About this question
Medium · low_latency · Quant Developer interview question · cache-oblivious, matrix-transpose, recursion, low_latency, correlation, cache
Efficient matrix transposition is critical for high-frequency operations on correlation matrices in quantitative risk systems. A naive transpose suffers from poor cache locality, leading to significant performance degradation. The cache-oblivious recursive algorithm resolves this by recursively dividing the matrix, ensuring subproblems fit into cache and achieving optimal performance without hardware-specific tuning. Task Implement the recursive helper function transpose_rec to perform an out-o
Keep practicing