2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.AR2024
SIP: Autotuning GPU Native Schedules via Stochastic Instruction Perturbation
Guoliang He, Eiko Yoneki
Large language models (LLMs) have become a significant workload since their appearance. However, they are also computationally expensive as they have billions of parameters and are…
cs.LG2023★ 2 cited
X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation
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. Such a graph transformation…
cs.AI2023
MCTS-GEB: Monte Carlo Tree Search is a Good E-graph Builder
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 sequenc…