Stream compaction and reduction are both fundamental parallel primitives used extensively in GPU programming, but they serve distinct purposes and have different characteristics.
Stream Compaction:
Purpose:
Stream compaction (also known as stream compression or filtering) is the process of removing elements from a data stream that do not satisfy a certain condition. It creates a new, smaller stream containing only the elements that pass the filter.
Mechanism:
The process involves two main steps:
1. Predicate Evaluation: Each element in the input stream is evaluated against a predicate (a boolean condition).
2. Compaction: Elements that satisfy the predicate are copied into a new, contiguous output stream, effectively "compacting" the stream by removing the elements that failed the predicate.
Applications:
1. Particle Systems: In particle simulations, stream compaction can be used to remove dead or inactive particles from the system, reducing the amount of computation required in subsequent simulation steps.
2. Collision Detection: Stream compaction can be used to identify pairs of objects that are potentially colliding. The predicate would check for proximity between objects, and only those pairs that are close enough would be passed on to more detailed collision detection algorithms.
3. Image Processing: Stream compaction can be used to select pixels based on color or intensity values, effectively filtering out unwanted regions of an image.
4. Graph Algorithms: Stream compaction can be used to remove edges from a graph that do not meet certain criteria, such as edges with low weights or edges that are not part of a connected comp....
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