GPU Scheduling

A technical note on ASTRAEA, a multi-tenant GPU scheduler that measures fairness by long-term GPU-time instead of instantaneous allocation or finish time alone.

一篇关于 ASTRAEA 的技术笔记：它面向多租户 GPU 集群，用长期 GPU-time 衡量公平性，避免只看瞬时分配或任务完成时间。

A technical story behind Hydro’s Bubble Squeezer, which runs surrogate hyperparameter tuning trials inside the idle bubbles of pipeline-parallel large-model training.

A technical guide to GPU datacenter scheduling based on our ACM Computing Surveys paper, covering training, inference, HPO, mixed workloads, and future scheduler design.

A technical note on GPU checkpoint/restore for schedulers, using FlowGPU as the main reference and my cudaw prototype as the first version of the codebase.

GPU checkpointing and restore promises to enable emerging tasks, such as deep learning, to benefit from functionalities like task …

Large Language Models (LLMs) have presented impressive performance across several transformative tasks. However, it is non-trivial to …

We present UniSched, a unified scheduler to optimize different types of scheduling objectives (e.g., guaranteeing the deadlines of SLO jobs, minimizing the latency of best-effort jobs).

Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL model is a time-consuming and …

Hyperparameter tuning is an essential step in deep learning model development that provides better model performance at the cost of …