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 ...

Model Parallelism: A strategy that divides a neural network model into segments distributed over several devices, each processing part of the overall computation concurrently.

Data parallelism, on the other hand, revolves around employing the same model across multiple servers while each server operates on a different subset of the dataset.

Task parallelism on the other hand is where you have multiple tasks that need to be done. So perhaps you have a large data set and you want to know the minimum value and you want to know the ...

In the task-parallel model represented by OpenMP, the user specifies the distribution of iterations among processors and then the data travels to the computations. In data-parallel programming, the ...

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… ...

Two Google Fellows just published a paper in the latest issue of Communications of the ACM about MapReduce, the parallel programming model used to process more than 20 petabytes of data every day ...

The model weights and optimizer state can take as much as 10.8 Terabytes of memory for training for GPT4. Tensor parallelism reduces the total memory used per GPU by the number of tensor parallelism ...

For embarrassingly parallel problems, for example digital tomography, an under-$10,000 Tesla personal supercomputer can beat a $5 million Sun CalcUA. CUDA makes the parallel programming tractable.