The team received the Test of Time Award for their paper, GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server. The paper addresses the challenges of scaling deep ...

The sheer volume of ‘Big Data’ produced today by various sectors is beginning to overwhelm even the extremely efficient computational techniques developed to sift through all that information. But a ...

Introduction to parallel computing for scientists and engineers. Shared memory parallel architectures and programming, distributed memory, message-passing data-parallel architectures, and programming.

Enterprise AI workloads require infrastructure designed for large-scale data processing and distributed computing. Organizations are modernizing AI data center infrastructure with GPU computing, ...

The tide is changing for analytics architectures. Traditional approaches, from the data warehouse to the data lake, implicitly assume that all relevant data can be stored in a single, centralized ...

Hadoop, an open source framework that enables distributed computing, has changed the way we deal with big data. Parallel processing with this set of tools can improve performance several times over.

Cloud data was supposed to enable AI at scale and democratize data. But how do we cope with the new complexities of distributed data? The emerging discipline of DataOps may help us here - along with ...

Huge volumes of data need near-supercomputer power to process and analyze it all. You can get there with the .NET Task Parallel Library. As Web and mobile applications face the challenge of quickly ...