Carl Pearson is a Postdoctoral Appointee at Sandia National Labs and a Research Assistant Professor (LAT) of Computer Science at University of New Mexico. He works on GPU communication for distributed linear algebra, and GPU acceleration of sparse matrix multiplication.
He recieved his Ph.D in Electrical and Computer Engineering from the University of Illinois in 2021, and his B.S. in Engineering from Harvey Mudd College.
One of the resources I use to evaluate sparse-matrix vector multiplication (SpMV) performance at Sandia National Labs is the SuiteSparse Collection.
Here are some interesting things I read this month. Presented without comment or endorsement.
Setting up a python development environment is annoying. We’d especially like to avoid:
What to do when Disk Utility won’t
I MC’ed the Center for Cognitive Computing and Systems Research anniversary celebration.
GPLv3 single-header C++11 Matrix Market Reader
Accumulated tips for formatting technical writing in Latex
Set up PhotoPrism on Google Cloud with docker-compose and a LetsEncrypt HTTPS certificate
Improving CUDA-Aware MPI_Pack speed by 300,000x. Code available.
I was invited to give a guest lecture for the Spring 2020 ECE 408 GPU programming course at the University of Illinois.