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Data parallelism copies a model onto multiple devices and divides the training batch among those copies. Tensor parallelism splits the calculations inside individual layers across devices. Pipeline parallelism places successive groups of layers on different devices and passes activations between them. The three methods address different dimensions of the same training job, so large-model systems often combine them rather than choose only one.
The practical choice depends on whether the bottleneck is batch throughput, the memory and compute required by a single layer, or the total depth of the model. Network topology, microbatch scheduling, memory for optimizer state, and framework support can change the best arrangement; no strategy is universally fastest.
The three strategies at a glance
| Strategy | What is divided? | Primary purpose | Important cost or risk |
|---|---|---|---|
| Data parallelism | Examples in a batch | Increase throughput when a complete model replica fits on every device | Replicated model state and gradient synchronization |
| Tensor parallelism | Layer operations and their tensors | Compute or fit very large layers and hidden dimensions | Communication among devices during each layer |
| Pipeline parallelism | Sequential groups of layers | Spread a deep model across devices | Stage imbalance and idle time, known as pipeline bubbles |
These definitions match the distinctions in NVIDIA Megatron Core’s parallelism guide. “Model parallelism” is often used as an umbrella term for tensor and pipeline parallelism, while data parallelism normally keeps a whole model copy on each worker.
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Data parallelism: divide the batch
How replicated data parallelism works
In basic data-parallel training, every participating device stores a copy of the model. A global batch is split into per-device portions, and each worker runs a forward pass and backward pass on its own samples. Workers then synchronize their gradients, commonly with an all-reduce operation, so that their parameter updates remain equivalent. The next step starts with matching model weights.
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Megatron Core summarizes the arrangement as each GPU having a full model copy and processing a portion of the batch. The PyTorch DistributedDataParallel (DDP) documentation makes an important implementation detail explicit: DDP synchronizes gradients, but it does not divide input data for you. Your training program must assign distinct samples to each process, typically with a distributed sampler and a correctly configured data loader.
When data parallelism is the right first step
- One device can hold the model, gradients, activations needed by your chosen batch size, and the training state such as optimizer data.
- You want more samples processed per unit time by running replicas concurrently.
- Your interconnect can complete gradient synchronization without overwhelming the time saved by parallel execution.
- Your framework and input pipeline already support multiple processes and deterministic sharding of the dataset.
Increasing the number of replicas does not make an oversized model fit on a single device. If every worker still needs a complete copy and that copy exceeds device memory, plain replicated DDP cannot solve the capacity problem.
Input handling and synchronization details
Each process needs a unique slice of the effective batch for a given step. A distributed sampler normally changes its ordering every epoch through a set_epoch call, preventing workers from repeatedly seeing the same sequence. The global batch is the per-device batch multiplied by the number of data-parallel workers, subject to gradient accumulation and any framework-specific definition of a training step.
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Synchronization also affects failure behavior. A worker that reaches a collective operation while another worker has crashed or taken a different code path can leave the job waiting indefinitely. Ensure every process executes the same collectives in the same order, and configure launch, checkpoint, and restart handling for the distributed environment.
DDP versus sharded data parallelism
Do not use “data parallelism” as a synonym for every technique that distributes model state. Fully Sharded Data Parallel (FSDP) and ZeRO-style systems partition parameters, gradients, and or optimizer states instead of keeping all of them replicated on every worker. The PyTorch distributed package documentation covers these broader distributed primitives. Sharding can reduce memory per device, but it introduces different communication and implementation trade-offs; describe it separately from basic replicated DDP when comparing architectures.
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Tensor parallelism: split work inside a layer
What is partitioned
Tensor parallelism divides a layer’s mathematical operation across devices. A large matrix multiplication might be split by rows or columns, for example, so each device stores and computes only a portion of the weights or intermediate activations. The partial results are then combined, or the next operation consumes a correspondingly partitioned representation.
Unlike data parallelism, the devices participating in one tensor-parallel group cooperate on the same sample and the same layer execution. Communication therefore occurs during the forward and backward computation, not only after a complete batch has been processed. The exact collectives depend on the partitioning scheme and layer implementation.
When tensor parallelism helps
- A single layer’s weights or activations do not fit comfortably on one device.
- The model has very large hidden, attention, or feed-forward dimensions that can be divided efficiently.
- You have a fast, closely connected group of devices so within-layer communication does not dominate computation.
- Your model architecture has tensor-parallel implementations for the operations you use.
The PyTorch tensor-parallel tutorial presents tensor parallelism as a model-parallel technique for large Transformer-like models. In practice, unsupported custom layers, uneven dimensions, or operations that require gathering full tensors can limit the benefit.
Communication and placement considerations
Tensor-parallel ranks should generally be placed on devices with the strongest mutual interconnect. A topology that is excellent for independent replicas may be less suitable when every layer requires frequent exchanges. Measure time spent in collectives, overlap communication with computation where the implementation permits it, and account for the memory used by temporary gathered tensors.
Tensor parallelism does not automatically distribute the model’s sequential depth. If every layer is tensor-partitioned but all layers still execute in order on the same tensor group, the method addresses layer width, not depth. Pipeline parallelism is the complementary tool for distributing that depth.
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Pipeline parallelism: split the model by depth
Stages and activation flow
Pipeline parallelism assigns consecutive groups of layers to separate devices or device groups. Stage one runs the first layers and sends activations to stage two; later stages continue the forward pass. During backpropagation, gradients travel in the reverse direction. Each stage stores the activations it needs until its corresponding backward work is complete.
Because a single batch would leave most stages waiting, pipeline systems divide it into microbatches. While one stage processes a later microbatch, another stage can process an earlier microbatch. The schedule determines when each stage performs forward and backward work and when it sends or receives tensors.
Pipeline bubbles and stage balance
The periods when a stage has no useful work are called pipeline bubbles. Bubbles arise while the pipeline is filling and draining and whenever stages have unequal execution times. More stages can reduce the memory held by each stage, but increasing the pipeline degree can also lower utilization if the workload does not provide enough microbatches or if communication and imbalance grow.
Assign layers so that stage times and memory requirements are as even as possible. A stage containing unusually expensive attention, embedding, or output operations can become a bottleneck even when every stage owns the same number of layers. Some systems support virtual pipeline stages, which interleave smaller chunks of the model on each device to improve balance. The DeepSpeed pipeline tutorial describes the microbatch-oriented mechanics and scheduling choices.
When pipeline parallelism is useful
- The model is too deep or too large in total to place all layers on one device or one tensor-parallel group.
- Layers can be divided into stages with reasonably predictable execution and memory demands.
- You can use enough microbatches to keep stages busy without creating an impractically large effective batch.
- Your framework supports the model’s forward and backward schedule, activation transfer, checkpointing, and recomputation requirements.
Pipeline parallelism is not simply a slower form of tensor parallelism. Tensor parallelism communicates within a layer; pipeline parallelism communicates between layer groups and introduces scheduling idle time. Which cost is smaller depends on the model and hardware.
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How the strategies differ in real training jobs
Data parallelism versus model parallelism
Data parallelism replicates the model and divides independent examples. Tensor and pipeline parallelism divide the model computation itself, so the devices in one model-parallel group jointly execute each example. That is why replicated data parallelism is effective for throughput scaling only after a complete model replica fits, while model-parallel methods can address a model-capacity limit.
The phrase “model parallelism” can be broad. In this context, tensor parallelism means partitioning operations within layers, and pipeline parallelism means partitioning the ordered layer stack. Sharded data parallelism, context parallelism (which partitions sequence-related work), and expert parallelism for mixture-of-experts models use other axes and should not be conflated with these three.
Tensor parallelism versus pipeline parallelism
| Question | Tensor parallelism | Pipeline parallelism |
|---|---|---|
| Where is the model split? | Within individual layer computations | Between sequential groups of layers |
| When do devices communicate? | Repeatedly during layer execution | When passing activations forward and gradients backward between stages |
| What hardware characteristic matters most? | Fast, low-latency communication among ranks in the same layer | Enough bandwidth between stages and a topology that supports balanced stage placement |
| What is the principal efficiency risk? | Collective communication dominating computation | Pipeline bubbles or uneven stage workloads |
There is no evidence-based universal answer to “which is faster.” A result measured for a particular model, GPU generation, network, microbatch schedule, and software version cannot be transferred automatically to another setup. The 2021 Megatron-LM paper analyzes trade-offs for the systems it studied; its measurements are not a universal ranking for modern workloads.
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Parallel dimensions are complementary
A training job can use a tensor-parallel group inside each pipeline stage and replicate that complete model-parallel arrangement across data-parallel groups. In a simplified configuration, the total number of participating devices is the product of the data-parallel degree, tensor-parallel degree, and pipeline-parallel degree:
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Megatron Core also documents configurations that add other dimensions, such as context or expert parallelism. The product is a way to describe a configuration, not a promise that every factor should be increased.
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A practical sequence for designing the layout
- Check a single replica. Determine whether the model, activations, gradients, and optimizer state fit on one device at the intended sequence length and batch settings.
- Start with data parallelism when it fits. Use replicated workers to raise throughput, and verify that input sharding and gradient synchronization scale acceptably.
- Add tensor parallelism for wide layers. Choose a degree that lets the largest layer fit and keeps communicating ranks on a fast interconnect.
- Add pipeline parallelism for depth. Divide the ordered layers into stages, estimate each stage’s memory and runtime, and select enough microbatches to keep the schedule occupied.
- Recalculate the global batch and memory budget. Changing any degree changes per-device batch, activation storage, communication volume, and often the number of gradient-accumulation steps.
- Benchmark the actual workload. Record throughput, step time, peak memory, collective time, pipeline utilization, and validation behavior rather than relying on a generic rule.
Illustrative layout
Imagine a transformer whose layers are too wide for one device but whose full depth also exceeds the memory of a single tensor group. You might partition each stage’s large matrix operations with tensor parallelism, place successive stage groups on different devices with pipeline parallelism, and replicate the complete pipeline across data-parallel groups to process more examples. The exact degrees must be chosen from the model’s dimensions, available device count, topology, and framework constraints; the documentation’s example configurations are illustrations, not universal prescriptions.
Choosing a strategy by the bottleneck
| Observed constraint | First strategy to evaluate | Why | What to measure next |
|---|---|---|---|
| The model fits, but training throughput is too low | Data parallelism | Independent replicas can process more samples concurrently | Gradient-reduction time, input-pipeline throughput, scaling efficiency |
| One or more layers exceed device memory or have very large dimensions | Tensor parallelism | Layer weights and computations can be partitioned | Within-layer collective time, activation memory, interconnect utilization |
| The complete layer stack cannot fit in one model-parallel group | Pipeline parallelism | Different depth segments reside on different stages | Stage runtime balance, bubble fraction, activation transfers |
| The model needs both wider layers and more total depth than one device group can handle | Tensor plus pipeline parallelism | The methods partition different model dimensions | End-to-end step time and the interaction between both communication patterns |
| Optimizer or gradient state is the memory bottleneck while the model itself fits | Sharded data parallelism | State is distributed instead of fully replicated | Sharding communication, peak memory, checkpoint and recovery behavior |
Common implementation pitfalls
Assuming DDP shards inputs automatically
Wrapping a module in DDP does not ensure that workers see different samples. Configure a distributed sampler or equivalent input partitioning, call the sampler’s epoch-setting method when required, and verify that the effective global batch is what you intend.
Using tensor parallelism across a slow link
Frequent layer-level collectives can erase the benefit of splitting computation if the ranks communicate over a high-latency or congested connection. Keep tensor groups within the fastest available domain when possible, and compare measured collective time with the compute time saved.
Balancing pipeline stages by layer count alone
Equal numbers of layers do not guarantee equal work. Account for sequence length, attention and feed-forward dimensions, embeddings, output heads, activation memory, and recomputation. Profile each proposed stage and adjust boundaries or virtual stages when the framework supports them.
Ignoring microbatch and schedule effects
A pipeline with too few microbatches spends a larger fraction of time filling and draining. Increasing microbatches can improve occupancy but may alter memory use, optimization behavior, and the global batch. Test the schedule under the same sequence lengths and accumulation settings used for production.
Generalizing a benchmark
Report hardware topology, model architecture, precision, sequence length, batch and microbatch sizes, software versions, and parallel degrees with any performance result. Without those conditions, “faster” comparisons between tensor and pipeline parallelism are not reliable.
Bottom line for a design decision
Use replicated data parallelism when a full model and its training state fit on each device and your goal is higher throughput. Use tensor parallelism when individual layers or their dimensions are the capacity or compute problem. Use pipeline parallelism when the model’s depth must be distributed and you can keep stages balanced with an appropriate microbatch schedule. Combine the methods when the model requires more than one partitioning axis, then validate the arrangement on the exact model, hardware topology, and software stack that will run it.
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