Modern graphics processing units (GPUs) have complex architectures that admit
exceptional performance and energy efficiency for high-throughput applications. Although …

Cloud Computing is an emerging computing paradigm. It shares massively scalable, elastic
resources (eg, data, calculations, and services) transparently among the users over a …

Graphs are common data structures for many applications, and efficient graph processing is
a must for application performance. Recently, the graphics processing unit (GPU) has been …

Finding the shortest paths from a single source to all other vertices is a fundamental method
used in a variety of higher-level graph algorithms. We present three parallel friendly and …

GPUs have become prevalent and more general purpose, but GPU programming remains
challenging and time consuming for the majority of programmers. In addition, it is not always …

Algorithms operating on a graph setting are known to be highly irregular and unstructured.
This leads to workload imbalance and data locality challenge when these algorithms are …

As key applications become more data-intensive and the computational throughput of
processors increases, the amount of data to be transferred in modern memory subsystems …

A central task in high-level synthesis is scheduling: the allocation of operations to clock
cycles. The classic approach to scheduling is static, in which each operation is mapped to a …

Historically, improvement in GPU performance has been tightly coupled with transistor
scaling. As Moore's Law slows down, performance of single GPUs may ultimately plateau …

Graph Neural Networks (GNNs) have emerged as a promising class of Machine Learning
algorithms to train on non-euclidean data. GNNs are widely used in recommender systems …