Explore machine learning concepts related to performance. Clear explanations and practical insights.
Deep dive into CPU cache lines — interactive cache simulator with configurable associativity and replacement policies, false sharing MESI protocol visualization, access pattern benchmarks, and optimization techniques.
Master GPU memory hierarchy from registers to global memory, understand coalescing patterns, bank conflicts, and optimization strategies for maximum performance
Complete guide to PyTorch pin_memory — how DMA transfers work, when pinning helps vs hurts, NUMA effects, profiling with torch.profiler, num_workers interaction, and debugging slow data loading.
Flynn's Classification explained — SISD, SIMD, MISD, MIMD with interactive architecture explorer, SIMD evolution from MMX to AMX, branch divergence visualization, and workload-architecture throughput comparison.
OpenMP parallel programming: fork-join model, scheduling, data races, false sharing, NUMA thread affinity, and GPU offloading.
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.
Deep dive into CPU pipeline architecture covering 5-stage RISC pipelines, data hazards, control hazards, superscalar execution, and out-of-order processing.
Master Linux mount options like noatime and async for performance tuning and security hardening. Interactive guide to fstab configuration.
Python performance optimization guide: CPython peephole optimizer, lru_cache, profiling with cProfile, and Python 3.11+ adaptive bytecode specialization.
Complete guide to Python concurrency — OS threads, green threads (asyncio), the GIL, event loop internals, Python 3.13 free-threading, and production patterns.
XFS filesystem internals: allocation groups, extent-based allocation, and delayed allocation for high-performance parallel I/O.
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.
Master sequential vs strided memory access patterns. Learn how cache efficiency and hardware prefetching affect application performance.
Discover how memory interleaving distributes addresses across banks for parallel access. Boost memory bandwidth in DDR5 and GPU systems.
Explore NUMA architecture and memory locality in multi-socket systems. Understand local vs remote memory access latency and optimization strategies.
Learn how Transparent Huge Pages (THP) reduces TLB misses by promoting 4KB to 2MB pages. Understand performance benefits and memory bloat tradeoffs.
Master Structure of Arrays (SoA) vs Array of Structures (AoS) data layouts for optimal cache efficiency, SIMD vectorization, and GPU memory coalescing.
Eliminating GPU initialization latency through nvidia-persistenced - a userspace daemon that maintains GPU driver state for optimal startup performance.
Deep dive into PyTorch DataLoader num_workers parameter: how parallel workers prefetch data, optimal configuration, and common pitfalls.
CPU performance optimization: memory hierarchy, cache blocking, SIMD vectorization, and profiling tools for modern processors.