Sparse matrix-matrix multiplication (SpMM) is a crucial kernel in various applications, including sparse deep neural networks [1]–[6], graph analytics [7], triangle counting [8], and linear algebra ...

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Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most challenging tasks in numerical ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Implementations of matrix multiplication via diffusion and reactions, thus eliminating ...

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

In this assignment, you'll be investigating the performance impacts of different cache architectures and different algorithm designs on matrix multiplication. The goals of this assignment are: Show ...

A new technology for autonomously operating aircraft dubbed "MATRIX" is drawing scrutiny across social media after a commercial airliner collided with a military helicopter in Washington, D.C. on ...

The Purdue Office of Undergraduate Research (OUR) is launching an undergraduate research program matrix that captures the scale of research programming at West Lafayette and Indianapolis. The Purdue ...

Provide Purdue faculty, staff, and students with a single source summary of URE programs. Give UR programs, administrators, and mentors broader and inclusive marketing to prospective student ...

Since homomorphic encryption enables SIMD operations by packing multiple values into a vector of operations and enabling pairwise addition or multiplication operations, one (old) conventional method ...