Precise hardware performance models play a crucial role in code optimizations. They can
assist compilers in making heuristic decisions or aid autotuners in identifying the optimal …

In this paper, we present WACO, a novel method of co-optimizing the format and the
schedule of a given sparsity pattern in a sparse tensor program. A core challenge in this …

Search-based tensor compilers can greatly accelerate the execution of machine learning
models by generating high-performance tensor programs, such as matrix multiplications and …

Search-based techniques have been demonstrated effective in solving complex optimization
problems that arise in domain-specific compilers for machine learning (ML). Unfortunately …

Software optimization refines programs for resource efficiency while preserving functionality.
Traditionally, it is a process done by developers and compilers. This paper introduces a third …

Automatic optimization for tensor programs becomes increasingly important as we deploy
deep learning in various environments, and efficient optimization relies on a rich search …

Tensor program tuning is a non-convex objective optimization problem, to which search-
based approaches have proven to be effective. At the core of the search-based approaches …

Auto-tuning DL compilers are gaining ground as an optimizing back-end for DL frameworks.
While existing work can generate deep learning models that exceed the performance of …

Polyhedral techniques have been widely used for automatic code optimization in low-level
compilers and higher-level processes. Loop optimization is central to this technique, and …

Auto-scheduling for tensor programs is a process where a search algorithm automatically
explores candidate schedules (program transformations) for a given program on a target …