Microsoft recently introduced Azure Synapse Analytics, which offers an integrated experience across data ingestion, storage, and querying in Apache Spark and T-SQL over data in the lake, including files and warehouse tables. In this paper, we present our experiences with designing and implementing Hyperspace, the indexing subsystem underlying Synapse. Hyperspace enables users to build multiple types of secondary indexes on their data, maintain them through a multi-user concurrency model, and leverage them automatically---without any change to their application code---for query/workload acceleration. Many requirements of Hyperspace are based on feedback from several enterprise customers. We present the details of Hyperspace's underlying design, the user-facing APIs, its concurrency control protocol for index access, its index-aware query processing techniques, and its maintenance mechanisms for handling index updates. Evaluations over standard industry benchmarks and real customer workloads show that Hyperspace can accelerate query execution by up to 10x and in certain real-world workloads, even up to two orders of magnitude.

We propose and evaluate a framework to test the functional correctness of coarse-grained reconfigurable array (CGRA) processors for pre-silicon verification and post-silicon validation. To reflect the reconfigurable nature of CGRAs, an architectural model of the system under test is built directly from the hardware description files. A guided place-and-routing algorithm is used to map operations and operands onto the heterogeneous processing elements (PE). Test coverage is maximized by favoring unexercised parts of the architecture. Requiring no explicit knowledge about the semantics of operations, the random test program generator (RTPG) framework seamlessly supports custom ISA extensions.