tilelang-cuda-memory

What problem does it solve?

GPU memory access is a key bottleneck in high-performance TileLang CUDA kernels. This guide provides memory-access optimization strategies to maximize throughput and minimize latency by leveraging shared memory, registers, and caching tricks.

Core Features & Use Cases

• Memory hierarchy aware optimizations: tactics for shared memory tiling, register fragments, local storage, and L2/swizzle-based cache tuning.
• Coalesced data transfers: using T.copy and tiled layouts to improve bandwidth efficiency in GEMM-like workloads.
• Use Case: accelerate a memory-bound kernel such as matrix multiplication by reusing data in fast memory levels and reducing global memory traffic.

Quick Start

Run the TileLang CUDA memory-optimized kernel example to verify improved bandwidth and reduced latency.