As transistor scaling becomes increasingly more difficult to achieve, scaling the core count
on a single GPU chip has also become extremely challenging. As the volume of data to …

Multi-GPU systems have become a popular platform to meet the ever-growing application
demands. However, employing multiple GPUs does not guarantee proportional performance …

Graphics Processing Unit (GPU) performance has relied heavily on our ability to scale of
number of transistors on chip, in order to satisfy the ever-increasing demands for more …

With the growing popularity of cloud gaming and cloud virtual reality (VR), interactive 3D
applications have become a major class of workloads for the cloud. However, despite their …

Although the k-means algorithm has been parallelized into different platforms, it has not yet
been explored on multi-GPU architecture thoroughly. This paper presents a study of …

Nowadays the use of graphical processors in fast and accurate scientific calculations has
increased. The heterogeneous design space that is conducted by the processors could …

While multi-GPU (MGPU) systems are extremely popular for compute-intensive workloads,
several inefficiencies in the memory hierarchy and data movement result in a waste of GPU …

Dynamic parallelism is a feature of general purpose graphics processing units (GPUs)
whereby threads running on a GPU can spawn other threads without CPU intervention. This …

GPUs have been adopted widely based their ability to exploit data-level parallelism found in
modern-day applications, ranging from high performance computing to machine learning …

Over the past few decades, Machine Learning (ML) has gained unprecedented popularity,
becoming a pervasive technology that has benefitted a broad range of domains such as …