Preface 1. Background in linear algebra 2. Discretization of partial differential equations 3. Sparse matrices 4. Basic iterative methods 5. Projection methods 6. Krylov subspace methods Part I 7. Krylov subspace methods Part II 8. Methods related to the normal equations 9. Preconditioned iterations 10. Preconditioning techniques 11. Parallel implementations 12. Parallel preconditioners 13. Multigrid methods 14. Domain decomposition methods Bibliography Index.

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Iterative Methods for Sparse Linear Systems

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.

/pdf/scipy-1-0-fundamental-algorithms-for-scientific-computing-in-111lr790ol.pdf

SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels

/pdf/matrix-factorization-techniques-for-recommender-systems-1ygvnzk1c8.pdf

Matrix Factorization Techniques for Recommender Systems

https://users.fmrib.ox.ac.uk/~jesper/papers/readgroup_071009/Ashburner07.pdf

A fast diffeomorphic image registration algorithm

We present an overview of the lattice Boltzmann method (LBM), a parallel and efficient algorithm for simulating single-phase and multiphase fluid flows and for incorporating additional physical complexities. The LBM is especially useful for modeling complicated boundary conditions and multiphase interfaces. Recent extensions of this method are described, including simulations of fluid turbulence, suspension flows, and reaction diffusion systems.

Lattice boltzmann method for fluid flows

Techniques for data block write mapped to memory bank are provided In one aspect, a block of data to be written to a line in a bank of a rank of memory may be received The rank of memory may comprise multiple banks, and the block of data may be written to a number of memory devices determined by the size of the data block A memory device mapping may be retrieved The mapping may determine the order in which the block of data is written to the memory devices within the rank It may be determined when the banks of the memory devices used to store the line are available for writing The block of data may be written to the banks of the memory devices when the banks are available for writing

Data block write mapped to memory bank

In some examples, a graph processing server is communicatively linked to a shared memory. The shared memory may also be accessible to a different graph processing server. The graph processing server may compute an updated vertex value for a graph portion handled by the graph processing server and flush the updated vertex value to the shared memory, for retrieval by the different graph processing server. The graph processing server may also notify the different graph processing server indicating that the updated vertex value has been flushed to the shared memory.

Graph update flush to a shared memory

Memory system and method for memory protection and related non-transitory computer readable medium

http://hpl.hp.com/research/papers/2002/pico.pdf

Robert Schreiber

Papers

Data block write mapped to memory bank

Graph update flush to a shared memory

Memory system and method for memory protection and related non-transitory computer readable medium

PICO: Designing Computers

Volloptische schnelle verteilte Entscheidung in einer Computersystemvorrichtung