As someone who has spent the better part of two decades optimizing distributed systems—from early MapReduce clusters to ...

GridGain’s Data Fabric – an in-memory analytics software implementation that can be used for real time Big Data applications. It is designed for a distributed, massively parallel processing ...

Scale-Out with Distributed Memory. In contrast with the scale-up design, a scale-out or a distributed memory system is a method where multiple independent computers are used together to solve big ...

Achieving continuous learning with in-memory computing. Today’s in-memory computing platforms are deployed on a cluster of servers that can be on-premises, in the cloud, or in a hybrid environment.

Scaling AI Isn't A Computing Problem... Dedicated hardware, like GPUs (graphics processing units) and TPUs (tensor processing units), has become essential for training AI models.

CS 358 serves as an introduction to the field of parallel computing. Topics include common parallel architectures (shared memory, distributed memory, CPU vs. GPU, multicore vs. multiprocessor), ...

Selected advanced topics including: Parallel computing; network security; client-server computing; compression; web applications; wireless and mobile computing. The fourth number of the course code ...