Machine learning (ML) has recently emerged as an important application driving future
architecture design. Traditionally, architecture research has used detailed simulators to …

This article presents a graphics processing unit (GPU) scheduling scheme that maximizes
the exploitation of data locality in deep neural networks (DNNs). Convolution is one of the …

The Sia scheduler efficiently assigns heterogeneous deep learning (DL) cluster resources to
elastic resource-adaptive jobs. Although some recent schedulers address one aspect or …

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 …

Hazelwood et al. observed that at Facebook data centers, variations in user activity (eg due
to diurnal load) resulted in low utilization periods with large pools of idle resources [4]. To …

As machine learning techniques are applied to a widening range of applications, high
throughput machine learning (ML) inference servers have become critical for online service …

Recent advances in hardware, such as systems with multiple GPUs and their availability in
the cloud, are enabling deep learning in various domains including health care …

Companies build separate training and inference GPU clusters for deep learning, and use
separate schedulers to manage them. This leads to problems for both training and inference …

Distributed synchronized GPU training is commonly used for deep learning. The resource
constraint of using a fixed number of GPUs makes large-scale training jobs suffer from long …

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 …