Data parallelism is an approach towards parallel processing that depends on being able to break up data between multiple compute units (which could be cores in a processor, processors in a computer, ...

When I wrote about password guessing using GPUs last week, I mentioned that password guessing is an embarrassingly parallel problem, right up there with 3-D rendering, face recognition, Monte Carlo ...

Multi-core processors theoretically can run many threads of code in parallel, but some categories of operation currently bog down attempts to raise overall performance by parallelizing computing. Is ...

The landscape of high-performance computing (HPC) storage is undergoing significant change. Traditional simulation and data engineering workloads are increasingly running alongside generative AI, ...

Achieving autonomous driving safely requires near endless hours of training software on every situation that could possibly arise before putting a vehicle on the road. Historically, autonomy companies ...

Concurrent and parallel systems span from tightly integrated multicore and many-core processors to distributed clusters and cloud infrastructures. At the hardware level, advances in pipelining, ...

This is a schematic showing data parallelism vs. model parallelism, as they relate to neural network training. Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases ...