6 papers with code • 0 benchmarks • 0 datasets
Machine learning guided compiler optimization
These leaderboards are used to track progress in Compiler Optimization
Most implemented papers
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose.
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research
What is needed is an easy, reusable experimental infrastructure for real world compiler optimization tasks that can serve as a common benchmark for comparing techniques, and as a platform to accelerate progress in the field.
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution
We propose CPrune, a compiler-informed model pruning for efficient target-aware DNN execution to support an application with a required target accuracy.
Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching
Thus, we propose Ginex, the first SSD-based GNN training system that can process billion-scale graph datasets on a single machine.
Compiler Optimization for Quantum Computing Using Reinforcement Learning
Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer.
Robust Scheduling with GFlowNets
Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization.