Explore machine learning concepts related to hardware. 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.
Explore the inner workings of RAM through beautiful animations and interactive visualizations. Understand memory cells, addressing, and the memory hierarchy.
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.
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.
Understanding character devices, major/minor numbers, and the device file hierarchy created by NVIDIA drivers for GPU access in Linux.
Learn how memory controllers manage CPU-RAM data flow. Interactive demos of channels, ranks, banks, and command scheduling for optimal bandwidth.
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.
Explore NUMA architecture and memory locality in multi-socket systems. Understand local vs remote memory access latency and optimization strategies.
Automate NVIDIA GPU management in Kubernetes with the GPU Operator. Deploy drivers, device plugins, and monitoring as DaemonSets.
Explore the concept of CUDA contexts, their role in managing GPU resources, and how they enable parallel execution across multiple CPU threads.