Clarify the data parallel setup: each worker holds the full weight matrix W and a shard of the input batch X. Compute the local output Y_local = X_local @ W^T (or W depending on convention) on each worker, then gather all Y_local shards and concatenate them along the batch dimension to form the final output Y.
Pro tip: Mention that if the batch is sharded along the batch dimension, no communication is needed for the weights, and the final assembly is a simple concatenation; also note that if the sharding were along the feature dimension, an all-reduce would be required, showing you understand the distinction.
Confirm that each worker has the full weight matrix W and a disjoint shard of the input batch X (e.g., X_i of shape [batch_i, in_features]). The output shard Y_i will have shape [batch_i, out_features].
On each worker, compute Y_i = X_i @ W^T (or X_i @ W depending on whether W is stored as [out_features, in_features] or [in_features, out_features]). This is a local matrix multiplication with no inter-worker communication.
Gather all Y_i from workers and concatenate them along the batch dimension (dim=0) to form the full output Y of shape [total_batch, out_features]. Ensure the order matches the original batch order.
Consider uneven shards (e.g., last worker gets fewer samples), and ensure the concatenation handles variable batch sizes per worker. Also, if the batch is not sharded along dim 0, adjust the concatenation axis accordingly.
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This one was genuinely hard to get right under pressure.
Start by clarifying the problem scope and constraints, then outline the sharding strategy and communication pattern. Walk through the implementation details, emphasizing memory bounds and correctness, and finish by discussing trade-offs and potential optimizations.
Pro tip: Mention that all-gather can be overlapped with computation to hide communication latency, and that using a bucketed or pipelined all-gather can reduce peak memory further. This shows awareness of real-world performance considerations.
Confirm the matrix dimensions, number of workers, memory limits, and whether the weight matrix is sharded along rows or columns. Ask about the expected input distribution and output requirements.
Decide how to partition the weight matrix across workers (e.g., column-wise sharding for row-parallel input). Plan the all-gather operation to collect shards needed for each worker's computation, ensuring only necessary shards are gathered to bound memory.
Write code that performs local matrix multiplication using the gathered shards, then reduces or scatters results as needed. Use asynchronous communication to overlap all-gather with computation where possible.
Verify that each worker's memory usage is limited to its shard plus temporary buffers. Test correctness against a single-worker baseline and check for numerical stability.
Analyze communication overhead, scalability, and potential improvements like gradient accumulation, mixed precision, or using all-to-all instead of all-gather for certain sharding schemes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt like the interview was winding down and this was the debrief question.
Start by briefly naming the two parallelism strategies you implemented (e.g., data parallelism and model parallelism) and the context in which they were used. Then compare them across the three dimensions—correctness guarantees, memory footprint, and communication overhead—using concrete metrics and trade-offs. Conclude with how you chose between them based on workload characteristics and constraints.
Pro tip: Quantify wherever possible: e.g., 'communication overhead was 15% of step time for data parallelism vs. 40% for model parallelism' or 'memory footprint reduced by 60% with model parallelism.' This shows you measure and optimize, not just implement.
State the two parallelism strategies (e.g., data parallelism and model parallelism) and briefly describe the system or model where you applied them. Mention the scale (e.g., number of GPUs, model size) to ground the comparison.
Explain how each strategy ensures correct results: for data parallelism, discuss gradient synchronization (e.g., all-reduce) and potential issues like non-determinism; for model parallelism, discuss how layers are split and how communication of activations/gradients preserves correctness. Mention any validation or testing done.
Describe the memory usage per device for each strategy: data parallelism replicates the full model, so memory scales with model size; model parallelism partitions the model, reducing per-device memory but potentially increasing overall memory due to communication buffers. Provide concrete numbers if possible.
Detail the communication patterns: data parallelism requires all-reduce of gradients (communication volume proportional to model size); model parallelism requires point-to-point communication of activations/gradients between stages (volume depends on batch size and layer sizes). Discuss latency, bandwidth, and scalability implications.
Conclude with when each strategy is preferable: data parallelism for models that fit in memory and when scaling batch size is easy; model parallelism for very large models that don't fit on one device. Mention hybrid approaches if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.