Tag: #hpc
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 5 posts
Running a GPU Cluster with Slurm — Knowing Why a Job Will Not Run Matters More Than Submitting
Everything you need to actually use Slurm on a GPU cluster in practice. Sets up the coordinate system of partition, QoS, and account first, then covers how to request GPUs, CPUs, and memory in an sbatch script and the bi
2026-08-02 · 14 min read #mlops#slurm#hpc#gpu-cluster#distributed-trainingWEKA High-Performance Storage Complete Guide 2025: Parallel File System for AI/HPC Workloads
Everything about WEKA! Parallel file system architecture, NVMe tiering, GPU Direct Storage, AI/ML workload optimization, cloud integration (AWS/Azure/GCP), vs Ceph/Lustre/GPFS, data pipelines, performance benchmarks.
2026-03-25 · 22 min read #weka#wekafs#storage#parallel-filesystem#ai-infrastructureNVIDIA GPU and CUDA Architecture Deep Dive: Why GPUs Dominate AI
From H100 hardware specs to Tensor Core WMMA API. A complete breakdown of the SIMT execution model, shared memory tiling, warp divergence, and why GPUs are purpose-built for AI workloads.
2026-03-18 · 14 min read #cuda#gpu#nvidia#matrix-multiplication#parallel-computingMastering Slurm: A Practical Guide to the HPC/AI Cluster Workload Manager
A comprehensive, hands-on guide to the Slurm workload manager. Covers architecture (slurmctld/slurmd/slurmdbd), core concepts (Partitions/QoS/Fairshare), essential commands (sbatch/srun/salloc), GPU scheduling (GRES/MIG/
2026-03-01 · 15 min read #slurm#hpc#gpu#distributed-training#clusterCUDA Hands-on Complete Guide: Everything About GPU Computing
A comprehensive guide covering NVIDIA CUDA from fundamental concepts to GPU architecture, C/C++ kernel programming, memory optimization, Python integration (PyTorch, Numba, CuPy), Multi-GPU training, profiling, and troub
2026-03-01 · 51 min read #cuda#gpu#nvidia#deep-learning#machine-learning