An RL agent with a graph neural network learns loop nest optimizations for the Tiramisu compiler and generalizes to unseen benchmarks, reporting 2.02x and 3.36x geometric mean speedups over Tiramisu and Pluto.
ProTuner: Tuning Programs with Monte Carlo Tree Search
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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 our framework on top of Halide and show that MCTS can outperform the state-of-the-art beam-search algorithm. Unlike beam search, which is guided by greedy intermediate performance comparisons between partial and less meaningful schedules, MCTS compares complete schedules and looks ahead before making any intermediate scheduling decision. We further explore modifications to the standard MCTS algorithm as well as combining real execution time measurements with the cost model. Our results show that MCTS can outperform beam search on a suite of 16 real benchmarks.
fields
cs.PL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Pearl: Automatic Code Optimization Using Deep Reinforcement Learning
An RL agent with a graph neural network learns loop nest optimizations for the Tiramisu compiler and generalizes to unseen benchmarks, reporting 2.02x and 3.36x geometric mean speedups over Tiramisu and Pluto.