Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
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 MPI guide — point-to-point and collective communication with real C and mpi4py code, deadlock simulation, performance benchmarking, communicator splitting, and debugging on HPC clusters.
OpenMP parallel programming: fork-join model, scheduling, data races, false sharing, NUMA thread affinity, and GPU offloading.
CUDA page migration and fault handling between CPU and GPU memory. Learn TLB management, DMA transfers, and memory optimization.
How Slurm tracks resource consumption through account hierarchies, TRES billing, and resource limits — sacctmgr, sreport, and the association model explained.
How C++ programs are loaded — ELF segments, the _start to main() chain, dynamic linking with PLT/GOT, ASLR, real readelf/strace/proc maps output, and startup debugging.