Vector indexing enables semantic search over diverse corpora and has become an important interface to databases for both users and AI agents. Efficient vector search requires deep optimizations in database systems. This has motivated a new class of specialized vector databases that optimize for vector search quality and cost. Instead, we argue that a scalable, high-performance, and cost-efficient vector search system can be built inside a cloud-native operational database like Azure Cosmos DB while leveraging the benefits of a distributed database such as high availability, durability, and scale. We do this by deeply integrating DiskANN, a state-of-the-art vector indexing library, inside Azure Cosmos DB NoSQL. This system uses a single vector index per partition stored in existing index trees, and kept in sync with underlying data. It supports < 20ms query latency over an index spanning 10 million vectors, has stable recall over updates, and offers approximately 43× and 12× lower query cost compared to Pinecone and Zilliz serverless enterprise products. It also scales out to billions of vectors via automatic partitioning. This convergent design presents a point in favor of integrating vector indices into operational databases in the context of recent debates on specialized vector databases, and offers a template for vector indexing in other databases.

Simulation-based microarchitecture research is often hindered by the slow speed of simulators. In this work, we propose a novel statistical technique to identify highly representative unique behaviors or phases in a benchmark based on its IPC (instructions committed per cycle) trace. By simulating the timing of only the unique phases, the cycle-accurate simulation time for the SPEC suite is reduced from 5 months to 5 days, with a significant retention of the original dynamic behavior. Evaluation across many processor configurations within the same architecture family shows that the algorithm is robust. A cost function is provided that enables users to easily optimize the parameters of the algorithm for either simulation speed or accuracy depending on preference. A new measure is introduced to quantify the ability of a simulation speedup technique to retain behavior realized in the original workload. Unlike a first order statistic such as mean value, the newly introduced measure captures important differences in dynamic behavior between the complete and the sampled simulations

This paper discusses a particular network configuration plan used at New Mexico State University which enables us to effectively manage IP, Appletalk, and IPX. The network addressing scheme is based on an IP configuration of the network. From this configuration we derive Appletalk and IPX configurations. In this manner, we configure the network only once, and benefit from this configuration each time that we implement another network protocol. This configuration has helped us manage a network supporting 5000 hosts in 100 buildings at five geographically distinct locations. We believe that our network addressing scheme is simple and, hence, easy to administer. As we demonstrate in this paper, our scheme is easily applicable to other protocols and, therefore, can be a benefit to network managers.