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ZYFA kernels boost FP8 FlashAttention speed and accuracy on AMD Instinct MI355X

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ZYFA, a new set of prefill kernels for the AMD Instinct MI355X accelerator, designed to improve the speed and accuracy of FlashAttention. ZYFA employs three key techniques: direct computation of FP8 probabilities, a ping-pong scheduling for overlapping softmax and matrix multiplications, and a scaling method for normalization. These optimizations result in a 1.16-1.76x throughput increase over existing AITER kernels and significantly reduce numerical error, bringing it closer to that of Composable Kernel and Triton. AI

IMPACT Improves efficiency of core attention mechanisms, potentially accelerating training and inference for large language models on specific hardware.

RANK_REASON Academic paper detailing novel optimization techniques for hardware acceleration of a core AI model component. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ZYFA kernels boost FP8 FlashAttention speed and accuracy on AMD Instinct MI355X

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Academic paper detailing novel optimization techniques for hardware acceleration of a core AI model component. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Quentin Anthony, Ganesh Bikshandi, Praneeth Medepalli, Stephen Ebert ·

    ZYFA: Speed and Accuracy of FP8 FlashAttention on AMD CDNA4

    arXiv:2611.07496v1 Announce Type: cross Abstract: Attention is a core building block of frontier models and often dominates compute-bound prefill at long context. FP8 matrix instructions offer twice the peak throughput of BF16, but they accelerate only matrix multiplication, and …

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