Learning Series

A series on LLM inference internals - the end-to-end inference flow, FlashAttention and PagedAttention, and agentic workflows with Mixture-of-Experts

1. LLM Inference Introduction: the end-to-end flow and vector math

A guide to CPU instruction-set features for ML workloads - numeric precision formats, the VNNI layout, and Intel AMX/TPP performance on Granite Rapids

1. Everything on Precision: Numeric Formats in Modern ML and HPC
2. VNNI Data Layout for Intel AMX Matrix Multiplication

A deep dive into CPU microarchitecture on Intel Granite Rapids - cache coherence and MESI, hardware/software prefetching, and IOMMU-based device isolation

1. Why Innocent Shared Updates Stop Scaling: Cache Coherence on a Multi-Socket Server

A complete guide on CXL (Compute Express Link) technology and its applications

1. CXL Introduction

Practical tools and workflows for profiling GPU utilization, memory usage, and performance bottlenecks

1. Reading torch.profiler Correctly: Schedules, Labels, and CPU vs GPU Time
2. GPU Profiling with Nsight Systems
3. Introduction to GPU Profiling
4. PyTorch Profiler vs Nsight Systems

A practical guide to Linux performance profiling - perf stat/record internals and adding custom /proc counters

1. Understanding Linux perf: stat and record
2. Adding a counter to the *proc* interface

A complete guide on different locking primitives used in Linux Kernel, C, and C++

1. Spin or Sleep: That is the question
2. Read-Copy-Update (RCU): A Primer