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Hand-written PTX kernels show significant speedups for INT8/INT4 GEMM on NVIDIA L4 GPUs

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the performance benefits of using hand-written PTX (Parallel Thread Execution) kernels for GEMM (General Matrix Multiply) operations on NVIDIA L4 GPUs, compared to the standard WMMA (Warp Matrix Multiply Accumulate) C++ API. The study found that while hand-written PTX offered no speedup for FP16 precision, it achieved significant performance gains for INT8 (1.4x-1.8x) and INT4 (2.9x-4.3x) precisions. These speedups are attributed to factors like reduced instruction counts, improved global-memory coalescing, and more efficient execution of quantized operations, with INT4 performance reaching up to 98.7x faster than FP16 at large problem sizes. AI

IMPACT Optimized kernels for lower precision could accelerate AI inference and training on specific hardware.

RANK_REASON The cluster contains an academic paper detailing performance optimizations for GPU kernels. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

Hand-written PTX kernels show significant speedups for INT8/INT4 GEMM on NVIDIA L4 GPUs

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The cluster contains an academic paper detailing performance optimizations for GPU kernels. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Matt J. Borowski, Blazej Osinski ·

    Hand-Written PTX Tensor-Core GEMM Kernels: A Multi-Precision Study on NVIDIA L4

    arXiv:2608.10103v1 Announce Type: cross Abstract: High-performance Tensor Core kernels rely on a low-level PTX pipeline built from asynchronous data movement with cp.async, warp-level matrix loads with ldmatrix, and matrix multiply-accumulate operations with mma.sync. However, mo…

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