Deep learning workload scheduling in gpu datacenters: Taxonomy, challenges and vision
Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …
Sia: Heterogeneity-aware, goodput-optimized ML-cluster scheduling
S Jayaram Subramanya, D Arfeen, S Lin… - Proceedings of the 29th …, 2023 - dl.acm.org
The Sia scheduler efficiently assigns heterogeneous deep learning (DL) cluster resources to
elastic resource-adaptive jobs. Although some recent schedulers address one aspect or …
elastic resource-adaptive jobs. Although some recent schedulers address one aspect or …
Miso: exploiting multi-instance gpu capability on multi-tenant gpu clusters
GPU technology has been improving at an expedited pace in terms of size and performance,
empowering HPC and AI/ML researchers to advance the scientific discovery process …
empowering HPC and AI/ML researchers to advance the scientific discovery process …
Deep Learning Workload Scheduling in GPU Datacenters: A Survey
Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …
development of a DL model is a time-consuming and resource-intensive procedure. Hence …
Beware of Fragmentation: Scheduling {GPU-Sharing} Workloads with Fragmentation Gradient Descent
Large tech companies are piling up a massive number of GPUs in their server fleets to run
diverse machine learning (ML) workloads. However, these expensive devices often suffer …
diverse machine learning (ML) workloads. However, these expensive devices often suffer …
Chronus: A novel deadline-aware scheduler for deep learning training jobs
Modern GPU clusters support Deep Learning training (DLT) jobs in a distributed manner.
Job scheduling is the key to improve the training performance, resource utilization and …
Job scheduling is the key to improve the training performance, resource utilization and …
AI-enabling workloads on large-scale GPU-accelerated system: Characterization, opportunities, and implications
Production high-performance computing (HPC) systems are adopting and integrating GPUs
into their design to accommodate artificial intelligence (AI), machine learning, and data …
into their design to accommodate artificial intelligence (AI), machine learning, and data …
Task placement and resource allocation for edge machine learning: a GNN-based multi-agent reinforcement learning paradigm
Machine learning (ML) tasks are one of the major workloads in today's edge computing
networks. Existing edge-cloud schedulers allocate the requested amounts of resources to …
networks. Existing edge-cloud schedulers allocate the requested amounts of resources to …
Lucid: A non-intrusive, scalable and interpretable scheduler for deep learning training jobs
While recent deep learning workload schedulers exhibit excellent performance, it is arduous
to deploy them in practice due to some substantial defects, including inflexible intrusive …
to deploy them in practice due to some substantial defects, including inflexible intrusive …
Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems
The rapid growth in demand for HPC systems has led to a rise in carbon footprint, which
requires urgent intervention. In this work, we present a comprehensive analysis of the …
requires urgent intervention. In this work, we present a comprehensive analysis of the …