Ever since the end of the era of single processor performance improvement, we observe proliferation of multi and many-core architectures in almost all spheres of computing. Tablets, desktop PCs, workstation clusters, ...
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ISBN:
(纸本)9781450333160
Ever since the end of the era of single processor performance improvement, we observe proliferation of multi and many-core architectures in almost all spheres of computing. Tablets, desktop PCs, workstation clusters, and supercomputers are ripe with multi-core CPUs and/or accelerators. Although there are considerable architectural heterogeneity and scale differences present among these machine architectures, there is also significant commonality in their multicore building blocks. This situation revives the interest for possibility of parallel programming paradigms that are both efficient and portable across environments. We have been investigating such a programming paradigm for over a year and half now. Two key aspects of our work are development of a common machine abstraction for diverse hardware platforms and a clear separation of different concerns embodied in parallel programming. This is our first paper describing the philosophy, abstraction mechanism, programming model, and early results of our ongoing project. Copyright is held by the owner/author(s).
Multi-core processor has become the main body of the processor architecture, which demands more for operating system and application software. The original serial programming techniques on multi-core platform is no lo...
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parallel programming can provide higher computational performance over a sequential implementation by making use of the many cores available in parallel systems. However, the parallel-capable devices introduce complex...
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This paper describes the features of the parallel programming course content for the programs "Software and Administration of Information Systems" and "Information Security". We present the experie...
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Taskflow tackles the long-standing question: How can we make it easier for developers to program parallel and heterogeneous computer-aided design (CAD) applications with high performance and simultaneous high producti...
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Taskflow tackles the long-standing question: How can we make it easier for developers to program parallel and heterogeneous computer-aided design (CAD) applications with high performance and simultaneous high productivity? Taskflow introduces a new powerful task graph programming model to assist developers in the implementation of parallel and heterogeneous algorithms with complex control flow. We develop an efficient system runtime to solve many of the new scheduling challenges arising out of our models and optimize the performance across latency, energy efficiency, and throughput. Taskflow has demonstrated promising performance on both micro-benchmarks and real-world applications. As an example, Taskflow solved a large-scale circuit placement problem up to 17% faster, with 1.3x fewer memory, 2.1x less power consumption, and 2.9x higher throughput than two industrial-strength systems, oneTBB and StarPU, on a machine of 40 CPUs and 4 GPUs.
The work describes research and specification of basic paradigms of programming. The author analyzes and compares special features of parallel programming languages. A functional model of comparative description of im...
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With Cloud Computing emerging as a promising new approach for ad-hoc parallel data processing, major companies have started to integrate frameworks for parallel data processing in their product portfolio, making it ea...
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With Cloud Computing emerging as a promising new approach for ad-hoc parallel data processing, major companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. We have entered the Era of Big Data. The explosion and profusion of available data in a wide range of application domains rise up new challenges and opportunities in a plethora of disciplines-ranging from science and engineering to biology and business. One major challenge is how to take advantage of the unprecedented scale of data-typically of heterogeneous nature-in order to acquire further insights and knowledge for improving the quality of the offered services. To exploit this new resource, we need to scale up and scale out both our infrastructures and standard techniques. Our society is already data-rich, but the question remains whether or not we have the conceptual tools to handle it. In this paper we discuss and analyze opportunities and challenges for efficient parallel data processing. Big Data is the next frontier for innovation, competition, and productivity, and many solutions continue to appear, partly supported by the considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. We review various parallel and distributed programming paradigms, analyzing how they fit into the Big Data era, and present modern emerging paradigms and frameworks. To better support practitioners interesting in this domain, we end with an analysis of on-going research challenges towards the truly fourth generation data-intensive science.
The aim of this study is to present an approach to the introduction into pipeline and parallel computing, using a model of the multiphase queueing system. Pipeline computing, including software pipelines, is among the...
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The aim of this study is to present an approach to the introduction into pipeline and parallel computing, using a model of the multiphase queueing system. Pipeline computing, including software pipelines, is among the key concepts in modern computing and electronics engineering. The modern computer science and engineering education requires a comprehensive curriculum, so the introduction to pipeline and parallel computing is the essential topic to be included in the curriculum. At the same time, the topic is among the most motivating tasks due to the comprehensive multidisciplinary and technical requirements. To enhance the educational process, the paper proposes a novel model-centered framework and develops the relevant learning objects. It allows implementing an educational platform of constructivist learning process, thus enabling learners' experimentation with the provided programming models, obtaining learners' competences of the modern scientific research and computational thinking, and capturing the relevant technical knowledge. It also provides an integral platform that allows a simultaneous and comparative introduction to pipelining and parallel computing. The programming language C for developing programming models and message passing interface (MPI) and OpenMP parallelization tools have been chosen for implementation.
Over the years, several parallel programming Models (PPMs) have supported the abstraction of programming complexity for parallel computer systems. However, few studies aim to evaluate the productivity reached by such ...
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