Explore machine learning concepts related to optimization. Clear explanations and practical insights.
Complete guide to CUDA MPS — architecture, performance benchmarks vs time-slicing and MIG, thread percentage planning, production deployment with systemd and Kubernetes, profiling with nsys, and troubleshooting.
Mastering HPC performance — Amdahl's Law, Gustafson's Law, strong vs weak scaling, roofline model, communication-computation overlap, load balancing, and profiling with Nsight and VTune.
Python performance optimization guide: CPython peephole optimizer, lru_cache, profiling with cProfile, and Python 3.11+ adaptive bytecode specialization.
Master Python __slots__ for 40-50% memory reduction and faster attribute access. Learn CPython descriptor protocol, inheritance patterns, and best practices.
Explore CPU pipeline stages, instruction-level parallelism, pipeline hazards, and branch prediction through interactive visualizations.
Master pipeline hazards through interactive visualizations of data dependencies, control hazards, structural conflicts, and advanced detection mechanisms.
Complete C++ thread safety guide — race conditions with step-through simulation, mutexes, atomics, condition variables, deadlock detection, memory ordering, and Thread Sanitizer walkthrough.
Understand dilated (atrous) convolutions: how dilation rates expand receptive fields exponentially without extra parameters and how to avoid gridding artifacts.
Understand cross-entropy loss for classification: interactive demos of binary and multi-class CE, the -log(p) curve, softmax gradients, and focal loss.
Master virtual memory and TLB address translation with interactive demos. Learn page tables, page faults, and memory management optimization.
Master sequential vs strided memory access patterns. Learn how cache efficiency and hardware prefetching affect application performance.
Explore how hierarchical attention enables Vision Transformers (ViT) to process sequential data by encoding relative positions.
Learn how Transparent Huge Pages (THP) reduces TLB misses by promoting 4KB to 2MB pages. Understand performance benefits and memory bloat tradeoffs.
Compare Multi-Head, Grouped-Query, and Multi-Query Attention mechanisms to understand their trade-offs and choose the optimal approach for your use case.
Learn how Grouped-Query Attention (GQA) balances Multi-Head quality with Multi-Query efficiency for faster LLM inference.
Explore linear complexity attention mechanisms including Performer, Linformer, and other efficient transformers that scale to very long sequences.
Learn Multi-Query Attention (MQA), the optimization that shares keys and values across attention heads for massive memory savings.
Sliding Window Attention for long sequences: local context windows enable O(n) complexity, used in Mistral and Longformer models.
Explore sparse attention mechanisms that reduce quadratic complexity to linear or sub-quadratic, enabling efficient processing of long sequences.
Master Structure of Arrays (SoA) vs Array of Structures (AoS) data layouts for optimal cache efficiency, SIMD vectorization, and GPU memory coalescing.
Interactive guide to MSE vs MAE for regression: explore outlier sensitivity, gradient behavior, and Huber loss with visualizations.
Eliminating GPU initialization latency through nvidia-persistenced - a userspace daemon that maintains GPU driver state for optimal startup performance.
Master vector compression techniques from scalar to product quantization. Learn how to reduce memory usage by 10-100× while preserving search quality.
Learn adaptive tiling in vision transformers: dynamically partition images based on visual complexity to reduce token counts while preserving detail.
Master prompt engineering for large language models: from basic composition to Chain-of-Thought, few-shot, and advanced techniques.
Explore neural scaling laws in deep learning: power law relationships between model size, data, and compute that predict AI performance.
Learn visual complexity analysis in deep learning - how neural networks measure entropy, edges, and saliency for adaptive image processing.
Embedding quantization simulator: explore memory-accuracy trade-offs from float32 to int8 and binary representations for retrieval.
Interactive Flash Attention visualization - the IO-aware algorithm achieving memory-efficient exact attention through tiling and kernel fusion.
Interactive KV cache visualization - how key-value caching in LLM transformers enables fast text generation without quadratic recomputation.
Discover how multimodal vision-language models like CLIP, ALIGN, and LLaVA scale with data, parameters, and compute following Chinchilla-style power laws.
Explore modern C++ features including auto, lambdas, ranges, and coroutines. Learn how C++11/14/17/20 transformed the language.
C++ compiler optimization deep dive — optimization levels compared with assembly output, auto-vectorization, LTO, PGO, compiler flags reference, and dangerous flags explained.
Learn how gradients propagate through deep neural networks during backpropagation. Understand vanishing and exploding gradient problems.
Deep dive into PyTorch DataLoader num_workers parameter: how parallel workers prefetch data, optimal configuration, and common pitfalls.
Understand the NAdam optimizer that fuses Adam adaptive learning rates with Nesterov look-ahead momentum for faster, smoother convergence in deep learning.
Understand internal covariate shift: why layer input distributions change during training, how it slows convergence, and how batch norm fixes it.
CPU performance optimization: memory hierarchy, cache blocking, SIMD vectorization, and profiling tools for modern processors.