Overview
DeepGEMM provides efficient FP8 and low-precision GEMM kernels focused on balancing numerical stability and performance. Fine-grained scaling strategies and optimized GPU kernels help reduce numerical errors while leveraging low-precision speedups.
Key Features
- High-performance FP8 GEMM implementations optimized for deep learning training and inference.
- Fine-grained scaling strategies to improve numerical stability while maintaining performance.
- CUDA-based kernels designed for real-world GPU acceleration.
Use Cases
- Using low-precision matrix operations to save memory and increase throughput in model training and inference.
- Replacing default GEMM operators with custom kernels to optimize hotspot performance.
- Integrating as a low-precision computation library when trading off precision and speed.
Technical Details
- Fine-grained scaling to mitigate FP8 numerical bias.
- Optimized CUDA kernels and memory layouts for parallel efficiency on GPUs.
- Integration-friendly interfaces for PyTorch and other frameworks.