Modern GPU clusters are designed to support distributed Deep Learning jobs from multiple
tenants concurrently. Each tenant may have varied and dynamic resource demands …

Many emerging cyber-physical systems, such as autonomous vehicles and robots, rely
heavily on artificial intelligence and machine learning algorithms to perform important …

GPUs are increasingly being used in real-time systems, such as autonomous vehicles, due
to the vast performance benefits that they offer. As more and more applications use GPUs …

In general-purpose graphics processing unit (GPGPU) computing, data is processed by
concurrent threads execut-ing the same function. This model, dubbed single …

For the rapid growth computation requirements in big data and artificial intelligence area,
CPU-GPU heterogeneous clusters can provide more powerful computing capacity …

GPU (Graphics Processing Unit) architecture has evolved to accelerate parts of a single
application at a time. Consequently, several aspects of its architecture, particularly the virtual …

GPUs are the workhorse in modern server infrastructure fueling advances in a number of
compute-intensive workloads such as deep neural network (DNN) training. Several recent …

With the widespread use of GPUs for performing deep learning applications, the issue of
efficient execution of multiple deep learning jobs in a GPU cluster has attracted great …

Sharing GPUs in the cloud is cost effective and can facilitate the adoption of hardware
accelerator enabled cloud. Butsharing causes interference between co-located VMs …

Deep learning (DL) has been widely adopted in various domains of artificial intelligence (AI),
achieving dramatic developments in industry and academia. Besides giant AI companies …