Year: 2023
Type: conference-paper
Abstract: We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the flexibility needed to handle the requirements of modern autotuning tasks. Particularly, it deals with permutation, ordered, and continuous parameter types along with both known and unknown parameter constraints. To reason about these parameter types and efficiently deliver high-quality code, BaCO uses Bayesian optimization algorithms specialized towards the autotuning domain. We demonstrate BaCO's effectiveness on three modern compiler systems: TACO, RISE & ELEVATE, and HPVM2FPGA for CPUs, GPUs, and FPGAs respectively. For these domains, BaCO outperforms current state-of-the-art auto-tuners by delivering on average 1.36X--1.56X faster code with a tiny search budget, and BaCO is able to reach expert-level performance 2.9X--3.9X faster.
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G1441096865, SHF: Medium: Collaborative Research: From Volume to Velocity: Big Data Analytics in Near-Realtime, G3321450376, G4572525476, G712881263, RTML: Large: Continuous Adaptation for Decision Streams, CAREER: A Unified Compiler for Sparse Array Operations and Relational Algebra, SHF: Medium: PRISM: Platform for Rapid Investigation of efficient Scientific-computing & Machine-learning, G796431723, PPoSS: Planning: Eliminating the Bottlenecks to ML Usability and Scalability, G893917011
OpenAlex ID: W4391623948