37 citations · 45 across the 5 of their papers we have counts for
8 papers
ILA: Compilable Markdown for Linear Algebra
Yong Li, Shoaib Kamil, Alec Jacobson +1
Communicating linear algebra in written form is challenging: mathematicians must choose between writing in languages that produce well-formatted but semantically-underdefined repre…
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…
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…
Optimizing Ordered Graph Algorithms with GraphIt
Yunming Zhang, Ajay Brahmakshatriya, Xinyi Chen +4
Many graph problems can be solved using ordered parallel graph algorithms that achieve significant speedup over their unordered counterparts by reducing redundant work. This paper…
GraphIt: A High-Performance DSL for Graph Analytics
Yunming Zhang, Mengjiao Yang, Riyadh Baghdadi +3
The performance bottlenecks of graph applications depend not only on the algorithm and the underlying hardware, but also on the size and structure of the input graph. Programmers m…
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane +6
This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. Tiram…