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This diagram illustrates a key inefficiency in standard 'prefill in one go' batching. Sequence 2, with a short prompt, completes its prefill (P₂₁) and first decoding step (D₂₁) in Iteration 1. It then enters a prolonged 'Idle Time' during Iteration 2, as it must wait for the much longer prefilling of Sequence 1 (P₁₁) to complete. Only after this long prefill finishes can both sequences proceed with decoding in parallel from Iteration 3 onwards. This idle period demonstrates how long prefill tasks can block shorter decoding tasks, leading to underutilization of hardware and increased latency for some sequences in the batch.

Example of Decoder Idle Time in Standard Prefilling

Based on the provided system log, identify the specific iteration where a key inefficiency occurs for Sequence B and explain why this inefficiency is a direct consequence of the batching strategy described.

Analyzing Hardware Utilization in Batched Inference

Consider a batch of two sequences being processed by a language model. Sequence A has a very long prompt, and Sequence B has a very short prompt. The system uses a strategy where it must finish processing the entire prompt for all sequences in the batch before it can begin generating the second token for any sequence. Analyze why Sequence B will experience a significant delay before its second token is generated, even though its own prompt was processed quickly.

Explaining Inefficiency in Batched Processing

A language model processes a batch containing two sequences: Sequence A with a long prompt and Sequence B with a short prompt. The system is configured to complete the entire prompt-processing (prefill) phase for all sequences in the batch before starting the parallel token-generation (decode) phase for the entire batch. Which statement best analyzes the primary source of computational inefficiency in this scenario?

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