Modern GPU Matmul Optimization

2026-06-02 · Source: AI Advances - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, medium

Summary

This article details modern GPU matrix multiplication (matmul) optimization techniques, demonstrating how to achieve cuBLAS-equivalent or superior performance. Using the deplodock tool, it explains optimizations like register tiling, which yields a 5.2x speed-up, and shared-memory staging. It covers advanced data movement strategies including double-buffering, asynchronous copies (cp.async), and Tensor Memory Accelerator (TMA) on sm_90+ GPUs. The analysis, performed on an NVIDIA RTX 5090 with 2048x2048 fp32 and fp16 matmuls, shows that a custom fp16 tensor-core implementation, particularly with warp specialization, can surpass cuBLAS by 105%. Other techniques like software pipelining, bank conflict resolution via padding (3.7x win for cp.async), CTA swizzle (5% win for L2-bound 8192³ kernels), and Split-K (7.1x win for skinny 128x128x16384 matrices) are also explored.

Key takeaway

For AI Engineers optimizing deep learning inference or training kernels, this analysis highlights that custom GPU matmul implementations can outperform standard libraries like cuBLAS, especially for fp16 operations on Blackwell GPUs. You should investigate deplodock to apply techniques like warp specialization and TMA, which are crucial for maximizing performance on modern hardware. Consider tailoring optimizations such as Split-K for specific matrix shapes to fully utilize GPU resources and achieve significant speed-ups.

Key insights

Modern GPU matmul optimization requires deep hardware understanding to surpass library performance.

Principles

• Maximize arithmetic intensity by reusing loaded data.
• Overlap data movement with computation to hide latency.
• Specialize GPU resources (e.g., warps) for specific tasks.

Method

Optimize torch.matmul to CUDA using deplodock by applying a series of passes: register tiling, shared-memory staging, double-buffering, async-copy, TMA, software pipelining, and warp specialization.

In practice

• Use deplodock to inspect and apply matmul optimization passes.
• Consider tensor cores for fp16/lower precision matmuls on modern GPUs.
• Implement Split-K for matmuls with small M, N and large K.

Topics

• GPU Optimization
• Matrix Multiplication
• CUDA Kernels
• Tensor Cores
• TMA (Tensor Memory Accelerator)
• Warp Specialization
• Deplodock

Code references

• cloudrift-ai/deplodock
• NVIDIA/cutlass
• JuliaGPU/CUDA.jl

Best for: Machine Learning Engineer, AI Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Advances - Medium.