Memory-Efficient Attention Kernels

• Tiled computation: Breaks matrices into cache-friendly tiles, reducing memory traffic.
• Flash-style accumulation: Streams keys and values while maintaining softmax denominators in high precision.
• Kernel fusion: Combines operations (attention, bias, dropout) into single kernels to limit global memory reads.
• Dynamic memory allocation: Allocates just enough shared memory per sequence, enabling longer contexts without O(n²) blowups.

Understanding these building blocks helps evaluate marketing claims and adapt kernels to your workloads.