Approximate Nearest Neighbor Search (ANNS) plays a critical role in applications such as search engines, recommender systems, and RAG for LLMs. Vector quantization (VQ), a crucial technique for ANNS, is commonly used to reduce space overhead and accelerate distance computations. However, despite significant research advances, state-of-the-art VQ methods still face challenges in balancing encoding efficiency and quantization accuracy. To address these limitations, we propose a novel VQ method called SAQ. To improve accuracy, SAQ employs a new dimension segmentation technique to strategically partition PCA-projected vectors into segments along their dimensions. By prioritizing leading dimension segments with larger magnitudes, SAQ allocates more bits to high-impact segments, optimizing the use of the available space quota. An efficient dynamic programming algorithm is developed to optimize dimension segmentation and bit allocation, ensuring minimal quantization error. To speed up vector encoding, SAQ devises a code adjustment technique to first quantize each dimension independently and then progressively refine quantized vectors using a coordinate-descent-like approach to avoid exhaustive enumeration. Extensive experiments demonstrate SAQ's superiority over classical methods (e.g., PQ, PCA) and recent state-of-the-art approaches (e.g., LVQ, Extended RabitQ). SAQ achieves up to 80% reduction in quantization error and accelerates encoding speed by over 80× compared to Extended RabitQ.

Many systems are designed to run graph algorithms efficiently in memory but they achieve only cache efficiency or work efficiency. We tackle this fundamental trade-off in existing systems by designing CoroGraph, a system that attains both cache efficiency and work efficiency for in-memory graph processing. CoroGraph adopts a novel hybrid execution model , which generates update messages at vertex granularity to prioritize promising vertices for work efficiency, and commits updates at partition granularity to share data access for cache efficiency. To overlap the random memory access of graph algorithms with computation, CoroGraph extensively uses coroutine , i.e., a lightweight function in C++ that can yield and resume with low overhead, to prefetch the required data. A suite of designs are incorporated to reap the full benefits of coroutine, which include prefetch pipeline, cache-friendly graph format, and stop-free synchronization. We compare CoroGraph with five state-of-the-art graph algorithm systems via extensive experiments. The results show that CoroGraph yields shorter algorithm execution time than all baselines in 18 out of 20 cases, and its speedup over the best-performing baseline can be over 2x. Detailed profiling suggests that CoroGraph achieves both cache efficiency and work efficiency with a low memory stall and a small number of processed edges.