Efficiently scheduling deep learning jobs on large-scale GPU clusters is crucial for job
performance, system throughput, and hardware utilization. It is getting ever more …

We introduce Gandiva, a new cluster scheduling framework that utilizes domain-specific
knowledge to improve latency and efficiency of training deep learning models in a GPU …

Deep learning (DL) training jobs bring some unique challenges to existing cluster
managers, such as unpredictable training times, an all-or-nothing execution model, and …

With widespread advances in machine learning, a number of large enterprises are
beginning to incorporate machine learning models across a number of products. These …

With widespread advances in machine learning, a number of large enterprises are
beginning to incorporate machine learning models across a number of products. These …

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 …

Specialized accelerators such as GPUs, TPUs, FPGAs, and custom ASICs have been
increasingly deployed to train deep learning models. These accelerators exhibit …

Training Deep Learning (DL) model requires multiple resource types, including CPUs,
GPUs, storage IO, and network IO. Advancements in DL have produced a wide spectrum of …

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped
with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of …

Training Deep Neural Networks (DNNs) is a popular workload in both enterprises and cloud
data centers. Existing schedulers for DNN training consider GPU as the dominant resource …