## Meet your strawman book

Comparison Table¶. Here is a list of NumPy / SciPy APIs and its corresponding CuPy implementations.-in CuPy column denotes that CuPy implementation is not provided yet.We welcome contributions for these functions.

upon the insight that the matrix multiplication generally breaks the large matrix into multiple smaller tiles for parallel execution. We propose a tiling-friendly “tile-wise” sparsity pattern, which maintains a regular pattern at the tile level for efﬁcient execution but allows for irregular, arbitrary pruning at the global scale

Aug 14, 2019 · Note that "emulation mode" has been removed as of CUDA Toolkit Version 3.1. CUDA model Host. A host contains zero or more CUDA-capable devices (emulation must be used if zero devices are available). It can run multiple CUDA processes, each composed of one or more host threads. A given host thread can execute code on only one device at once.

Feb 12, 2012 · Matrix multiplication. The next ingredient we need is matrix multiplication. If A and B are a and a matrix, respectively, their product C=AB is a matrix – note the middle dimension has to match between the two. The element in row i and column j of matrix C is computed as the dot product of the i-th row of A and the j-th column of B, or in ...

CUDA Matrix Multiplication with Shared Memory. GitHub Gist: instantly share code, notes, and snippets.

This repository contains the code and scripts for verifying the claims in the paper "Design Principles for Sparse Matrix Multiplication on the GPU" accepted to the International European Conference on Parallel and Distributed Computing (Euro-Par) 2018. The related study involves the implementation of novel algorithms for sparse-matrix dense matrix multiplication (SpMM) on the GPU, considering ...