The paper presents a fine‑grained monitoring study of a university GPU cluster, collecting data at 100 ms intervals using a lightweight tool built on Nvidia’s DCGM library. Analysis of a 10‑day period with 7,399 jobs shows that over 70 % of jobs use less than 20 % of GPU memory and 50 % have below 5 % SM utilization, while 80 % of jobs experience a large mismatch between requested and actual runtimes. The authors also observe highly periodic communication patterns in multi‑GPU jobs, indicating that significant performance gains could be achieved through improved scheduling and multitasking rather than hardware upgrades.
By Andrew Sheinberg, Samyak Gupta, Kai Li
The paper introduces PWR, a scheduling policy that selects power‑efficient GPU and CPU combinations to reduce power consumption in GPU datacenters. It complements the existing Fragmentation Gradient Descent (FGD) policy, which mitigates GPU fragmentation by using a fragmentation metric. Experiments in a simulated cluster show that combining PWR with FGD balances power savings and fragmentation reduction.
By Francesco Lettich, Emanuele Carlini, Franco Maria Nardini, Raffaele Perego, Salvatore Trani
arXiv:2605. 09370v3 Announce Type: replace-cross Abstract: Large-scale AI training is now fundamentally a distributed systems problem, and hardware failures have become routine operating conditions rather than rare exceptions.
By Daemyung Kang, Eunjin Hwang, Hanjeong Lee, HyeokJin Kim, Hyunhoi Koo, Jeongkyu Shin, Jeongseok Kang, Jihyun Kang, Joongi Kim, Junbum Lee, Jungseung Yang, Kyujin Cho, Youngsook Song
arXiv:2610.28223v1 Announce Type: cross
Abstract: Large language models can solve complex problems across a variety of subjects, such as mathematics and coding. For such complex analytical tasks, mod...
By Mattias D\"urrmeier, Alberto Lerner
arXiv:2604. 10180v2 Announce Type: replace-cross Abstract: Disaggregation maps parts of an AI workload to different types of GPUs, offering a path to utilize modern heterogeneous GPU clusters.
By Tiancheng Hu, Jin Qin, Zheng Wang, Junhao Hu, Yuzheng Wang, Lei Chen, Yizhou Shan, Mingxing Zhang, Ting Cao, Chunwei Xia, Huimin Cui, Tao Xie, Chenxi Wang
arXiv:2606. 00735v1 Announce Type: cross Abstract: In distributed Mixture-of-Experts (MoE) inference, input-dependent token routing interacts with GPU performance variability to create persistent stragglers under synchronized execution, where the slowest GPU determines layer latency.
By Seokjin Go, Marko Scrbak, Ephrem Wu, Srilatha Manne, Divya Mahajan
arXiv:2508.19073v4 Announce Type: replace-cross
Abstract: Deep learning training commonly runs on shared multi-tenant GPU servers, where exclusive allocation provides isolation but can leave resource...
By Ehsan Yousefzadeh-Asl-Miandoab, B\"u\c{s}ra Karatay Demiray, Florina M. Ciorba, Pamela Delgado, P{\i}nar T\"oz\"un
The paper introduces REACT, a system that dynamically tunes communication collectives in distributed AI training to mitigate congestion without requiring network infrastructure changes. REACT operates at the application layer, detecting congestion via flow statistics and adjusting the pattern of data exchange—such as selecting different aggregation nodes in an AllReduce tree—while preserving the semantics of the communication. Evaluations on a shared academic GPU cluster show that REACT improves algorithm bandwidth by 13%–38% under congestion, with simulations indicating potential gains up to 75%.
By Eashan Gupta, Yongzhou Chen, Apoorve Mohan, Pavlos Maniotis, Abdullah Kayi, Radhika Mittal
arXiv:2606. 09200v1 Announce Type: cross Abstract: The rapid growth of large-scale machine learning (ML) has made distributed training across multiple GPUs a fundamental component of modern ML systems.
By Minyu Cui, Miquel Pericas
arXiv:2610.14009v1 Announce Type: cross
Abstract: Large AI models increasingly rely on distributed GPU execution, yet it remains unclear whether established failure analysis methods transfer effectiv...
By Boyuan Cui, Yuyang Zeng, Yifang Tian, Hans-Arno Jacobsen
arXiv:2608. 04458v1 Announce Type: new Abstract: Agentic AI is emerging in datacenters, but its architectural implications remain unexplored.
By Jirong Yang, Peizhe Liu, Chaojie Zhang, Jovan Stojkovic