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Hardcore Project for Internships
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Module 4
Data Layout and Exact k-NN Search
1
Brute-Force K-Nearest Neighbors Search
Implement a brute-force (sequential scan) search to find the k-nearest neighbors for a query vector.
2
CPU Cache Lines and Performance
Analyze the impact of CPU cache lines on data access patterns and performance.
3
Cache-Line Aligned Memory Allocation with `posix_memalign`
Implement a cache-line aligned memory allocator using `posix_memalign`.
4
Optimizing Vector Storage with Aligned Memory
Refactor the vector storage to use aligned memory to optimize sequential scan performance.
5
Top K Candidates with Bounded Max-Heap
Use a bounded max-heap data structure to efficiently track the top 'k' candidates during a search.
6
Exact vs. Approximate k-NN: Speed-Accuracy Tradeoff
Contrast exact k-NN search with Approximate Nearest Neighbor (ANN) search, explaining the tradeoff between speed and accuracy.
Previous module
Efficient I/O with Memory-Mapped Files
Next module
ANN with Navigable Small World (NSW) Graphs