Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

In the increasingly polarized international political arena, it has become difficult to find
common ground to solve Brazil's ongoing environmental crisis, which has global as well as …

Efficiently scheduling data processing jobs on distributed compute clusters requires complex
algorithms. Current systems use simple, generalized heuristics and ignore workload …

The next generation of AI applications will continuously interact with the environment and
learn from these interactions. These applications impose new and demanding systems …

Large-batch SGD is important for scaling training of deep neural networks. However, without
fine-tuning hyperparameter schedules, the generalization of the model may be hampered …

Datacenter applications demand microsecond-scale tail latencies and high request rates
from operating systems, and most applications handle loads that have high variance over …

This paper describes a learning-based approach to the acceleration of approximate
programs. We describe the Parrot transformation, a program transformation that selects and …

A comprehensive update of the leading algorithms text, with new material on matchings in
bipartite graphs, online algorithms, machine learning, and other topics. Some books on …

Distributed computing has become a common approach for large-scale computation tasks
due to benefits such as high reliability, scalability, computation speed, and cost …