Hands-on Build Guide: Tiled Self-Attention Kernel with Online Softmax in Pure NumPy
A step‑by‑step NumPy implementation of tiled soft‑max attention, mirroring FlashAttention’s efficiency, perfect for a CV‑boosting side project.
A step‑by‑step NumPy implementation of tiled soft‑max attention, mirroring FlashAttention’s efficiency, perfect for a CV‑boosting side project.
A step‑by‑step guide to creating a speculative decoding prototype in pure Python, showcasing algorithmic design, performance tuning, and production‑ready patterns.
This post details the end-to-end optimization of CUDA Graphs for sparse matrix multiplication across GPU clusters, covering architecture, implementation patterns, and performance benchmarks.
Learn to implement a production‑ready INT8 quantizer with per‑channel scaling, test it on transformer weights, and extend it for real‑world deployment.
Learn how to harness eBPF programs to safely instrument the kernel, collect metrics, and build low‑overhead tracing pipelines.