Dong Zhou (dongz@cs.cmu.edu)
Mu Li (muli@cs.cmu.edu)
A large number of machine learning algorithms uses gradient descent
like numerical method to get the optimal solution, such as page rank,
linear classificiation, collaborative filtering. At its core, matrix
vector multiplication is the essential computation. In this project,
we focus on optimizing the single machine version of sparse
matrix-vector multiplication. We want to handle the case that the
spare matrix exceedes the capacity of the memory. Besides, we would
like to explore cache-friendly, NUMA-aware optimizations.