Howie Liu

Howie Liu

Home Institution
The University of Illinois at Urbana-Champaign

Year Participated
2022

Year in School
Undergraduate

REU Faculty Mentor
Edger Solomonik

Research Area Interest
Architecture, Compilers, and Parallel Computing

Project Title
Faster Sparse MTTKRP on Scalable Massively Parallel Architectures

Biography & Research Abstract

Abstract:

Tensor decomposition is one of the most fundamental strategies that could extract information from a sparse tensor. One of the most well-known tensor decomposition methods is the canonical polyadic decomposition (CPD). The matricized tensor times Khatri-Rao product (MTTKRP) operation takes most of the time and computing resources when computing CPD. Atomic operations on GPUs, which are heavily relied by many state-of-the-art MTTKRP kernels, are extremely expensive. We proposed a kernel that minimizes the number of atomic operations while maintaining parallelism, and our kernel can also be applied to a scalable architecture.

Bio:

Hanwen Liu is a rising senior at University of Illinois Urbana-Champaign, where he is completing his major in computer engineering and his minor in mathematics. His interest lies in the area of computer microarchitecture and high-performance computing. Specifically, he keeps implementing and exploring applications of sparse tenser-tensor multiplication on passively parallel architectures. He has served as teaching assistant for several courses. His eventual career goal is to pass on what he has learned with others, as a professor.