For decades, the processor manufacturers have attempted to achieve performance gains by increasing the clock frequency on single-core processors. But physical problems-such as the high power dissipation-lead to the re...
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(纸本)9788897999683
For decades, the processor manufacturers have attempted to achieve performance gains by increasing the clock frequency on single-core processors. But physical problems-such as the high power dissipation-lead to the release of the first multi-core processors on the market in 2005. To benefit from the multi-core architecture, parallel programming is required. However, this programming model requires a different approach and is associated with certain risks and pitfalls. This paper focuses on modelling of certain test scenarios for two common multi-core specific problems, namely oversubscription and false sharing. Various simulations and tests offered solutions and design patterns to avoid such problems. Results have shown that the problems have a fatal impact on the execution time, so that the performance gain on the multi-core system is nearly nonexistent. Thence, any software developer must have in-depth knowledge of the used hardware and software to benefit as much as possible from multi-core architectures.
Now we have the need for methodics of teaching the topic "parallel computing" in secondary school. The paper presents a three-year experience of the author in this field: a methodical approach, the selection...
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Now we have the need for methodics of teaching the topic "parallel computing" in secondary school. The paper presents a three-year experience of the author in this field: a methodical approach, the selection of materials, the business games, experience of tasks on parallel computing at the contest "TRIZformashka", classes of tasks, examples of tasks, program executors, texts for propaedeutic textbook on informatics.
DASH is a new parallel programming model for HPC which is implemented as a C++ template library on top of a runtime library implementing various PGAS (Partitioned Global Address Space) substrates. DASH’s goal is to b...
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Among all classes of parallel programming abstractions, lock-free data structures are considered one of the most scalable and efficient thanks to their fine-grained style of synchronization. However, they are also cha...
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Transactional memory is a perspective abstraction for the creating a scalable parallel programs for multi-core systems. It will be included in C++17. In this work, are proposed optimization method of conflicts detecti...
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Transactional memory is a perspective abstraction for the creating a scalable parallel programs for multi-core systems. It will be included in C++17. In this work, are proposed optimization method of conflicts detection, that accur in parallel programs with the software transactional memory during execution. The autors have implemented a module for GCC compiler for profiling parallel programs with software transactional memory and a tool for adaptive tuning runtime-library. The efficiency of method is investigated on the STAMP benchmarks.
The APGAS programming model is a powerful computing paradigm for multi-core and massively parallel computer architectures. It allows for the dynamic creation and distribution of thousands of threads amongst hundreds o...
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Discrete event simulations (DES) are central to exploration of "what-if" scenarios in many domains including networks, storage devices, and chip design. Accurate simulation of dynamically varying behavior of...
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We describe a family of difference schemes for the numerical solving of first-order multi-dimensional equations with partial derivatives and time delay. The order of approximation, stability and order of convergence a...
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Bayesian Network algorithms are widely applied in the fields of bioinformatics, document classification, data mining, and marketing informatics. In this paper, three Bayesian Network algorithms are evaluated: Naive Ba...
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Bayesian Network algorithms are widely applied in the fields of bioinformatics, document classification, data mining, and marketing informatics. In this paper, three Bayesian Network algorithms are evaluated: Naive Bayes, Tree Augmented Naive Bayes and Ordering-based Bayesian Networks. The algorithms are implemented using Scala, the bnlearn library in R and the WEKA software package. Several data sets with varying levels of attributes are used to test the accuracy of the algorithms and implementation testing is performed across all software platforms. We also parallelize these algorithms for efficiency gains when handling huge data sets. Significant speed-ups were achieved based on parallel processing.
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