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Start by defining a clear abstraction for the image processing pipeline, then design the sequential version as a baseline. For the parallel version, focus on partitioning the image data (e.g., by tiles or rows) and using a process pool to apply operations independently, then merging results. Discuss synchronization points and tradeoffs like overhead, scalability, and complexity.
Pro tip: Emphasize that the optimal parallelization strategy depends on the nature of the operations: embarrassingly parallel per-pixel operations can be easily partitioned, while operations with dependencies (e.g., convolutions) require halo regions or different decomposition. Also, mention that process-based parallelism avoids GIL issues in Python but incurs IPC overhead.
Specify the image processing operations (e.g., blur, edge detection) and their dependencies. Clarify whether operations are per-pixel, local neighborhood, or global.
Implement a simple sequential pipeline that applies each operation in order to the entire image. This serves as a correctness baseline and performance reference.
Choose a partitioning strategy (e.g., split image into tiles or rows) based on operation dependencies. For local operations, include halo regions to avoid artifacts at boundaries.
Use a process pool to apply operations to each partition concurrently. Synchronize between pipeline stages if operations depend on previous results, and merge partitions into the final image.
Compare sequential vs parallel in terms of speedup, overhead (IPC, process creation), scalability, and complexity. Discuss when parallelism is beneficial and potential bottlenecks.
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