Dissecting vLLM's PagedAttention: A Memory-Level Analysis
A deep technical analysis of PagedAttention's memory management, including KV cache fragmentation analysis, page table implementation details, and performance implications at the GPU memory hierarchy level.
Achieving Sub-10µA Sleep Current on ESP32: Register-Level Analysis
A systematic investigation into ESP32 power domains, RTC memory retention, and peripheral leakage. Includes register-level configurations and oscilloscope measurements proving sub-10µA deep sleep.
FlashAttention Through the Memory Hierarchy Lens
Analyzing FlashAttention's tiling strategy from an HBM bandwidth perspective. Includes roofline analysis, SRAM utilization measurements, and comparison with standard attention implementations.
Production LLM Profiling with eBPF: Beyond nvidia-smi
Using BPFtrace and custom eBPF programs to trace CUDA runtime behavior, understand GPU scheduling latencies, and diagnose inference performance issues that nvidia-smi can't reveal.
Habana Gaudi2 Memory Subsystem: Optimization Strategies for LLM Inference
Deep dive into Gaudi2's HBM architecture, SRAM hierarchy, and TPC memory access patterns. Practical optimization techniques for maximizing memory bandwidth utilization in transformer workloads.
Implementing Continuous Batching: From Scheduling Theory to vLLM Practice
A detailed analysis of continuous batching algorithms, including iteration-level scheduling, preemption strategies, and the interaction between scheduler and memory manager in production inference systems.
KV Cache Quantization: Trading Precision for Throughput
Comprehensive analysis of FP8, INT8, and INT4 KV cache quantization techniques. Includes calibration strategies, accuracy measurements, and practical implementation guidance for production inference.
CUDA Graphs for Inference: Eliminating CPU Launch Overhead
Deep dive into CUDA graph capture, replay, and the specific challenges of applying graphs to dynamic LLM inference workloads. Includes capture strategies and performance measurements.
Speculative Decoding: Trading Compute for Latency
Implementation details of speculative decoding, including draft model selection, acceptance rate optimization, tree-structured speculation, and when speculative decoding helps vs hurts.
Attention Variants Compared: MHA, MQA, GQA, and MLA
Technical comparison of Multi-Head Attention, Multi-Query Attention, Grouped-Query Attention, and Multi-head Latent Attention. Analysis of memory-compute trade-offs and implementation considerations.
ARM Cortex-M4 DSP Instructions: Practical Audio Processing
Leveraging SIMD instructions on Cortex-M4 for real-time audio processing. Includes cycle-accurate analysis, CMSIS-DSP usage, and hand-optimized assembly for FIR filters.
Grouped Query Attention: Memory-Throughput Trade-offs
Analysis of GQA's KV cache reduction mechanism, optimal group sizes, and performance implications for inference at scale. Includes benchmarks across different model sizes.
Tensor Parallelism Implementation: AllReduce Patterns and Efficiency
Detailed analysis of tensor parallelism for multi-GPU inference, including column/row splitting strategies, AllReduce optimization, and practical implementation considerations.
GPU Memory Profiling: Finding Leaks and Fragmentation
Practical techniques for diagnosing GPU memory issues using PyTorch memory profiling APIs, including allocation tracking, fragmentation analysis, and memory snapshot debugging.
I2C Bus Optimization: Achieving 1MHz on Noisy Lines
Practical techniques for reliable high-speed I2C communication, including rise time analysis, pull-up resistor calculations, and noise immunity improvements.
Writing Efficient CUDA Kernels: From Naive to Optimized
Step-by-step optimization of a CUDA kernel using memory coalescing, shared memory, occupancy tuning, and instruction-level parallelism.
Request Routing for LLM Inference: Load Balancing Strategies
Analysis of load balancing algorithms for multi-replica LLM serving, including least-connections, weighted routing, and queue-depth-aware strategies.
RoPE Embeddings: Implementation and Long Context Scaling
Understanding Rotary Position Embeddings, their efficient implementation, and techniques for extending context length including YaRN and Dynamic NTK scaling.