Instruction Scheduling for the GPU on the GPU

In this paper, we show how to use the GPU to parallelize a precise instruction scheduling algorithm that is based on Ant Colony Optimization (ACO). ACO is a nature-inspired intelligent-search technique that has been used to compute precise solutions to NP-hard problems in operations research (OR). S...

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Bibliographic Details
Published in:Proceedings / International Symposium on Code Generation and Optimization pp. 435 - 447
Main Authors: Shobaki, Ghassan, Muyan-Ozcelik, Pinar, Hutton, Josh, Linck, Bruce, Malyshenko, Vladislav, Kerbow, Austin, Ramirez-Ortega, Ronaldo, Gordon, Vahl Scott
Format: Conference Proceeding
Language:English
Published: IEEE 02.03.2024
Subjects:
Ant Colony Optimization (ACO)
Benchmark testing
GPU computing
Graphics processing units
instruction scheduling
multi-objective optimization
NP-hard problem
Operations research
Optimization
parallel compiler optimization
Production
Scheduling algorithms
ISSN:2643-2838
Online Access:Get full text
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Summary:In this paper, we show how to use the GPU to parallelize a precise instruction scheduling algorithm that is based on Ant Colony Optimization (ACO). ACO is a nature-inspired intelligent-search technique that has been used to compute precise solutions to NP-hard problems in operations research (OR). Such intelligent-search techniques were not used in the past to solve NP-hard compiler optimization problems, because they require substantially more computation than the heuristic techniques used in production compilers. In this work, we show that parallelizing such a compute-intensive technique on the GPU makes using it in compilation reasonably practical. The register-pressure-aware instruction scheduling problem addressed in this work is a multi-objective optimization problem that is significantly more complex than the problems that were previously solved using parallel ACO on the GPU. We describe a number of techniques that we have developed to efficiently parallelize an ACO algorithm for solving this multi-objective optimization problem on the GPU. The target processor is also a GPU. Our experimental evaluation shows that parallel ACO-based scheduling on the GPU runs up to 27 times faster than sequential ACO-based scheduling on the CPU, and this leads to reducing the total compile time of the rocPRIM benchmarks by 21%. ACO-based scheduling improves the execution-speed of the compiled benchmarks by up to 74% relative to AMD's production scheduler. To the best of our knowledge, our work is the first successful attempt to parallelize a compiler optimization algorithm on the GPU.
ISSN:2643-2838
DOI:10.1109/CGO57630.2024.10444869