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distributed Quantum computing with QMPI 21

Distributed Quantum Computing with QMPI

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international conference for High Performance computing, networking, Storage and Analysis (SC21)

作者： Haner, Thomas Steiger, Damian S. Hoefler, Torsten Troyer, Matthias Microsoft Quantum Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Microsoft Quantum New York NY USA

ISBN: (纸本)9781450384421

Practical applications of quantum computers require millions of physical qubits and it will be challenging for individual quantum processors to reach such qubit numbers. It is therefore timely to investigate the resource requirements of quantum algorithms in a distributed setting, where multiple quantum processors are interconnected by a coherent network. We introduce an extension of the Message Passing Interface (MPI) to enable high-performance implementations of distributed quantum algorithms. In turn, these implementations can be used for testing, debugging, and resource estimation. In addition to a prototype implementation of quantum MPI, we present a performance model for distributed quantum computing, SENDQ. The model is inspired by the classical LogP model, making it useful to inform algorithmic decisions when programming distributed quantum computers. Specifically, we consider several optimizations of two quantum algorithms for problems in physics and chemistry, and we detail their effects on performance in the SENDQ model.

关键词： distributed quantum computing QMPI

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Dynamic Taxi Ride-Sharing Through Adaptive Request Propagation Using Regional Taxi Demand and Supply 18th

Dynamic Taxi Ride-Sharing Through Adaptive Request Propagati...

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18th European-Alliance-for-Innovation (EAI) international conference on Mobile and Ubiquitous Systems: computing, networking and Services (MobiQuitous)

作者： Yu, Haoxiang Raychoudhury, Vaskar Saha, Snehanshu Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Miami Univ Dept CSE Oxford OH 45056 USA BITS Pilani Dept CS & IS Goa Campus Sancoale Goa India BITS Pilani APPCAIR Goa Campus Sancoale Goa India

ISBN: (纸本)9783030948221;9783030948214

Taxi ride-sharing is an emerging public transportation model that provides several benefits in terms of cost, environmental impact, and road congestion. It is further popularized through market available app-based systems, such as Uber, Lyft, Didi, etc. However, those systems are limited due to their centralized architecture, high cost sharing with the driver, and proprietary business model. distributed ride-sharing, on the other hand, involves only passengers and drivers and operates in a peer-to-peer manner. But, distributed ride-sharing systems often suffer due to Spatio-temporal constraints associated with taxi demand and supply as well as broadcast message storms. While we have observed dynamic and distributed ride-sharing systems which address the Spatio-temporal issues, there is hardly any effort to reduce their message overhead. In this paper, we present a hybrid model of ride-sharing where a central server adaptively calculates transmission range for passenger request propagation using Spatio-temporal information of ride-sharing success rate for the past 30-minute. Passengers use the adaptive transmission range to find the best shared-ride using a distributed manner. Our extensive empirical evaluation shows that our proposed approach increases the overall ride-sharing success rate and taxi utilization while significantly reducing the communication overhead, request processing time, and passenger waiting time.

关键词： Dynamic ride sharing Adaptive transmission range distributed algorithm Spatio-temporal constraints

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PanguLU: A Scalable Regular Two-Dimensional Block-Cyclic Sparse Direct Solver on distributed Heterogeneous Systems 23

PanguLU: A Scalable Regular Two-Dimensional Block-Cyclic Spa...

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2023 international conference for High Performance computing, networking, Storage and Analysis, SC 2023

作者： Fu, Xu Zhang, Bingbin Wang, Tengcheng Li, Wenhao Lu, Yuechen Yi, Enxin Zhao, Jianqi Geng, Xiaohan Li, Fangying Zhang, Jingwen Jin, Zhou Liu, Weifeng SSSLab China University of Petroleum-Beijing Beijing China

ISBN: (纸本)9798400701092

Sparse direct solvers play a vital role in large-scale high performance computing in science and engineering. Existing distributed sparse direct methods employ multifrontal/supernodal patterns to aggregate columns of nearly identical forms and to exploit dense basic linear algebra subprograms (BLAS) for computation. However, such a data layout may bring more unevenness when the structure of the input matrix is not ideal, and using dense BLAS may waste many floating-point operations on zero *** this paper, we propose a new sparse direct solver called PanguLU. Unlike the multifrontal/supernodal layout, our work relies on simpler regular 2D blocking and stores the blocks in their sparse forms to avoid any extra fill-ins. Based on the sparse patterns of the blocks, a variety of block-wise sparse BLAS methods are developed and selected for higher efficiency on local GPUs. To make PanguLU more scalable, we also adjust the mapping of blocks to processes for overall more balanced workload, and propose a synchronisation-free communication strategy considering the dependencies among different sub-tasks to reduce overall latency *** on two distributed heterogeneous platforms consisting of 128 NVIDIA A100 GPUs and 128 AMD MI50 GPUs demonstrate that PanguLU achieves up to 11.70x and 17.97x speedups over the latest SuperLU_DIST, and scales up to 47.51x and 74.84x on the 128 A100 and MI50 GPUs over a single GPU, respectively. © 2023 ACM.

关键词： Linear algebra

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Efficient Allocation of Image Recognition and LLM Tasks on Multi-GPU System 15th

Efficient Allocation of Image Recognition and LLM Tasks on...

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15th international conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Lawenda, Marcin Samborski, Krzesimir Khloponin, Kyrylo Szustak, Lukasz Poznan Supercomputing and Networking Center Jana Pawla II 10 Poznań61-139 Poland Czestochowa University of Technology Dąbrowskiego 69 Częstochowa42-201 Poland

ISBN: (纸本)9783031857027

This work is concerned with the evaluation of the performance of parallelization of learning and tuning processes for image classification and large language models. For machine learning model in image recognition, various parallelization methods are developed based on different hardware and software scenarios: simple data parallelism, distributed data parallelism, and distributed processing. A detailed description of presented strategies is given, highlighting the challenges and benefits of their application. Furthermore, the impact of different dataset types on the tuning process of large language models is investigated. Experiments show to what extent the task type affects the iteration time in a multi-GPU environment, offering valuable insights into the optimal data utilization strategies to improve model performance. Furthermore, this study leverages the built-in parallelization mechanisms of PyTorch that can facilitate these tasks. Furthermore, performance profiling is incorporated into the study to thoroughly evaluate the impact of memory and communication operations during the training/tuning procedure. Test scenarios are developed and tested with numerous benchmarks on the NVIDIA H100 architecture showing efficiency through selected metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Maxwell's Demon in Tail-tolerant, Resource-efficient Serverless computing 28

Maxwell's Demon in Tail-tolerant, Resource-efficient Serverl...

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IEEE 28th international conference on Parallel and distributed Systems (IEEE ICPADS)

作者： Zhang, Huanyu Huang, Wenhao Zhao, Laiping Li, Keqiu Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Tianjin Key Lab Adv Networking TANKLAB Tianjin 300350 Peoples R China

ISBN: (纸本)9781665473156

computing systems always face a "resource allocation dilemma" that shows the great difficulties in trading off resource efficiency for tail latency, due to the internal uncertainty of cluster status and execution behavior. Inspired by the imaginary "Maxwell's demon" in thermodynamics who can reduce the uncertainty through a per-gas molecule-level control policy, we consider the "one-to-one mapping" feature of serverless computing and build a novel resource allocator, named Maxwell, that can achieve low tail latency and high resource efficiency in serverless simultaneously. Like the "Maxwell's demon", Maxwell is able to optimize the resource allocation for every request. It observes the state of each request and makes decisions about the minimum resource allocation through a reinforcement learning predictor. As the per-request-grained control incurs significant overhead, we further design a pipeline for avoiding the accumulated effect on a workflow. Experimental results show that Maxwell not only saves up to 31% CPU resources but also reduces the standard deviation of latency by 1.9x. Its time overhead is negligible and the resource overhead is also limited when the query per second <= 500.

关键词： Resource allocation

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Reservoir: Named Data for Pervasive Computation Reuse at the Network Edge 20

Reservoir: Named Data for Pervasive Computation Reuse at the...

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20th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Al Azad, Md Washik Mastorakis, Spyridon Univ Nebraska Omaha Omaha NE 68182 USA

ISBN: (纸本)9781665416436

In edge computing use cases (e.g., smart cities), where several users and devices may be in close proximity to each other, computational tasks with similar input data for the same services (e.g., image or video annotation) may be offloaded to the edge. The execution of such tasks often yields the same results (output) and thus duplicate (redundant) computation. Based on this observation, prior work has advocated for "computation reuse", a paradigm where the results of previously executed tasks are stored at the edge and are reused to satisfy incoming tasks with similar input data, instead of executing these incoming tasks from scratch. However, realizing computation reuse in practical edge computing deployments, where services may be offered by multiple (distributed) edge nodes (servers) for scalability and fault tolerance, is still largely unexplored. To tackle this challenge, in this paper, we present Reservoir, a framework to enable pervasive computation reuse at the edge, while imposing marginal overheads on user devices and the operation of the edge network infrastructure. Reservoir takes advantage of Locality Sensitive Hashing (LSH) and runs on top of Named-Data networking (NDN), extending the NDN architecture for the realization of the computation reuse semantics in the network. Our evaluation demonstrated that Reservoir can reuse computation with up to an almost perfect accuracy, achieving 4.25-21.34. lower task completion times compared to cases without computation reuse.

关键词： Edge computing Computation Reuse Locality Sensitive Hashing Named-Data networking

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Smart EV Charging to Mitigate Range Anxiety in VANET Backbone Guided by Named Data networking and Block-Chain 2

Smart EV Charging to Mitigate Range Anxiety in VANET Backbon...

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2nd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Vendan, Vetri Chaudhary, Anamika Galgotias University Department of Computer Science and Engineering Greater Noida India Noida Institute of Engineering and Technology Department of Information Technology Greater Noida India

ISBN: (纸本)9798350347456

A interesting area of research for future connected car application is the development of an effective charging management system for on-the-go EVs, which is especially important given the inherent unpredictability in EV mobility. The main technological hurdles here are the necessity for intelligent decision making when choosing a CS and the related communication infrastructure for exchanging data between the grid and mobile EVs. Switching to electric vehicles has the potential to reduce pollution, vehicle maintenance expenses, and increasing gas prices (EVs). The range anxiety issue is a significant impediment to the rapid adoption of EV. Range anxiety can be alleviated by guaranteeing that a charging point will be found within the vehicle's driving range, subject to other constraints such as waiting time, reliability, cost, and so on. In this research, we suggest building a P2P infrastructure on top of a VANET so that users may trade power with one another in a secure and reliable manner. Although the labour itself is decentralised, secure, and reliable, the associated financial transactions are not. The proposed framework for exchanging electric charge is based on the intersection of three concepts from the realm of automotive social networks: data connectivity, the bitcoin, and the anxiety of having insufficient battery power. We address reliability using an established collaborative paradigm rooted in the field of automotive social networks. Organizational verification previous to trading and encrypted parameter exchange provide safety. With the help of named data networking, reliable charging points may be located using a set of criteria. The simulation results demonstrate that the proposed technique decreases the amount of time it takes to complete an energy transaction by 17%, boosts coverage for picking charging providers by 13%, and decreases the amount of erroneous provider filtering by 18%. © 2023 IEEE.

关键词： Commerce

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Encryption Techniques for Hadoop distributed File System (HDFS) 5

Encryption Techniques for Hadoop Distributed File System (HD...

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5th IEEE international conference on Advances in computing, Communication Control and networking, ICAC3N 2023

作者： Mishra, Anupam Kapoor, Nitika Chandigarh University Department Of Computer Science And Technology Mohali Punjab Gharuan India

ISBN: (纸本)9798350330861

The Hadoop distributed File System (HDFS) was created with the express purpose of making the management of large data sets a breeze. The absence of something in data encryption methods in HDFS, however, presents serious security concerns. While the introduction of encryption methods has improved data security, traditional algorithms struggle when processing larger files. This study aims to provide a novel hybrid encryption technique for protecting massive datasets by fusing Cipher-Text Policy Attribute-Based Encryption (CP-ABE) and Advanced Encryption Standard (AES). Java-based open-source project that includes the MapReduce framework for data processing and the HDFS. It modifies many Portable Operating System Interface (POSIX) standards to provide streaming access to file system information. Initiated as a platform for the open-source web crawler Apache Nutch, HDFS has already expanded to include other similar programs. Authors have compared the method to the one that was present in the system, they have implemented it to protect HDFS, and performed experimental research to prove its performance. Further, the authors have tested the suggested HDFS technique by encrypting files of various sizes and compared it to other algorithms, such as AES and AES with One-Time-Pad (OTP). The HDFS method improves performance significantly when it relates to file encryption. © 2023 IEEE.

关键词： Cryptography

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9th international conference on computing and Informatics, ICOCI 2023

9th International Conference on Computing and Informatics, I...

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9th international conference on computing and Informatics, ICOCI 2023

ISBN: (纸本)9789819995912

The proceedings contain 55 papers. The special focus in this conference is on computing and Informatics. The topics include: Sentiment Analysis of Arabic Dialects: A Review Study;charting Inclusive Digital Society Research Trends: A Bibliometric Analysis of E-Participation Through Social Media;systematic Literature Review and Bibliometric Analysis on Addressing the Vanishing Gradient Issue in Deep Neural Networks for Text Data;a Test Dataset of Offensive Malay Language by a Cyberbullying Detection Model on Instagram Using Support Vector Machine;tikTok Video Cluster Analysis Based on Trending Topic;a Video Summarization Method for Movie Trailer-Genre Classification Based on Emotion Analysis;e-Nose: Spoiled Food Detection Embedded Device Using Machine Learning for Food Safety Application;an Analysis of Objective Function Modification Approaches in Routing Protocols for Low Power and Lossy Networks: A Fuzzy Logic-Based Perspective;an Empirical Study of Label Size Effect on Classification Model Accuracy Using a Derived Rule from the Holy Quran Verses;large Scale Web Crawling and distributed Search Engines: Techniques, Challenges, Current Trends, and Future Prospects;anomalies in Mooring (Thin) Lines: Causes, Risk Mitigations, and Real Time Consequences of Failure – A Comprehensive Review;data Analytics Modelling System for Short Courses at Seberang Jaya Community College;examining the Software Developers’ Perception in Open-Source Software of Blockchain Project Using Association Rules Mining;support Vector Machine for Satellite Images Classification Using Radial Basis Function Kernel Method;a Regression Test Case Prioritization Framework for Software Sustainability;towards a Sustainable Digital Society: Supporting Producer Mobility in Named Data networking Through Immobile Anchor-Based Mechanism.

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Preparing for HPC on RISC-V: Examining Vectorization and distributed Performance of an Astrophysics Application with HPX and Kokkos

Preparing for HPC on RISC-V: Examining Vectorization and Dis...

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2024 Workshops of the international conference for High Performance computing, networking, Storage and Analysis, SC Workshops 2024

作者： Diehl, Patrick Syskakis, Panagiotis Dais, Gregor Brandt, Steven R. Kheirkhahan, Alireza Singanaboina, Srinivas Yadav Marcello, Dominic Taylor, Chris Leidel, John Kaiser, Hartmut Los AlamosNM87545 United States Louisiana State University Department of Physics and Astronomy Baton RougeLA70803 United States Louisiana State University LSU Center for Computation & Technology Baton RougeLA70803 United States University of Stuttgart IPVS Stuttgart70174 Germany Tactical Computing Labs LindsayTX776250 United States Louisiana State University Department of Computer Science Baton RougeLA70803 United States

ISBN: (纸本)9798350355543

In recent years, interest in RISC-V computing architectures has moved from academic to mainstream, especially in the field of High Performance computing where energy limitations are increasingly a concern. As of this year, the first single board RISC-V CPUs implementing the finalized ratified vector specification are being released. The RISC-V vector specification follows in the tradition of vector processors found in the CDC STAR-100, the Cray-1, the Convex C-Series, and the NEC SX machines and accelerators. The family of vector processors offers support for variable-length array processing as opposed to the fixed-length processing functionality offered by SIMD. Vector processors offer opportunities to perform vector-chaining which allows temporary results to be used without the need to resolve memory *** this work, we use the Octo-Tiger multi-physics, multi-scale, 3D adaptive mesh refinement astrophysics application to study these early RISC-V chips with vector machine support. We report on our experience in porting this modern C++ code (which is built upon several open-source libraries such as HPX and Kokkos) to RISC-V. In addition, we show the impact of the RISC-V Vector extension on a RISC-V single board computer by implementing the std :: experimental:simd interface and integrating it with our code. We also compare the application's performance, scalability, and power consumption on desktop-grade RISC-V computer to an A64FX *** results presented in this paper are part of a longer-term evaluation of RISC-V's viability for HPC applications. © 2024 IEEE.

关键词： asynchronous many-task system HPX Kokkos RISC-V scalable vector extensions task-based run time system vectorization

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