Optimization Techniques for High-Performance computing on CPU Architectures

Abstract

This paper introduces a comprehensive methodology for optimizing Linux libraries to maximize performance on CPU architectures such as POWER. The proposed optimization pipeline encompasses compiler selection and configuration, runtime profiling, and manual vectorization. The methodology systematically addresses critical performance bottlenecks by applying architecture-specific compiler flags, managing dependencies strategically, and implementing targeted code-level optimizations. Proper compiler selection, use of optimized dependencies such as Open BLAS, and application of manual vectorization techniques are shown to yield performance improvements of 10-20 times over baseline implementations. Validation is provided through practical examples, including matrix multiplication libraries, which demonstrate measurable improvements in FLOPS and overall throughput. These findings offer actionable guidance for developers aiming to maximize CPU utilization in performance-critical Linux applications.