Matrix multiplication is a fundamental operation in scientific computing, game development, AI, and numerous high-performance applications. While its mathematical definition is simple, achieving optimal performance in C++ is far from trivial.
In this talk, we will explore different optimization techniques for matrix multiplication, from naive implementations to highly tuned versions leveraging modern hardware features. We will cover key performance-enhancing strategies such as loop unrolling, cache blocking, SIMD vectorization, parallelization using threads and more. Through benchmarking and profiling, we will measure the real impact of these optimizations.
By the end of this session, attendees will gain insights into two critical questions:
How hard is it to implement an optimized matrix multiplication in C++? How effective is C++ for achieving peak performance in this task?
This talk is suitable for developers interested in performance optimization, computational efficiency, and modern C++ techniques for numerical computing.
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I am an experienced C++ developer with over 10 years of expertise in modern C++, working across embedded systems, automotive, and cloud domains. Currently, my focus is on AI and high-performance computing (HPC). I am actively engaged in the C++ community, having volunteered at CppCon 2024, and I am eager to take the next step by speaking at the conference this year.