SIP: Autotuning GPU Native Schedules via Stochastic Instruction Perturbation

no code implementations • 25 Mar 2024 • Guoliang He, Eiko Yoneki

In this work, we explore the possibility of GPU native instruction optimization to further push the CUDA kernels to extreme performance.

X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation

1 code implementation • 28 Apr 2023 • Guoliang He, Sean Parker, Eiko Yoneki

Tensor graph superoptimisation systems perform a sequence of subgraph substitution to neural networks, to find the optimal computation graph structure.

Decision Making reinforcement-learning +1

MCTS-GEB: Monte Carlo Tree Search is a Good E-graph Builder

1 code implementation • 8 Mar 2023 • Guoliang He, Zak Singh, Eiko Yoneki

Rewrite systems [6, 10, 12] have been widely employing equality saturation [9], which is an optimisation methodology that uses a saturated e-graph to represent all possible sequences of rewrite simultaneously, and then extracts the optimal one.

graph construction Reinforcement Learning (RL)