To use parallelism effectively to improve algorithm design, you should analyze the problem and identify the sources and potential of parallelism, such as data, task, or pipeline parallelism.

Learn how to implement parallelism in data visualization using different programming languages and libraries. Discover the benefits, challenges, and options of parallelism for data visualization.

An Application of Data Parallelism At the Max Planck Institute in Munich, Germany, researchers applied data parallelism to a LabVIEW program that performs plasma control of Germany's most advanced ...

Data parallelism In data parallelism, the dataset is split into ‘N’ parts, where ‘N’ is the number of GPUs. These parts are then assigned to parallel computational machines. Post that, gradients are ...

Data Parallelism 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 ...

So let’s start with task and data parallelism. Parallelism is doing multiple things at once, but there are fundamentally two types of parallelism that people will talk about.

It’s exactly the embarrassingly parallel problems that exhibit massive data parallelism, and so they are the problems that show the best speed-ups by shifting the processing from the CPU to the GPU.

This is a schematic showing data parallelism vs. model parallelism, as they relate to neural network training.

For more on this topic see Using pipelining in multicore LabView and Using data parallelism in multicore LabView. Until recently, advances in computing hardware have provided significant increases in ...

Intel director James Reinders explains the difference between task and data parallelism, and how there is a way around the limits imposed by Amdahl's Law ...