The proceedings contain 76 papers. The topics discussed include: list scheduling: alone, with foresight, and with lookahead;massivelyparallelcomputingsystems with real time constraints the 'algorithm architectu...
ISBN:
(纸本)0818663227
The proceedings contain 76 papers. The topics discussed include: list scheduling: alone, with foresight, and with lookahead;massivelyparallelcomputingsystems with real time constraints the 'algorithm architecture adequation' methodology;systolic-type implementation of matrix computations based on the Faddeev algorithm;architecture and realization of the modular expandable multiprocessor system MEMSY;communications is more than I/O;avoiding memory contention on tightly coupled multiprocessors;cache coherence in a multiport memory environment;stochastic modeling of multiprocessor reliability;comparing architectures using throughput-versus-cost modeling;optimal triple modular redundancy embeddings in the hypercube;and general purpose massivelyparallelsystems: the role of programming environments.
An increased and growing interest in large-scale data processing has triggered a demand for specialized algorithms that thrive in massivelyparallel shared-nothing systems. To answer the question of how to efficiently...
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Trusted computing technology represents a significant element of cyber security systems, serving to guarantee the integrity and accessibility of data and systems. The incorporation of Trusted computing introduces a se...
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In this paper, we present a deterministic Õ(log1/3)-round algorithm for the 2-ruling set problem in the sublinear massivelyparallel Computation (MPC) model. This improves upon the fastest known deterministic 2-r...
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This special issue is dedicated to examining the rapidly evolving fields of artificial intelligence, mathematical modeling, and optimization, with particular emphasis on their growing importance in computational scien...
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This special issue is dedicated to examining the rapidly evolving fields of artificial intelligence, mathematical modeling, and optimization, with particular emphasis on their growing importance in computational science. It features the most notable papers from the "Mathematical Modeling and Problem Solving" workshop at PDPTA'24, the 30th internationalconference on parallel and Distributed Processing Techniques and Applications. The issue showcases pioneering research in areas such as natural language processing, system optimization, and high-performance computing. The nine selected studies include novel AI-driven methods for chemical compound generation, historical text recognition, and music recommendation, along with advancements in hardware optimization through reconfigurable accelerators and vector register sharing. Additionally, evolutionary and hyper-heuristic algorithms are explored for sophisticated problem-solving in engineering design, and innovative techniques are introduced for high-speed numerical methods in large-scale systems. Collectively, these contributions demonstrate the significance of AI, supercomputing, and advanced algorithms in driving the next generation of scientific discovery.
We propose a novel parallelcomputing that allows processors to access data in predictable time without the need to access it from different locations in memory using addresses. It uses orbital data that is mapped to ...
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ISBN:
(纸本)9783031762727;9783031762734
We propose a novel parallelcomputing that allows processors to access data in predictable time without the need to access it from different locations in memory using addresses. It uses orbital data that is mapped to time and is made available to multiple processors at the same time in multiple different orbits and at a specific predictable time in each orbit. This allows processors in different orbits to share the same data, eliminating the problem of sharing data at the same time among multiple processors. It provides processors with the ability to hide the waiting time when accessing shared data by overlapping it with useful work on another data while allowing other processors to work on the shared data in another orbit. The performance of this novel method shows significant improvements in scalability compared to that of conventional parallelcomputing.
We study the maximum set coverage problem in the massivelyparallel model. In this setting, m sets that are subsets of a universe of n elements are distributed among m machines. In each round, these machines can commu...
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Linear algebra algorithms, such as the Householder QR decomposition, are pivotal in various applications including signal processing, optimization, and numerical solutions to systems of linear equations. Traditional s...
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ISBN:
(纸本)9783031814037;9783031814044
Linear algebra algorithms, such as the Householder QR decomposition, are pivotal in various applications including signal processing, optimization, and numerical solutions to systems of linear equations. Traditional sequential implementations of the Householder algorithm face significant limitations in terms of performance and scalability when applied to large matrices. To overcome these constraints, this paper explores the parallelization of the Householder QR algorithm on Graphics Processing Units (GPUs) using CUDA, a parallelcomputing platform and programming model developed by NVIDIA. Our method ensures the availability of critical intermediate data, distinguishing it from standard libraries like cuSOLVER, which modify the processing order and often discard important intermediate computations. By leveraging CUDA streams, we achieve enhanced parallelism without compromising the integrity of the algorithm's sequence or the accessibility of intermediate data. Our performance analysis reveals that our implementation achieves efficiency comparable to cuSOLVER, making it a viable option. This study not only presents a novel implementation but also extends the potential for GPU-accelerated linear algebra procedures to benefit a wider range of scientific and engineering applications.
In this paper, we propose an output reproducible framework that executes parallel sequence comparison algo rithms, computing the edit distance. The framework generates tables/graphics and linear regressions that can b...
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