triton-cuda-memory

What problem does it solve?

CUDA and Triton-CUDA kernels often struggle with memory bandwidth bottlenecks and non-coalesced accesses, leading to wasted cycles and reduced throughput. This guide provides strategies to optimize memory traffic, improve data layout, and lower latency for GPU kernels in memory-bound workloads.

Core Features & Use Cases

• Shared memory utilization to minimize global memory traffic and improve data reuse.
• Coalesced access patterns to maximize memory throughput across warps.
• Advanced layout techniques (grouped ordering, swizzle, and tl.make_block_ptr usage) to boost L2 cache efficiency.
• Use Case: accelerate matrix multiplications, attention-like operations, and large tensor workloads by applying the described memory access optimizations.

Quick Start

Evaluate a target kernel and apply shared memory, coalesced loads, and data layout optimizations to improve throughput.