Dr. Hoefler’s research aims at understanding the performance of parallel computing systems ranging from parallel computer architecture through parallel programming to parallel algorithms. He is also ...

Artificial intelligence is changing the way businesses store and access their data. That's because traditional data storage ...

Hadoop with MapReduce combines some of the best features of both distributed computing and concurrent programming with a hefty dose of parallel programming thrown in for good measure. Chuck Lam, ...

It, too, is a library for distributed parallel computing in Python, with a built-in task scheduling system, awareness of Python data frameworks like NumPy, and the ability to scale from one ...

As the complexity and scale of scientific, AI, and simulation workloads continue to grow, modern high-performance computing (HPC) systems are evolving into ...

Concurrent and parallel systems form the bedrock of modern computational infrastructures, enabling vast improvements in processing speed, efficiency and scalability. By orchestrating multiple ...

Intel OPA is designed to reduce network costs (Image courtesy Intel [2]) Machine learning is but one example of a tightly coupled distributed computation where the small message traffic generated by a ...

We consider a distributed parallel server system that consists of multiple server pools and a single customer class. We show that the minimum-expected-delay faster-server-first (MED-FSF) routing ...

Using a serverless model with AWS Lambda, API Gateway, and DynamoDB, her team was able to deploy a system that remained available 99.99% of the time—even during daily surges in usage. Her team ...