37 citations · 63 across the 11 of their papers we have counts for
11 papers
A Cross-Platform Benchmark for Interval Computation Libraries
Xuan Tang, Zachary Ferguson, Teseo Schneider +3
Interval computation is widely used to certify computations that use floating point operations to avoid pitfalls related to rounding error introduced by inaccurate operations. Desp…
Sparsity-Specific Code Optimization using Expression Trees
Philipp Herholz, Xuan Tang, Teseo Schneider +3
We introduce a code generator that converts unoptimized C++ code operating on sparse data into vectorized and parallel CPU or GPU kernels. Our approach unrolls the computation into…
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…
Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers
Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane +4
High-performance DSL developers work hard to take advantage of modern hardware. The DSL compilers have to build their own complex middle-ends before they can target a common back-e…
Sparse Tensor Algebra Optimizations with Workspaces
Fredrik Kjolstad, Willow Ahrens, Shoaib Kamil +1
This paper shows how to optimize sparse tensor algebraic expressions by introducing temporary tensors, called workspaces, into the resulting loop nests. We develop a new intermedia…
Sympiler: Transforming Sparse Matrix Codes by Decoupling Symbolic Analysis
Kazem Cheshmi, Shoaib Kamil, Michelle Mills Strout +1
Sympiler is a domain-specific code generator that optimizes sparse matrix computations by decoupling the symbolic analysis phase from the numerical manipulation stage in sparse cod…