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检索条件"任意字段=5th International Symposium on Parallel and Distributed Computing"

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GPU-Accelerated Error-Bounded Compression Framework for Quantum Circuit Simulations 37

GPU-Accelerated Error-Bounded Compression Framework for Quan...

引用

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

作者： Shah, Milan Yu, Xiaodong Di, Sheng Lykov, Danylo Alexeev, Yuri Becchi, Michela Cappello, Franck North Carolina State Univ Raleigh NC 27695 USA Argonne Natl Lab Argonne IL 60439 USA Univ Chicago Chicago IL USA

ISBN: (纸本)9798350337662

Quantum circuit simulations enable researchers to develop quantum algorithms without the need for a physical quantum computer. Quantum computing simulators, however, all suffer from significant memory footprint requirements, which prevents large circuits from being simulated on classical supercomputers. In this paper, we explore different lossy compression strategies to substantially shrink quantum circuit tensors in the QTensor package (a state-of-the-art tensor network quantum circuit simulator) while ensuring the reconstructed data satisfy the user-needed fidelity. Our contribution is fourfold. (1) We propose a series of optimized pre- and post-processing steps to boost the compression ratio of tensors with a very limited performance overhead. (2) We characterize the impact of lossy decompressed data on quantum circuit simulation results, and leverage the analysis to ensure the fidelity of reconstructed data. (3) We propose a configurable compression framework for GPU based on cuSZ and cuSZx, two state-of-the-art GPU-accelerated lossy compressors, to address different use-cases: either prioritizing compression ratios or prioritizing compression speed. (4) We perform a comprehensive evaluation by running 9 state-of-the-art compressors on an NVIDIA A100 GPU based on QTensor-generated tensors of varying sizes. When prioritizing compression ratio, our results show that our strategies can increase the compression ratio nearly 10 times compared to using only cuSZ. When prioritizing throughput, we can perform compression at the comparable speed as cuSZx while achieving 3-4x higher compression ratios. Decompressed tensors can be used in QTensor circuit simulation to yield a final energy result within 1-5% of the true energy value.

关键词： compression quantum computing GPU

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P-RECS '22: 5th international Workshop on Practical Reproducible Evaluation of Computer Systems 22

P-RECS '22: 5th International Workshop on Practical Reproduc...

引用

31st international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Lofstead, Jay Maltzahn, Carlos Jimenez, Ivo Sandia Natl Labs Ctr Comp Res Albuquerque NM 87123 USA Univ Calif Santa Cruz Ctr Res Open Source Software Santa Cruz CA USA

ISBN: (纸本)9781450391993

Reproducibility in computer science is often seen as a difficult to impossible task requiring bespoke hardware and software that others simply cannot field. Because of this, alternative approaches to reproducibility for computer systems is required. this workshop offers experiences and innovations in practical approaches for reproducibility for computer systems. the workshop also serves as the home of the ACM Exploratory Interest Group on Reproducibility and a community gathering for those interested in reproducible computer science research.

关键词： reproducibility computer systems scientific software verification and validation

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Sequre: a high-performance framework for rapid development of secure bioinformatics pipelines 36

Sequre: a high-performance framework for rapid development o...

引用

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

作者： Smajlovic, Haris Shajii, Ariya Berger, Bonnie Cho, Hyunghoon Numanagic, Ibrahim Univ Victoria Victoria BC Canada MIT Cambridge MA 02139 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA

ISBN: (纸本)9781665497473

Genomic data leaks are irreversible. Leaked DNA cannot be changed, stays disclosed indefinitely, and affects the owner's family members as well. the recent large-scale genomic data collections [1], [2] render the traditional privacy protection mechanisms, like the Health Insurance Portability and Accountability Act (HIPAA), inadequate for protection against the novel security attacks [3]. On the other hand, data access restrictions hinder important clinical research that requires large datasets to operate [4]. these concerns can be naturally addressed by the employment of privacy-enhancing technologies, such as a secure multiparty computation (MPC) [5]–[10]. Secure MPC enables computation on data without disclosing the data itself by dividing the data and computation between multiple computing parties in a distributed manner to prevent individual computing parties from accessing raw data. MPC systems are being increasingly adopted in fields that operate on sensitive datasets [11]–[13], such as computational genomics and biomedical research [14]–[22].

关键词： distributed processing Data privacy Pipelines Employment distributed databases Genomics Insurance

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A Survey on Energy efficiency in Cloud computing Frameworks at Different Platforms 5

A Survey on Energy efficiency in Cloud Computing Frameworks ...

引用

5th IEEE international Conference on Advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Anul Solanki, Kamna Dhankar, Amita Uiet Maharishi Dayanand University Department Of Computer Science And Engineering Rohtak India Maharishi Dayanand University Department Of Computer Science And Applications Rohtak India

ISBN: (纸本)9798350330861

Cloud computing is a paradigm that offers a restricted set of virtualized resources that may be accessed as needed. It is crucial to allocate these resources on target machines in a manner that minimizes both inadequate utilization and excess utilization, as these conditions can result in substantial consumption of energy. the term 'nano data centers' refers to a network of widely dispersed computers that have the capability to both host and distribute material and solicitations in a peer-to-peer manner. the investigation of energy usage in nano servers has not been extensively explored, despite their increasing popularity. Several findings have been derived from our study, which highlight the aspects within the system design that enable nDCs to achieve lower energy consumption compared to their centralized counterparts. this paper presents a comprehensive definition of Cloud Frameworks, encompassing many platforms such as edge computing, green computing, parallel computing, distributed computing, and fog computing. In conclusion, this study highlights a number of obstacles and potential within the realm of cloud computing. It is our intention that this work will attract the interest of the academic community and serve as a catalyst for further investigation in this domain. © 2023 IEEE.

关键词： Fog computing

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P-RECS '22: 5th international Workshop on Practical Reproducible Evaluation of Computer Systems 31

P-RECS '22: 5th International Workshop on Practical Reproduc...

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31st international symposium on High-Performance parallel and distributed computing, HPDC 2022

作者： Liem, Radita Solorzano, Ana Veroneze Scully-Allison, Connor RWTH Aachen University Aachen Germany Northeastern University BostonMA United States University of Arizona TucsonAZ United States

ISBN: (纸本)9781450391993

Modern software engineering is getting increasingly complicated. Especially in the HPC field, we are dealing with cutting edge infrastructure and a novel problem with unprecedented scale. the ability to monitor and analyze the performance of such applications and infrastructure is imperative for the future of improvement, design, and maintenance. In the current era, the writing and maintenance of these applications have ceased to be the job solely of computer scientists and has grown to encompass a wide variety of experts in mathematics, science, and other engineering disciplines. the fact that many developers from these disciplines have not received a formal education in computer science and rely increasingly on the tools created by computer scientists to analyze and optimize their code shows that there's a need for a forum to work together. © 2022 Owner/Author.

关键词： Software engineering

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parallel Committees: High-Performance, Scalable, Secure and Fault-Tolerant Data Replication Using a Novel Sharding Technique 5

Parallel Committees: High-Performance, Scalable, Secure and ...

引用

5th international Conference on Blockchain computing and Applications, BCCA 2023

作者： Solat, Siamak Naït-Abdesselam, Farid Université de Paris Cité France ENGIE Laboratory for Computer Science & Artificial Intelligence France University of Missouri Kansas City United States

ISBN: (纸本)9798350339239

distributed replication systems that use consensus mechanisms to process clients' requests have major limitations and problems in scalability, throughput, and performance. Such problems are mainly due to the time and message complexity of the consensus algorithms used in such systems. Sharding has already been implemented in several blockchain-based replication systems and although it has shown remarkable potential to improve performance and scalability, yet current sharding techniques have several significant scalability and security issues. In this paper, we propose a novel sharding technique for distributed replication systems that use a consensus to process clients' requests. We modified the approach of how nodes are allocated between shards through the public key generation process. Our approach effectively reduces undesirable cross-shard transactions that are more complex and costly to process than intra-shard transactions. Also, there is no concept of Beacon chain-as a shared-ledger between shards-which imposes additional scalability limitations and security issues on the network. With our approach, a network using a classical distributed consensus algorithm can grow outstandingly in number of nodes and still remain permissionless, © 2023 IEEE.

关键词： Scalability

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P-RECS 2022 - Proceedings of the 5th international Workshop on Practical Reproducible Evaluation of Computer Systems, co-located with HPDC 2022

P-RECS 2022 - Proceedings of the 5th International Workshop ...

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5th international Workshop on Practical Reproducible Evaluation of Systems, P-RECS 2022, co-located with the 31st international symposium on High-Performance parallel and distributed computing, HPDC 2022

the proceedings contain 3 papers. the topics discussed include: expanding the scope of artifact evaluation at HPC conferences: experience of SC21;reproducible experiments for internet systems;and managing randomness t...

ISBN: (纸本)9781450393133

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distributed Cloud computing Architecture in Hyperspectral Remote Sensing Image Classification under Big Data 5

Distributed Cloud Computing Architecture in Hyperspectral Re...

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5th IEEE international Conference on Information Systems and Computer Aided Education, ICISCAE 2022

作者： Feng, Kai Wu, Yi Shaanxi Academy of Aerospace Technology Application Co. Ltd. Xi'an China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

By using the cloud computing platform that has achieved excellent commercial results to perform parallel classification processing of massive remote sensing data, it can meet the requirements of improving the parallel processing efficiency of massive remote sensing data. the purpose of this paper is to study the application of distributed cloud computing architecture in hyperspectral remote sensing image classification based on big data. the related technologies of cloud computing are expounded, and the model of SCSRC, a sparse representation classification method based on spatial correlation constraints, is proposed. By analyzing the principle and solution method of SCSRC model, a distributed classification algorithm DP-SCSRC based on spatial correlation constraint sparse representation is proposed on the Spark platform, and the following optimization methods are designed for the core steps. Finally, through experiments with different computing nodes and different amounts of computing data, it is proved that the DP-SCSRC algorithm has a good distributed speedup ratio and data scalability under the premise of ensuring the classification accuracy. © 2022 IEEE.

关键词： distributed cloud

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parallel Bayesian Optimization for Optimal Scheduling of Underground Pumped Hydro-Energy Storage Systems 36

Parallel Bayesian Optimization for Optimal Scheduling of Und...

引用

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

作者： Gobert, Maxime Gmys, Jan Toubeau, Jean-Francois Melab, Nouredine Tuyttens, Daniel Vallee, Francois Univ Mons Math & Operat Res Mons Belgium Univ Lille CNRS CRIStAL INRIA Lille Nord Europe Lille France Univ Mons Power Syst & Markets Res Mons Belgium

ISBN: (纸本)9781665497473

Underground Pumped Hydro-Energy Storage stations are sustainable options to enhance storage capacity and thus the flexibility of energy systems. Efficient management of such units requires high-performance optimization algorithms able to find solutions in a very restricted timing to comply with the responsive energy markets. In this context, parallel computing offers a valuable solution to ensure appropriate decisions that maximize the profit of the station operator, while guaranteeing the safety of the energy network. this study investigates the use of three existing algorithms in parallel Bayesian Optimization, namely q-EGO, BSP-EGO and TuRBO. the three algorithms have different inherent behaviors in terms of parallel potential and, even though TuRBO scales better, q-EGO remains the best choice regarding the final outcomes for all investigated batch sizes and manages to get up to 5 times more profits than other approaches.

关键词： Bayesian Optimization Gaussian Process Batch-based parallelism Optimization Electrical Engineering

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A parallel DBSCAN algorithm based on KD-tree partitioning and a merging strategy 5

A parallel DBSCAN algorithm based on KD-tree partitioning an...

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5th international Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2023

作者： Zeng, Hongbin Qian, Xuezhong Song, Wei Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China

ISBN: (纸本)9798350359930

DBSCAN algorithm is a representative density-based clustering algorithm that has gained widespread application due to its ability to discover cluster of arbitrarily shapes and effectively handle noisy data. However, as the size of data increases, the performance of the classical single-machine sequential DBSCAN algorithm deteriorates significantly, failing to meet the efficiency requirements of real-world big data environments. To address this issue, this paper proposes a distributed parallel clustering algorithm - pDBSCAN. pDBSCAN uses Spark, a big data computing framework, to parallelize the algorithm. Furthermore, pDBSCAN leverages K-D Tree neighborhood queries to reduce distance calculations between points, and it divides the dataset into multiple balanced data partitions, thereby enhancing the algorithm's parallel performance. Additionally, to improve clustering effectiveness, an improved cluster merging strategy based on natural domain similarity is proposed for partial cluster merging. the performance and effectiveness of the pDBSCAN algorithm are evaluated in this paper based on real datasets, fully demonstrating the effectiveness and superiority of the proposed algorithm. © 2023 IEEE.

关键词： Clustering algorithms

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