Ash Vardanian, founder of Unum, explores the potential of modern CPUs to perform super-scalar operations using single instruction, multiple data (SIMD) parallel processing, often underutilized due to the complexities of writing parallel operations. Discussing insights gained from years of implementing SIMD kernels in the SimSIMD library, which powers vector math in various Database Management Systems and AI companies, Vardanian highlights challenges such as unpredictable performance, complex debugging, and computation precision inconsistencies across different CPUs and instruction sets. The post delves into the widespread use of cosine similarity in machine learning, providing detailed implementations in multiple programming languages and architectures, emphasizing the importance of leveraging CPU-specific optimizations for significant performance improvements. Vardanian illustrates how these optimizations can transform simple algorithms from being inefficient to achieving remarkable speeds, underscoring the necessity of specialized hardware acceleration and dynamic dispatch to accommodate various CPU capabilities. The discussion concludes with an acknowledgment of the complexities involved in SIMD programming and a promise to address these challenges further in a subsequent series, focusing on how the programming language Mojo can offer solutions.