Rodrigo Taufer, Michela Univ Tennessee Knoxville TN 37916 USA Univ Delaware Newark DE USA

ISBN: (纸本)9798400701559

Terrain parameters such as slope, aspect, and hillshading are essential in various applications, including agriculture, forestry, and hydrology. However, generating high-resolution terrain parameters is computationally intensive, making it challenging to provide these value-added products to communities in need. We present a scalable workflow called GEOtiled that leverages data partitioning to accelerate the computation of terrain parameters from digital elevation models, while preserving accuracy. We assess our workflow in terms of its accuracy and wall time by comparing it to SAGA, which is highly accurate but slow to generate results, and to GDAL, which supports memory optimizations but not data parallelism. We obtain a coefficient of determination (R-2) between GEOtiled and SAGA of 0.794, ensuring accuracy in our terrain parameters. We achieve an X6 speedup compared to GDAL when generating the terrain parameters at a high-resolution (10 m) for the Contiguous United States (CONUS).

关键词： High Throughput computing Data Partitioning Cloud computing Soil moisture

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Accelerating MPI Collectives with Process-in-Process-based Multi-object Techniques 23

Accelerating MPI Collectives with Process-in-Process-based M...

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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： Huang, Jiajun Ouyang, Kaiming Zhai, Yujia Liu, Jinyang Si, Min Raffenetti, Ken Zhou, Hui Hori, Atsushi Chen, Zizhong Guo, Yanfei Thakur, Rajeev Univ Calif Riverside Riverside CA 92521 USA NVIDIA Corp Santa Clara CA USA Meta Platforms Inc Menlo Pk CA USA Argonne Natl Lab Argonne IL USA Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798400701559

In the exascale computing era, optimizing MPI collective performance in high-performance computing (HPC) applications is critical. Current algorithms face performance degradation due to system call overhead, page faults, or data-copy latency, affecting HPC applications' efficiency and scalability. To address these issues, we propose PiP-MColl, a Process-in-Process-based Multi-object Interprocess MPI Collective design that maximizes small message MPI collective performance at scale. PiP-MColl features efficient multiple sender and receiver collective algorithms and leverages Process-in-Process shared memory techniques to eliminate unnecessary system call, page fault overhead, and extra data copy, improving intra- and inter-node message rate and throughput. Our design also boosts performance for larger messages, resulting in comprehensive improvement for various message sizes. Experimental results show that PiP-MColl outperforms popular MPI libraries, including OpenMPI, MVAPICH2, and Intel MPI, by up to 4.6X for MPI collectives like MPI_Scatter and MPI_Allgather.

关键词： mpi collective process-in-process distributed systems message passing interface parallel algorithms

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Interactive Data Visualization to Optimize Decision-Making Process 1

Interactive Data Visualization to Optimize Decision-Making P...

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1st international symposium on parallel computing and distributed Systems, PCDS 2024

作者： Phang, Zheng-Bin Haw, Su-Cheng Tai, Tong-Ern Ng, Kok-Why Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia

ISBN: (纸本)9798350349658

The increasing volume and complexity of data in today's business environment present significant challenges for traditional data analysis methods. Static reports and spreadsheets often fall short in providing comprehensive insights necessary for strategic decision-making. This paper addresses the critical issue of enhancing data comprehension and utilization through interactive data visualization. The primary objectives of this paper are to explore various visualization techniques, design and implement an interactive dashboard with a content-based recommender system, and evaluate its effectiveness in optimizing business processes. The content-based recommender system utilizes TF-IDF vectorization and cosine similarity to offer personalized product recommendations. The process includes data cleaning, preprocessing, and integrating the recommender system into the dashboard. Comprehensive evaluation through user testing and performance analysis ensures the vizualisations and recommender system's accuracy, responsiveness, and effectiveness. In addition, by leveraging descriptive analytics, the paper aims to create a user-friendly tool to explore data, uncover hidden patterns, and facilitate decision-making. The expected outcome of this paper is an interactive dashboard that significantly enhances data comprehension and strategic decision-making for businesses. © 2024 IEEE.

关键词： chart dashboard data visualization decision-making interactive recommender system

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parallel Inference of Phylogenetic Stands with Gentrius

Parallel Inference of Phylogenetic Stands with Gentrius

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37th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Togkousidis, Anastasis Chernomor, Olga Stamatakis, Alexandros Heidelberg Inst Theoret Studies Computat Mol Evolut Grp Heidelberg Germany Karlsruhe Inst Technol Inst Theoret Informat Karlsruhe Germany Univ Vienna Ctr Integrat Bioinformat Vienna CIBIV Max Perutz Labs Vienna Austria Med Univ Vienna Vienna Bio Ctr VBC Vienna Austria Fdn Res & Technol Hellas Inst Comp Sci Biodivers Comp Grp Iraklion Greece

ISBN: (纸本)9798350311990

Multi-locus datasets are frequently used to infer phylogenies instead of using single locus. Missing data constitute a common challenge in such datasets as they can lead to stands, that is, sets of trees that are compatible with the incomplete per-locus trees. Under many common criteria the trees from one stand have identical score. Hence, identifying stands and determining their sizes is of crucial importance for a robust phylogenetic analysis. Recently, Chernomor et al. published Gentrius, a branch-and-bound algorithm that enumerates all stand trees given a set of unrooted incomplete locus trees. Despite its efficiency, the pattern and proportion of missing data in multi-locus datasets can still induce extremely long execution times. Here, we introduce the parallel version of the Gentrius algorithm. Our parallelization deploys a thread-pooling mechanism that maintains threads that finish early in busy-wait mode, such that they can contribute to solving long-running tasks. Thereby, we substantially reduce load imbalance and attain high parallel efficiency. Our performance assessment up to 16 cores yields linear parallel speedups on both, simulated, and empirical data. The parallel version of Gentrius is available as open source code under GNU GPL at https://***/togkousa/iqtree2/tree/terragen. All data we used for our analyses, are available for download at https://***/exelixis/material/***.

关键词： phylogenetic stands phylogenetic terraces multi-locus data parallel computing shared memory parallelism

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A Hybrid Memory Data Placement Strategy for Edge computing 22

A Hybrid Memory Data Placement Strategy for Edge Computing

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

作者： Lin, Binghui Zhang, Jianxun Qiao, Xinyu Tianjin University of Technology and Education College of Information Technology Engineering Tianjin China

ISBN: (纸本)9798331509712

With the booming development of fields such as cloud computing, big data, and artificial intelligence, the productivity of data have experienced explosive growth, prompting the expansion of edge computing. However, as data-intensive applications continue to increase, memory systems based solely on Dynamic Random Access Memory (DRAM) are no longer able to meet the demands of edge computing for high memory footprints, low access latency, and low energy consumption. In this paper, we organize DRAM as a cache for Non-Volatile Memory (NVM) and propose a utility-based hybrid memory data placement (UDP-HM) strategy, to address energy efficiency issues. UDP-HM places data in a hybrid memory system by calculating page utility. A series of simulation experiments are conducted to verify and evaluate the proposed hybrid memory data placement strategy. Under different workload intensities, compared to UH-MEM and RBLA, UDP-HM shows an average increase of 8.71% and 9.21% respectively in DRAM cache read/write operations, an average increase of 10.18% and 13.94% respectively in latency, an average decrease of 35.24% and 37.18% respectively in energy consumption, and an average decrease of 26.48% and 31.68% respectively in energy-delay product. © 2024 IEEE.

关键词： data migration data placement edge computing hybrid memory non-volatile memory

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