MLOS is a project to enable autotuning for systems.
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Updated
Sep 26, 2024 - Python
MLOS is a project to enable autotuning for systems.
Kernel Tuner
CK automation actions to let users implement portable, customizable and reusable program workflows for reproducible, collaborative and multi-objective benchmarking, optimization and SW/HW co-design:
📝 "End-to-end Deep Learning of Optimization Heuristics" (🥇 PACT'17 Best Paper)
ytopt: machine-learning-based search methods for autotuning
A pattern-based algorithmic autotuner for graph processing on GPUs.
📝 "Synthesizing Benchmarks for Predictive Modeling" (🥇 CGO'17 Best Paper)
BOAST aims at providing a framework to metaprogram, benchmark and validate computing kernels
NODAL is an Open Distributed Autotuning Library in Julia
A GPU benchmark suite for autotuners
Collection of executable benchmarks
Autotuning NVCC Compiler Parameters, published @ CCPE Journal
Pitch Track Hack - Pythonic Implementation of the Pitch track used in autotune patent
Autotuning Source Transformation Tools with Design of Experiments, published @ CCGRID'19
Autotuning High-Level Synthesis for FPGAs, published @ ReConFig '17
Auto-Tuning chain to optimize software execution and compilation time upon heterogeneous systems
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