gpu

Weekly Paper Notes — one of the top picks from the 2026-06-20 CS paper digest. Area: Distributed Computing. Authors: Jiasheng Zhou, Longbin Zeng, Clavis Chen, Ruiming Lu et al. arXiv: 2606.20374 · PDF TL;DR ARGUS is a tracing and performance-diagnosis system designed for always-on operation on production LLM training clusters with more than 10,000 GPUs. The central insight is that no single profiler can be cheap, deep, and continuous all at once — so ARGUS decomposes observation along the training call hierarchy into three independent collection channels: CPU call stacks, framework semantics, and GPU kernel execution....

Weekly Paper Notes — one of the top picks from the 2026-06-13 CS paper digest. Area: Operating Systems / Systems. Authors: Zhuoping Yang, Yiyu Shi, Alex Jones arXiv: 2606.06697 · PDF TL;DR The GPU has quietly become a multi-tenant device — applications no longer just dispatch compute kernels, they call into vendor libraries (cuFFT, cuBLAS, NCCL), interact with GPU-resident services, and touch storage and network adapters through GPUDirect paths. But the CUDA programming model still hands each process the full keys to the device: its own context, raw device pointers, runtime handles, module loader, and direct kernel launch....

Weekly Video Notes — a short article distilling one talk from the weekly digest. Source video and key frames are embedded throughout. Dwarkesh Patel got an unusually concrete tour of a working AI training facility this week: Jane Street’s GB300 NVL72 cluster in Texas, guided by Ron Minsky (co-head of the technology group) and Daniel Pontecorvo (physical engineering). It’s only 16 minutes long, but it’s a dense walk through the things that actually break when you try to put modern GPU racks into a building that was never designed for them — cooling, leak detection, power balancing, and miles of copper and fiber....