14,863 research outputs found
Application of Supercomputer Technologies for Simulation of Socio-Economic Systems
To date, an extensive experience has been accumulated in investigation of problems related to quality, assessment of management systems, modeling of economic system sustainability. The studies performed have created a basis for formation of a new research area — Economics of Quality. Its tools allow to use opportunities of model simulation for construction of the mathematical models adequately reflecting the role of quality in natural, technical, social regularities of functioning of the complex socioeconomic systems. Extensive application and development of models, and also system modeling with use of supercomputer technologies, on our deep belief, will bring the conducted researches of social and economic systems to essentially new level. Moreover, the current scientific research makes a significant contribution to model simulation of multi-agent social systems and that isn’t less important, it belongs to the priority areas in development of science and technology in our country. This article is devoted to the questions of supercomputer technologies application in public sciences, first of all, — regarding technical realization of the large-scale agent-focused models (AFM). The essence of this tool is that owing to increase in power of computers it became possible to describe the behavior of many separate fragments of a difficult system, as social and economic systems represent. The article also deals with the experience of foreign scientists and practicians in launching the AFM on supercomputers, and also the example of AFM developed in CEMI RAS, stages and methods of effective calculating kernel display of multi-agent system on architecture of a modern supercomputer will be analyzed. The experiments on the basis of model simulation on forecasting the population of St. Petersburg according to three scenarios as one of the major factors influencing the development of social and economic system and quality of life of the population are presented in the conclusion
Proceedings of the 3rd Workshop on Domain-Specific Language Design and Implementation (DSLDI 2015)
The goal of the DSLDI workshop is to bring together researchers and
practitioners interested in sharing ideas on how DSLs should be designed,
implemented, supported by tools, and applied in realistic application contexts.
We are both interested in discovering how already known domains such as graph
processing or machine learning can be best supported by DSLs, but also in
exploring new domains that could be targeted by DSLs. More generally, we are
interested in building a community that can drive forward the development of
modern DSLs. These informal post-proceedings contain the submitted talk
abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel
discussion on Language Composition
swTVM: Exploring the Automated Compilation for Deep Learning on Sunway Architecture
The flourish of deep learning frameworks and hardware platforms has been
demanding an efficient compiler that can shield the diversity in both software
and hardware in order to provide application portability. Among the exiting
deep learning compilers, TVM is well known for its efficiency in code
generation and optimization across diverse hardware devices. In the meanwhile,
the Sunway many-core processor renders itself as a competitive candidate for
its attractive computational power in both scientific and deep learning
applications. This paper combines the trends in these two directions.
Specifically, we propose swTVM that extends the original TVM to support
ahead-of-time compilation for architecture requiring cross-compilation such as
Sunway. In addition, we leverage the architecture features during the
compilation such as core group for massive parallelism, DMA for high bandwidth
memory transfer and local device memory for data locality, in order to generate
efficient code for deep learning application on Sunway. The experimental
results show the ability of swTVM to automatically generate code for various
deep neural network models on Sunway. The performance of automatically
generated code for AlexNet and VGG-19 by swTVM achieves 6.71x and 2.45x speedup
on average than hand-optimized OpenACC implementations on convolution and fully
connected layers respectively. This work is the first attempt from the compiler
perspective to bridge the gap of deep learning and high performance
architecture particularly with productivity and efficiency in mind. We would
like to open source the implementation so that more people can embrace the
power of deep learning compiler and Sunway many-core processor
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
A Comparison of Parallel Graph Processing Implementations
The rapidly growing number of large network analysis problems has led to the
emergence of many parallel and distributed graph processing systems---one
survey in 2014 identified over 80. Since then, the landscape has evolved; some
packages have become inactive while more are being developed. Determining the
best approach for a given problem is infeasible for most developers. To enable
easy, rigorous, and repeatable comparison of the capabilities of such systems,
we present an approach and associated software for analyzing the performance
and scalability of parallel, open-source graph libraries. We demonstrate our
approach on five graph processing packages: GraphMat, the Graph500, the Graph
Algorithm Platform Benchmark Suite, GraphBIG, and PowerGraph using synthetic
and real-world datasets. We examine previously overlooked aspects of parallel
graph processing performance such as phases of execution and energy usage for
three algorithms: breadth first search, single source shortest paths, and
PageRank and compare our results to Graphalytics.Comment: 10 pages, 10 figures, Submitted to EuroPar 2017 and rejected. Revised
and submitted to IEEE Cluster 201
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