Problems of automatic code optimization by the compiler

Abstract

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Rational use of modern compiler capabilities, in particular automatic SIMD vectorization, enables significant improvements in computational performance for tasks involving dataarray processing and computer modeling of complex processes and systems.The growing demand for software performance in scientific computing, big-data analysis, artificial intelligence, and machine learning emphasizes the importance of exploiting hardware-level data parallelism.This study investigates the efficiency of automatic SIMD vectorization provided by the Microsoft Visual C++ compiler in comparison with manual optimization implemented through AVX2 instructions.To evaluate performance, three implementations were developed: a scalar baseline version, a compiler-optimized automatic SIMD code, and a manually vectorized SIMD version using intrinsic functions.Computational experiments were conducted using the SAXPY operation for arrays sized from 10 5 to 10 9 .The results demonstrated that automatic SIMD vectorization provides up to a 7.5x speedup with an efficiency of 0.94 for small-and medium-scale problems, effectively utilizing processor resources through aggressive optimizations such as loop unrolling and efficient use of FMA pipelines Manual SIMD optimization showed stable acceleration of up to 3. for large arrays but with lower efficiency (0.28-0.49due to memory-bandwidth limitations and less aggressive compiler-level transformations.The comparison revealed that automatic methods are more convenient for developers, significantly reducing the effort required for writing SIMD code, while manual optimizations remain relevant when scaling to large data volumes.The findings indicate that the optimal strategy is a combined use of automatic and manual SIMD transformations, allowing a balance between performance, accuracy, and development effort, thus ensuring both efficiency and scalability of software solutions in high-performance computing and computer modeling.Future research will focus on expanding the experimental base across various processor architectures, analyzing the interaction of SIMD vectorization with other compiler transformations, and applying MLbased methods for adaptive optimization-strategy selection.