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 …

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 …

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 …

Modern distributed machine learning (ML) training workloads benefit significantly from
leveraging GPUs. However, significant contention ensues when multiple such workloads are …

We study how to support elasticity, that is, the ability to dynamically adjust the parallelism (ie,
the number of GPUs), for deep neural network (DNN) training in a GPU cluster. Elasticity can …

With the sustained technological advances in machine learning (ML) and the availability of
massive datasets recently, tech companies are deploying large ML-as-a-Service (MLaaS) …

Deep learning training on a shared GPU cluster is becoming a common practice. However,
we observe severe sharing anomaly in production multi-tenant clusters where jobs in some …