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43rd Annual International Conference on Parallel Processing (ICPP)

作者： Williams, Kevin Le, Matthew Kaminski, Ted Van Wyk, Eric Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781479956180

This paper describes a compiler extension to our prototype extensible C translator that adds new features for parallel execution of matrix operations and shows their application to problems in spatio-temporal data mining. The extension provides new language features for constructing new matrices, mapping functions over elements of a matrix, and accumulating operations that, for example, can sum values in a matrix. It also provides the appropriate semantic analysis to check for errors before translating the constructs down to parallel C code. The extension also provides features that let the programmer indicate how the extension translates these matrix constructs down to C code. Programmers seeking higher levels of performance can specify how the underlying for-loops are structured so that code using, for example, loop-tiling techniques or vector processors, is generated. In general, compiler extensions supported by our approach allow new domain-specific syntax and semantic analyses to be easily added to the host language. Specifications of the host C language and the extensions are composed to create a custom translator that maps extended C programs down to plain (parallel) C code, checking for domain-specific errors and applying high-level domain-specific optimizations in the process.

关键词： C language data mining formal specification matrix algebra optimisation parallel programming program compilers compiler extension high-level domain-specific optimization language feature loop-tiling technique matrix operation parallel C code parallel matrix programming prototype extensible C translator semantic analysis spatio-temporal data mining syntax analysis vector processor Generators Indexing Instruction sets Oceans Semantics Syntactics extensible languages matrix programming

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Magas: matrix-based asynchronous graph analytics on shared memory systems

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JOURNAL OF SUPERCOMPUTING 2022年第4期78卷 5650-5680页

作者： Luo, Le Liu, Yi Yang, Hailong Qian, Depei Anhui Normal Univ Sch Comp & Informat Wuhu 241002 Anhui Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

Graph analytics plays an important role in many areas such as big data and artificial intelligence. The vertex-centric programming model provides friendly interfaces to programmers and is extensively used in graph processing frameworks. However, it is prone to generate many irregular memory accesses and scheduling overhead due to vertex-based execution and scheduling of programs in the backend. Instead, the matrix-based model provides a different approach by using high-performance matrix operations in the backend to improve the efficiency of graph processing. Unfortunately, current matrix-based frameworks only support the synchronous parallel model, which constrains its application to various graph algorithms. To address these problems, this paper proposes a graph processing framework, which combines matrix operations with the asynchronous model while providing friendly programming interfaces similar to vertex-centric programming model. Firstly, we propose an approach to map the vertex-based graph processing to matrix operations in the asynchronous model. Then, we propose two asynchronous scheduling policies, Gauss-Seidel policy and relaxed Gauss-Seidel policy, for different graph algorithms. After that, our framework applies the batch scheduling and optimized in-memory data structure to reduce the scheduling overhead introduced by the asynchronous model. Experimental results show that our framework performs better than the popular vertex programming frameworks such as GraphLab and GRACE in both performance and speedup and achieves similar performance compared to the BSP-based matrix framework such as GraphMat.

关键词： Graph analytics Graph processing matrix programming Asynchronous model

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REVIEWS OF GEOGRAPHIC SOFTWARE

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The Professional Geographer 1987年第1期39卷

Reviews of geographic software in this article: DEMO-GRAPHICS: WORLD POPULATIONS AND PROJECTIONS. ESP GAUSS. CEMODEL S. Damus LIMDEP. William H. Greene MICROSTAT 4.1 OTIS PCIPS. (Personal Computer Image Processing System) . H.J. Meyers and R. Bernstein. REGRESSION ANALYSIS OF TIME SERIES (RATS) SPSS/PC+ URBAN DATA MANAGEMENT SOFTWARE (UDMS)

关键词： population projections age pyramids population growth econometrics regression forecasting multi-equation modelling mathematical methods statistical methods matrix programming regression IBM PC multivariate logit probit tobit regression IBM PC multivariate logit probit tobit regression IBM PC multivariate logit probit tobit regression IBM PC multivariate logit probit tobit regression IBM PC multivariate logit probit tobit statistics regression analysis of variance microcomputer mapping image processing remote sensing microcomputer mapping image processing remote sensing time series econometrics forecasting ARIMA models microcomputer software statistical software descriptive analyses analysis of variance regression microcomputer Z80 chip GIS mapping statistical analysis spatial modeling expandibility

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