CUDA Shared Memory Swizzling: Eliminating Bank Conflicts

Matrix transposition is a common operation in GPU computing, but naive implementations suffer from shared memory bank conflicts, causing significant performance degradation. This article explores two techniques to eliminate these conflicts: padding and swizzling. It provides CUDA kernel implementations for both approaches, along with performance benchmarks on an 8192x8192 matrix. The results show that both padding and swizzling effectively remove bank conflicts, with swizzling offering a more memory-efficient alternative.