PulseAugur
EN
LIVE 11:41:36

MoonMath AI open-sources AMD MI300X attention kernel outperforming AITER v3 · 3 sources tracked

By PulseAugur Editorial · [3 sources] ·

MoonMath AI has released an open-source HIP attention kernel for AMD's MI300X GPU, which reportedly outperforms AMD's own AITER v3. The kernel achieves speedups of up to 1.26x by optimizing memory placement and using one-instruction assembly wrappers for register control. This optimization has been integrated into SGLang to accelerate video diffusion models like Wan2.1. AI

IMPACT Optimized GPU kernels can accelerate AI model training and inference, potentially leading to faster development cycles and more efficient deployment of AI applications.

RANK_REASON The cluster describes the open-sourcing of a specialized GPU kernel for AI computations, which is a research and development artifact rather than a full model release or product launch.

Read on Mastodon — sigmoid.social →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

MoonMath AI open-sources AMD MI300X attention kernel outperforming AITER v3 · 3 sources tracked

COVERAGE [3]

  1. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    MoonMath AI has open-sourced a HIP attention kernel for AMD MI300X that beats AMD's own AITER v3 on every shape and rounding mode. The bf16 forward attention ke

    MoonMath AI has open-sourced a HIP attention kernel for AMD MI300X that beats AMD's own AITER v3 on every shape and rounding mode. The bf16 forward attention kernel uses one-instruction asm wrappers and achieves up to 1.26x speedup. # AIagent # AI # GenAI # AIInfrastructure https…

  2. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    MoonMath AI has open-sourced a HIP attention kernel for AMD's MI300X GPU that beats AMD's own AITER v3 on every shape, with speedups up to 1.26x. The trick uses

    MoonMath AI has open-sourced a HIP attention kernel for AMD's MI300X GPU that beats AMD's own AITER v3 on every shape, with speedups up to 1.26x. The trick uses one-instruction asm wrappers for register control. K stays in LDS, V in L1, Q in registers. https://www. marktechpost.c…

  3. r/StableDiffusion TIER_2 English(EN) · /u/woct0rdho ·

    EvoTensile: Evolutionary algorithms for AMD Tensile GEMM kernel tuning

    <!-- SC_OFF --><div class="md"><p>There has been an effort to tune kernels in hipBLASLt so the most basic matmuls can run faster. It's known that on Strix Halo (gfx1151), GEMM with NN and TN input layouts (used in inference) are already well-tuned, while NT and TT layouts (used i…

RELATED ENTITIES

RELATED TOPICS