Quantum annealers like those from D-Wave Systems implement adiabatic quantum computing to solve optimization problems, but their analog nature and limited control functionalities present challenges to correcting or mi...
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ISBN:
(纸本)9798400705977
Quantum annealers like those from D-Wave Systems implement adiabatic quantum computing to solve optimization problems, but their analog nature and limited control functionalities present challenges to correcting or mitigating errors. As quantum computingadvances towards applications, effective error suppression is an important research goal. We propose a new approach called replication based mitigation (RBM) based on parallel quantum annealing. In RBM, physical qubits representing the same logical qubit are dispersed across different copies of the problem embedded in the hardware. This mitigates hardware biases, is compatible with limited qubit connectivity in current annealers, and is suited for available noisy intermediate-scale quantum (NISQ) annealers. Our experimental analysis shows that RBM provides solution quality on par with previous methods while being compatible with a much wider range of hardware connectivity patterns. In comparisons against standard quantum annealing without error mitigation, RBM consistently improves the energies and ground state probabilities across parameterized problem sets.
MPI collective communications play an important role in coordinating and exchanging data among parallel processes in high performance computing. Various algorithms exist for implementing MPI collectives, each of which...
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ISBN:
(纸本)9783031488023;9783031488030
MPI collective communications play an important role in coordinating and exchanging data among parallel processes in high performance computing. Various algorithms exist for implementing MPI collectives, each of which exhibits different characteristics, such as message overhead, latency, and scalability, which can significantly impact overall system performance. Therefore, choosing a suitable algorithm for each collective operation is crucial to achieve optimal performance. In this paper, we present our experience with MPI collectives algorithm selection on a large-scale supercomputer and highlight the impact of network traffic and system workload as well as other previously-investigated parameters such as message size, communicator size, and network topology. Our analysis shows that network traffic and system workload can make the performance of MPI collectives highly variable and, accordingly, impact the algorithm selection strategy.
Authentication, authorization, and access control are fundamental functionalities that are crucial for network infrastructures and software applications. These functionalities work together to create a fundamental sec...
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ISBN:
(纸本)9798331529109;9798331529093
Authentication, authorization, and access control are fundamental functionalities that are crucial for network infrastructures and software applications. These functionalities work together to create a fundamental security layer that allows administrative entities to control user actions. Implementing a security layer may be simple for basic applications, but as modern digital infrastructures become more complex, more advanced security systems are needed. Traditional perimeter-based security models, long relied upon for securing large networks, exhibit vulnerabilities and lack adaptability to modern architectures. As technology advances, there is a growing demand for new authentication and authorization systems to keep up with the changes. Zero Trust (ZT) emerges as a paradigm embodying such principles and concepts for constructing contemporary security systems. This paper introduces a ZT-based Single Sign-On (SSO) framework to demonstrate how ZT can be realized in multi-service environments using Attribute-Based Access Control (ABAC). A prototype is developed to show the feasibility and applicability of the proposed framework in a smart city context.
Due to their structure, metaheuristics such as parallel evolutionary algorithms (PEA) are well suited to be run on parallel and distributed infrastructure, e.g. supercomputers. However, there are still many issues tha...
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ISBN:
(纸本)9783031708183;9783031708190
Due to their structure, metaheuristics such as parallel evolutionary algorithms (PEA) are well suited to be run on parallel and distributed infrastructure, e.g. supercomputers. However, there are still many issues that are not well researched in this context, e.g. existence of delays in HPC-grade implementations of metaheuristics and how they affect the computation itself. The lack of this knowledge may expose the fact, that the power of supercomputers in this context may be not properly used. We want to focus our research on examining such white spots. In the paper we focus on giving the evidence for the existence of delays, showing the differences among them in different island topologies, try to explain their nature and prepare to propose dedicated migration operators considering these observations.
The proceedings contain 13 papers. The topics discussed include: a study on the performance of distributed storage systems in edge computing environments;RESCAPE: a resource estimation system for microservices with gr...
ISBN:
(纸本)9798350387339
The proceedings contain 13 papers. The topics discussed include: a study on the performance of distributed storage systems in edge computing environments;RESCAPE: a resource estimation system for microservices with graph neural network and profile engine;PrometheusMigrate: efficient live migration of confidential virtual machine with software abstraction;the cost perspective of adopting large language model-as-a-service;DCSA: the deployment mechanism of chained serverless applications in JointCloud environment;parallel computation in dynamic fog computing networks: a multi-armed bandit learning-based decentralized matching approach;and IBRI: an IoT solution for building collapse risk identification in smart cities.
Modern materials science research problems present a challenge to data science and analytics as experiments generate Petabyte-scale spatiotemporal datasets that span a number of modalities and formats. Creating comput...
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ISBN:
(纸本)9798350383225
Modern materials science research problems present a challenge to data science and analytics as experiments generate Petabyte-scale spatiotemporal datasets that span a number of modalities and formats. Creating computing infrastructure and frameworks that support the scale and diversity of materials science data while remaining accessible for materials scientists to use is a non-trivial task. We have developed the Common Research Analytics and Data Lifecycle Environment (CRADLE) to solve the challenges of materials data science through a scalable research computing framework and cyber infrastructure that can (1) handle large-scale, heterogeneous datasets (2) provide a flexible toolbox for building machine learning pipelines that span from ingestion to model deployment (3) be accessible to research scientists with limited to extensive computational backgrounds and (4) utilize a myriad of low performance to high performance computer systems. CRADLE is a framework that integrates distributed systems like Hadoop and High-Performance computing (HPC) infrastructure to handle materials data at scale. This all enables the general materials data scientist to query Petabytes of data and train thousands of models in a parallel, distributed environment. We demonstrate three use cases for CRADLE to benchmark its capability to ingest and analyze spatiotemporal materials data at scale. These tasks span three data modalities: transforming 2.6 billion Photovoltaic time-series power measurements, training hundreds of deep learning models on Atomic Force Microscopy images, and ingesting 27 billion geospatial data points. CRADLE exemplifies an overarching framework that accelerates time to science, extends to other domains with similar challenges, and expands the horizon of data science and research.
Mosaic Flow is a novel domain decomposition method designed to scale physics-informed neural PDE solvers to large domains. Its unique approach leverages pre-trained networks on small domains to solve partial different...
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The proceedings contain 28 papers. The topics discussed include: performance and usability implications of multiplatform and WebAssembly containers;operations patterns for hybrid quantum applications;optimization of c...
ISBN:
(纸本)9789897587474
The proceedings contain 28 papers. The topics discussed include: performance and usability implications of multiplatform and WebAssembly containers;operations patterns for hybrid quantum applications;optimization of cloud-native application execution over the edge cloud continuum enabled by DVFS;energy-aware node selection for cloud-based parallel workloads with machine learning and infrastructure as code;security-aware allocation of replicated data in distributed storage systems;performance analysis of mdx ii: a next-generation cloud platform for cross-disciplinary data science research;data orchestration platform for AI workflows execution across computing continuum;framework for decentralized data strategies in virtual banking: navigating scalability, innovation, and regulatory challenges in Thailand;and anomaly detection for partially observable container systems based on architecture profiling.
distributedcomputing (DC) involves a collection of tasks (or modules) executed in parallel on different compute nodes connected through a network. Cloud Service providers (CSP) such as Azure[1], Amazon[2], and Google...
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ISBN:
(纸本)9781450397964
distributedcomputing (DC) involves a collection of tasks (or modules) executed in parallel on different compute nodes connected through a network. Cloud Service providers (CSP) such as Azure[1], Amazon[2], and Google[3] are providing DC platforms as PaaS (Platform As A Service) offerings. These cloud platforms reduce implementation costs but have a significant drawback as these services can be configured to spawn only a single type of compute node for executing all the tasks in the DC environment. These drawback lead to inefficiency in execution cost and time as each task will have specific compute node requirements. This paper presents a novel framework called TreeOptimizer(TO) to resolve these shortcomings. TO uses a classifier-based dynamic task scheduling to determine the best available node to perform the task. The framework has been tested in Azure Batch[1] for an Oil Industry use case for extracting data from scanned images. Experimental results indicate that TO significantly reduces the overall execution cost by 68% and processing time by 8%. Although this paper uses Batch Service to explain the proposed framework, it can be applied to other PaaS DC platforms.
This paper introduces Speedcode, an online programming platform that aims to improve the accessibility of software performance-engineering education. At its core, Speedcode provides a platform that lets users gain han...
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ISBN:
(纸本)9798350364613;9798350364606
This paper introduces Speedcode, an online programming platform that aims to improve the accessibility of software performance-engineering education. At its core, Speedcode provides a platform that lets users gain hands-on experience in software performance engineering and parallel programming by completing short programming exercises. Speedcode challenges users to develop fast multicore solutions for short programming problems and evaluates their code's performance and scalability in a quiesced cloud environment. Speedcode supports parallel programming using OpenCilk, task-parallelcomputing platform that is open-source and easy to program, teach and use for research. Speedcode aims to reduce barriers to learning and teaching software performance engineering. It allows users to run and evaluate their code on modern multicore machines from their own computer without installing any software. This provides users an easy introduction to the topic, and enables teachers to more easily incorporate lessons on software performance engineering into their courses without incurring the onerous overhead of needing to setup computing environments for their students.
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