Sparse matrix computations for dynamic network centrality

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Sparse matrix computations for dynamic network centrality

*Correspondence: [email protected] University of Strathclyde, 16 Richmond St, G1 1XQ Glasgow, UK Abstract Time sliced networks describing human-human digital interactions are typically large and sparse. This is the case, for example, with pairwise connectivity describing social media, voice call or physical proximity, when measured over seconds, minutes or hours. However, if we wish...

متن کامل

Visualizing Sparse Matrix Computations

This paper describes ideas and tools that help with the visualization of sparse matrix computations. The paper uses the Sparse Matrix Manipulation System, an environment for handling sparse matrices of all types in a vy flexible manner via ASCII file interfaces. Two commands in this environment are ShowMalrix and ShowTree. The first illustrates the pattern of nonzeroes of a sparse matrix. The s...

متن کامل

Rescheduling for Locality in Sparse Matrix Computations

In modern computer architecture the use of memory hierarchies causes a program's data locality to directly aaect performance. Data locality occurs when a piece of data is still in a cache upon reuse. For dense matrix computations, loop transformations can be used to improve data locality. However, sparse matrix computations have non-aane loop bounds and indirect memory references which prohibit...

متن کامل

HPF-2 Support for Dynamic Sparse Computations

There is a class of sparse matrix computations, such as direct solvers of systems of linear equations, that change the fill-in (nonzero entries) of the coefficient matrix, and involve row and column operations (pivoting). This paper addresses the problem of the parallelization of these sparse computations from the point of view of the parallel language and the compiler. Dynamic data structures ...

متن کامل

Parallelization Primitives for Dynamic Sparse Computations

We characterize a general class of algorithms common in machine learning, scientific computing, and signal processing, whose computational dependencies are both sparse, and dynamically defined throughout execution. Existing parallel computing runtimes, like MapReduce and GraphLab, are a poor fit for this class because they assume statically defined dependencies for resource allocation and sched...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Applied Network Science

سال: 2017

ISSN: 2364-8228

DOI: 10.1007/s41109-017-0038-z