Compiler Optimization
9 papers with code • 0 benchmarks • 0 datasets
Machine learning guided compiler optimization
Benchmarks
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
alibaba/EasyNLP
•
•
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.
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
saleml/gfn
•
•
Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization.
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research
facebookresearch/CompilerGym
•
•
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
snu-arc/ginex
•
•
Thus, we propose Ginex, the first SSD-based GNN training system that can process billion-scale graph datasets on a single machine.
SimCLF: A Simple Contrastive Learning Framework for Function-level Binary Embeddings
A practical embedding learning framework relies on the robustness of the assembly code representation and the accuracy of function-pair annotation, which is traditionally accomplished using supervised learning-based frameworks.
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs.
White-box Compiler Fuzzing Empowered by Large Language Models
Nonetheless, prompting LLMs with compiler source-code information remains a missing piece of research in compiler testing.
