Publications (8)
ComPile: A Large IR Dataset from Production Sources
Aiden Grossman, Ludger Paehler, Konstantinos Parasyris +6
Code is increasingly becoming a core data modality of modern machine learning research impacting not only the way we write code with conversational agents like OpenAI's ChatGPT, Go…
Understanding Automatic Differentiation Pitfalls
Jan Hückelheim, Harshitha Menon, William Moses +3
Automatic differentiation, also known as backpropagation, AD, autodiff, or algorithmic differentiation, is a popular technique for computing derivatives of computer programs accura…
Performance Portable Solid Mechanics via Matrix-Free -Multigrid
Jed Brown, Valeria Barra, Natalie Beams +7
Finite element analysis of solid mechanics is a foundational tool of modern engineering, with low-order finite element methods and assembled sparse matrices representing the indust…
ProTuner: Tuning Programs with Monte Carlo Tree Search
Ameer Haj-Ali, Hasan Genc, Qijing Huang +4
We explore applying the Monte Carlo Tree Search (MCTS) algorithm in a notoriously difficult task: tuning programs for high-performance deep learning and image processing. We build…
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning
Ameer Haj-Ali, Qijing Huang, William Moses +4
The performance of the code generated by a compiler depends on the order in which the optimization passes are applied. In high-level synthesis, the quality of the generated circuit…
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
Qijing Huang, Ameer Haj-Ali, William Moses +4
The performance of the code a compiler generates depends on the order in which it applies the optimization passes. Choosing a good order--often referred to as the phase-ordering pr…