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Automatic code optimization is a complex process that typically involves theapplication of multiple discrete algorithms that modify the program structureirreversibly. However, the design of these algorithms is often monolithic, andthey require repetitive implementation to perform similar analyses due to thelack of cooperation. To address this issue, modern optimization techniques,such as equality saturation, allow for exhaustive term rewriting at variouslevels of inputs, thereby simplifying compiler design.  In this paper, we propose equality saturation to optimize sequential codesutilized in directive-based programming for GPUs. Our approach simultaneouslyrealizes less computation, less memory access, and high memory throughput. Ourfully-automated framework constructs single-assignment forms from inputs to beentirely rewritten while keeping dependencies and extracts optimal cases.Through practical benchmarks, we demonstrate a significant performanceimprovement on several compilers. Furthermore, we highlight the advantages ofcomputational reordering and emphasize the significance of memory-access orderfor modern GPUs.

ACC Saturator: Automatic Kernel Optimization for Directive-Based GPU Code