Data parallel
Data parallelism is a parallel-computing technique that applies the same computation concurrently to separate portions of data.
Designing Deep Learning Systems: A guide for software engineers
A distributed training strategy that replicates a model across computing devices, partitions data batches among them, and combines their parameter updates.
- How it is used
- Accelerates training of large datasets by distributing minibatches across replicated model workers.
- How deep it goes
- Explains synchronous execution, gradient aggregation, scaling limits, recovery, and framework configuration without model-theory detail.
- Examples
- Demonstrates three-GPU synchronization plus PyTorch DDP, TensorFlow MultiWorkerMirroredStrategy, and Horovod code patterns.
- What you can do after
- Operate/configure it through PyTorch, TensorFlow, or Horovod worker settings and resilient training-service practices.
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LLMs in Production
Data parallelism is a parallel-computing technique that processes separate portions of data concurrently with multiple copies of a model or pipeline.
- How it is used
- Uses replicated model copies to process separate data portions concurrently in distributed LLM training and inference workflows.
- How deep it goes
- Defines the workload split and names PyTorch configuration, but stops short of explaining synchronization or communication internals.
- Examples
- Demonstrates Ray task submission by comparing ten sequential calls with ten remote calls and collected results.
- What you can do after
- Apply it via PyTorch DistributedDataParallel or Ray when distributing compatible workloads across devices or cluster nodes.
Deep Learning with Python, Third Edition
A distributed-training method where each device holds a full replica of the model and processes different sub-batches in parallel.
- How it is used
- Scales training across devices by processing different portions of a batch on full model replicas.
- How deep it goes
- Explains replicas and batch splitting with numbers, then gives Keras setup and performance caveats; does not detail update-merging mechanics.
- Examples
- Uses a batch of 128 split across two GPUs and Keras DataParallel calls for automatic or selected-device setup.
- What you can do after
- Apply it via Keras DataParallel with JAX, selecting devices when needed and checking that each GPU receives enough data.
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Deep Learning with R, Third Edition
Data parallelism is a distributed-training technique that replicates a model across devices and processes different data subsets on those devices.
- How it is used
- Scales training across devices by processing different data subsets with copies of a model.
- How deep it goes
- Explains batch splitting, replica results, and Keras setup, but does not detail multi-GPU machine provisioning.
- Examples
- Works through a batch of 128 split across two GPUs and shows Keras DataParallel configuration.
- What you can do after
- Apply it via Keras DataParallel with JAX, optionally selecting devices and checking whether the batch is large enough.
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Deep Learning with PyTorch, Second Edition
A training strategy that replicates a model across devices while splitting data among them.
- How it is used
- Splits training data across devices while keeping a model replica on each device.
- How deep it goes
- Works through gradient reduction and an optimizer step, then delegates optimized synchronization to DDP.
- Examples
- Uses a manual all-reduce training loop, DDP model wrapping, and a DistributedSampler data loader.
- What you can do after
- Apply it via PyTorch DDP and DistributedSampler to synchronize model replicas while assigning processes separate data subsets.
Read how Deep Learning with PyTorch, Second Edition explains data parallel →
Sutskever's List
Data parallelism is a training technique that replicates a model across devices, splits batches among them, and synchronizes parameter updates.
- How it is used
- Scales training across GPUs by distributing batches while keeping model replicas synchronized.
- How deep it goes
- Explains batch division, gradient synchronization, measured speedups, communication overhead, and memory limits; does not detail the custom all-reduce routine.
- Examples
- Uses Deep Speech 2’s 8-to-16-GPU runs, batch sizes, and custom all-reduce performance as a concrete case.
- What you can do after
- Evaluate/understand when data parallelism speeds GPU training and when communication or full-model memory limits constrain it.
Deep Learning with JAX
A distributed training strategy that splits a batch across devices, replicates parameters, and aggregates gradients before updating.
- How it is used
- Uses multiple devices to train one neural network by processing separate data chunks with synchronized model copies.
- How deep it goes
- Explains distribution, reshaping, gradient aggregation, and edge cases, while deferring model-parallel implementation to later mechanisms.
- Examples
- Demonstrates an SPMD MNIST classifier using eight-device tensors, pmap updates, and per-device loss values.
- What you can do after
- Apply it via JAX pmap by reshaping batches, aggregating gradients, and completing a multi-device training loop.
Latency
Data parallelism is the execution of identical operations simultaneously on separate portions of a dataset.
- How it is used
- Uses identical operations on separate data portions to reduce application latency.
- How deep it goes
- Works through array partitioning, partial sums, workload balance, and SIMD widths without supplying implementation code.
- Examples
- Illustrates parallel array summation with 1,000 elements and locality-aware partitioning with contiguous matrix blocks.
- What you can do after
- Evaluate/understand when dividing data into balanced, independent chunks may reduce latency.
Related concepts
- distributed trainingkind of · Distributed training is the coordinated training of a machine-learning model across multiple computing devices, processes, or machines by distributing computation or data among them.
- rayonimplemented by · Rayon is a Rust library for data-parallel programming through parallel iterators and work distribution across threads.
- model parallelismcontrasts with · Model parallelism is a distributed-computing technique that partitions a machine-learning model’s parameters, layers, or computations across multiple devices.
- GPUuses · A graphics processing unit (GPU) is a processor designed for highly parallel computation, especially graphics and data-parallel workloads.
- parallelismkind of · Parallelism is the concurrent execution of multiple computational operations, often across multiple processing resources.
- allreduceuses · Allreduce is a collective communication operation that combines corresponding values across participating processes or devices and distributes the resulting aggregate to every participant.
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