Matrix Organizations: How Leaders Can Be More Effective

Matrix structures don’t work on their own. The work is less about control and more about integration, often without formal ...
The Next Platform

Three HPC Gurus Ask: Do We Still Need GPUs?

Yes, that simple question is, in the modern Nvidia world that has come to dominate AI training and to a certain extent HPC simulation and modeling, heretical. But given that CPUs are in many cases ...
IEEE

pSyncPIM: Partially Synchronous Execution of Sparse Matrix Operations for All-Bank PIM Architectures

Abstract: Recent commercial incarnations of processing-in-memory (PIM) maintain the standard DRAM interface and employ the all-bank mode execution to maximize bank-level memory bandwidth. Such a ...
GitHub

Kokkos C++ Performance Portability Programming EcoSystem: Math Kernels - Provides BLAS, Sparse BLAS and Graph Kernels

KokkosKernels implements local computational kernels for linear algebra and graph operations, using the Kokkos shared-memory parallel programming model. "Local" means not using MPI, or running within ...
IEEE

Toward Greener Matrix Operations by Lossless Compressed Formats

Abstract: Sparse matrix-vector multiplication (SpMV) is a fundamental operation in machine learning, scientific computing, and graph algorithms. In this paper, we investigate the space, time, and ...
C&EN

Sparse Robust Principal Component Analysis with Applications to Fault Detection and Diagnosis

Principal component analysis (PCA) is a popular method for modeling and analysis of high-dimensional data. In spite of its advantages, classical PCA also has two drawbacks. First, it is very sensitive ...
Nature

Sparse coding with memristor networks

The original input, such as an image, is fed to the rows of the memristor crossbar, and the columns of the crossbar are connected to output neurons. The memristor network performs critical pattern ...