The author reflects on the ease of learning CUDA once they discovered it’s essentially C++ with some additional features. However, coming in with C++ habits could lead to suboptimal code, as lessons in memory coalescing reveal. The majority of performance in a modern PC lies in specialized hardware like GPUs, with specialized chips for machine learning and raytracing. Understanding different types of memory in CUDA is essential, with shared memory being faster than normal memory. The piece highlights the importance of parallelism and optimizing code for massive parallelism. Overall, writing CUDA code feels like a puzzle that requires maximizing GPU usage for high-speed computations, akin to managing numerous fast container ships for efficient cargo transportation.
https://probablydance.com/2024/10/07/initial-cuda-performance-lessons/