Graphics Processing Unit (GPU) devices and their associated software programming
languages and frameworks can deliver the computing performance required to facilitate the …

Irregular applications such as big graph analysis, material simulations, molecular dynamics
simulations, and finite element analysis have performance problems due to their use of …

Performing Deep Neural Network (DNN) computation on hardware accelerators efficiently is
challenging. Existing DNN frameworks and compilers often treat the DNN operators in a …

Deep Neural Networks (DNNs) have reinvigorated real-world applications that rely on
learning patterns of data and are permeating into different industries and markets. Cloud …

Studies show that non-graphics programs can be less optimized for the GPU hardware,
leading to significant resource under-utilization. Sharing the GPU among multiple programs …

We present Heimdall, a mobile GPU coordination platform for emerging Augmented Reality
(AR) applications. Future AR apps impose an explored challenging workload: i) concurrent …

Modern GPUs are broadly adopted in many multitasking environments, including data
centers and smartphones. However, the current support for the scheduling of multiple GPU …

Embedded and autonomous systems are increasingly integrating AI/ML features, often
enabled by a hardware accelerator such as a GPU. As these workloads become …

As graphics processing units (GPUs) are broadly adopted, running multiple applications on
a GPU at the same time is beginning to attract wide attention. Recent proposals on …

Cache is designed to exploit locality; however, the role of on-chip L1 data caches on modern
GPUs is often awkward. The locality among global memory requests from different SMs …