12 citations · 25 across the 5 of their papers we have counts for
5 papers
Finch: Sparse and Structured Tensor Programming with Control Flow
Willow Ahrens, Teodoro Fields Collin, Radha Patel +3
From FORTRAN to NumPy, tensors have revolutionized how we express computation. However, tensors in these, and almost all prominent systems, can only handle dense rectilinear intege…
Compilation Techniques for Graph Algorithms on GPUs
Ajay Brahmakshatriya, Yunming Zhang, Changwan Hong +3
The performance of graph programs depends highly on the algorithm, the size and structure of the input graphs, as well as the features of the underlying hardware. No single set of…
Exploring the Design Space of Static and Incremental Graph Connectivity Algorithms on GPUs
Changwan Hong, Laxman Dhulipala, Julian Shun
Connected components and spanning forest are fundamental graph algorithms due to their use in many important applications, such as graph clustering and image segmentation. GPUs are…
ConnectIt: A Framework for Static and Incremental Parallel Graph Connectivity Algorithms
Laxman Dhulipala, Changwan Hong, Julian Shun
Connected components is a fundamental kernel in graph applications. The fastest existing parallel multicore algorithms for connectivity are based on some form of edge sampling and/…
A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra
Ryan Senanayake, Fredrik Kjolstad, Changwan Hong +2
We address the problem of optimizing mixed sparse and dense tensor algebra in a compiler. We show that standard loop transformations, such as strip-mining, tiling, collapsing, para…