dataparallel

Dataparallel, or data parallelism, is a form of parallel computing in which the same operation is applied to different pieces of data in parallel. Each processing unit, such as a CPU core, GPU, or compute node, handles a partition of the input independently, and a combining step aggregates the partial results to produce the final output. This approach is well suited to workloads where the computation per data item is substantial and the data can be partitioned with limited inter-item dependencies.

In practice, dataparallelism is achieved by replicating the program or model across devices and distributing the data among them. On a single machine with multiple GPUs or cores, data is split and fed to each device, with results reduced to form the complete batch outcome. In distributed systems, the same idea extends across machines, often using collective communications such as all-reduce to combine partial results, for example by averaging gradients in machine learning or aggregating partial computations.

In machine learning, data-parallel training typically involves multiple workers, each owning a copy of the model and a subset of the training data. After computing gradients on their data, the gradients are averaged or summed to update the model parameters. This can be done synchronously, where all workers synchronize at each step, or asynchronously, where workers proceed independently and updates are merged later. Data parallelism is a common strategy for scaling training and inference on large datasets and complex models.

Key considerations include the communication overhead between devices, load balancing, memory usage due to model replication, and the potential for synchronization bottlenecks. Effective dataparallel deployments often balance computation and communication to maximize throughput.