The Open structure for allotted and Cooperative Media Algorithms (OADCMA) is an open-deliver framework imparting a plug-in platform that lets customers, without problem, develop distributed and cooperative media algor...
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The proceedings contain 15 papers. The special focus in this conference is on parallelcomputing Technologies. The topics include: parallel Generation and Analysis of Optimal Chordal Ring Networks Using Pyth...
ISBN:
(纸本)9783031416729
The proceedings contain 15 papers. The special focus in this conference is on parallelcomputing Technologies. The topics include: parallel Generation and Analysis of Optimal Chordal Ring Networks Using Python Tools on Kunpeng Processors;Combinatorial Aspect of Code Restructuring for Virtual Memory Computer Systems Under WS Swapping Strategy;probabilistic Resources Allocation with Group Dependencies in distributedcomputing;multicriteria Task Distribution Problem for Resource-Saving Data Processing;scheduling of Workflows with Task Resource Requirements in Cluster Environments;Verifying the Correctness of HPC Performance Monitoring Data;automatic parallelization of Iterative Loops Nests on distributed Memory computing Systems;didal: distributed Data Library for Development of parallel Fragmented Programs;Trace Balancing Technique for Trace Playback in LuNA System;Case Study for Running Memory-Bound Kernels on RISC-V CPUs;pair of Genes: Technical Validation of distributed Causal Role Attribution to Gene Network Expansion;HiTViSc: High-Throughput Virtual Screening as a Service;expanding the Cellular Automata Topologies Library for parallel Implementation of Synchronous Cellular Automata.
Integrated data analysis pipelines combine rigorous data management and processing, high-performance computing and machine learning tasks. While these systems and operations share many compilation and runtime techniqu...
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ISBN:
(纸本)9783031488023;9783031488030
Integrated data analysis pipelines combine rigorous data management and processing, high-performance computing and machine learning tasks. While these systems and operations share many compilation and runtime techniques, data analysts and scientists are currently dealing with multiple systems for each stage of their pipeline. DAPHNE is an open and extensible system infrastructure for such pipelines, including language abstractions, compilation and runtime techniques, multilevel scheduling, hardware accelerators and computational storage. In this demonstration, we focus on the DAPHNE runtime that provides the implementation of kernels for local, distributed and accelerator-enhanced operations, vectorized execution, integration with existing frameworks and libraries for productivity and interoperability, as well as efficient I/O and communication primitives.
Real-time parallel applications with industrial automation are applications that are designed to run in parallel, in order to accomplish a task in a shorter amount of time. These applications are used in industrial en...
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In recent years, distributed data-processing frameworks have become popular for processing big data. However, in an HPC, where the computation and storage nodes are separated, the bandwidth between the computation and...
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ISBN:
(纸本)9798350370621
In recent years, distributed data-processing frameworks have become popular for processing big data. However, in an HPC, where the computation and storage nodes are separated, the bandwidth between the computation and storage components is small, causing a reduction in data processing throughput. Therefore, in this paper, data were stored on the computation node to solve the data processing throughput degradation. We propose an I/O acceleration method that integrates Apache Arrow and CHFS. It leverages non-volatile memory, a state-of-the-art storage device, via CHFS and leverages CHFS from a distributed data processing framework via Apache Arrow's abstract file system API. The evaluation results showed that the system achieved up to 11.60 times higher bandwidth than when reading data from the parallel file system Lustre. This study also compared with Apache Arrow with BeeOND and UnifyFS, other ad hoc filesystems. The proposed Apache Arrow CHFS showed up to 1.67x/1.23x better write performance. The implementation is published at https://***/tsukuba-hpcs/arrow-chfs
The most important and common consensus mechanism in the blockchain is the Proof-of-Work (PoW) algorithm, which requires a large amount of energy and consumes more time from the miners to reach a single distributed ag...
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The Budget-based Neighboring Object Group Query (BR-NOGQ) is a novel type of location-based query that considers both the spatial relationships between objects and the user's budget constraints on experiencing the...
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The rapid evolution of smart grids has increased the complexity of power system structures and operational characteristics, presenting new challenges in system management and reliability. Traditional power system simu...
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Today, the amount of data generated each year is growing exponentially, directly affecting the time required for its analysis. This problem worsens with high-dimensional datasets, such as those used in electroencephal...
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ISBN:
(纸本)9783031646287;9783031646294
Today, the amount of data generated each year is growing exponentially, directly affecting the time required for its analysis. This problem worsens with high-dimensional datasets, such as those used in electroencephalography, so a good feature selection method and techniques that improve algorithms' efficiency are increasingly relevant. Consequently, computing time and energy consumption are reduced, which could be used to explore more solutions to the problem. However, it is also necessary to adapt the applications to take advantage of the hardware offered by high-performance computing systems. Therefore, in this work, a parallel and distributed binary particle swarm optimization algorithm has been implemented, used as a feature selection method, and applied to two real electroencephalography datasets: the University of Essex dataset and the well-known BCI Competition IV 2a dataset. The proposed method has been analyzed in a multi-node computing cluster, not only in terms of classification accuracy, but also from the energy-time point of view to study its impact depending on different experimental conditions and datasets used.
The exponential data volume and complexity of Machine Learning (ML) algorithms has resulted in an increase in computational limitations, which affects artificial intelligence [AI]. This research seeks to establish whe...
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