There is growing interest in Secure Collaborative Analytics, but fully oblivious query execution in Secure Multi-Party Computation (MPC) settings is prohibitively expensive. Recent related works proposed different approaches to trimming the size of intermediate results between oblivious query operators, resulting in significant speedups at the cost of some controlled information leakage. In Reflex, we generalize these ideas into a flexible and efficient trimming method for the output of the oblivious operators, that we call Resizer. Resizers can be seamlessly integrated between MPC-based query operators. This allows for precisely controlling the security/performance trade-off on a per-operator and per-query basis. Our method has the potential to accelerate the performance of current oblivious query execution by up to 200 times compared to fully oblivious query execution, and by approximately 7 times compared to existing approaches with the same security guarantees. Our work lays down the foundation for a future MPC query planner that can pick different performance and security targets when composing physical plans. This demonstration showcases the benefits of Reflex. More precisely, it focuses on the integration of our proposed resizers into the oblivious query plan, significantly enhancing performance. Conference attendees will have the opportunity to observe the efficient trimming of intermediate results and, additionally, they will be able to configure the oblivious execution settings, ranging from fully oblivious to fully revealed. This hands-on experience will highlight the benefits of our proposal in various obliviousness scenarios.

Modern databases embrace weak isolation levels to cater for highly available transactions. However, weak isolation bugs have recently manifested in many production databases. This raises the concern of whether database implementations actually deliver their promised isolation guarantees in practice. In this paper we present Plume, the first efficient, complete, black-box checker for weak isolation levels. Plume builds on modular, fine-grained, transactional anomalous patterns, with which we establish sound and complete characterizations of representative weak isolation levels, including read committed, read atomicity, and transactional causal consistency. Plume leverages a novel combination of two techniques, vectors and tree clocks, to accelerate isolation checking. Our extensive assessment shows that Plume can reproduce all known violations in a large collection of anomalous database execution histories, detect new isolation bugs in three production databases along with informative counterexamples, find more weak isolation anomalies than the state-of-the-art checkers, and efficiently validate isolation guarantees under a wide variety of workloads.

Transactional isolation is critical to the functional correctness of database management systems (DBMSs). Much effort has recently been devoted to finding isolation bugs and validating isolation fulfilment in production DBMSs. However, there are still challenges that existing isolation checkers have not yet fully addressed. For instance, they may overlook bugs, incur high checking overhead, and return hard-to-understand counterexamples. We present IsoVista, the first black-box isolation checking system that encompasses all the following features. It builds on faithful characterizations of a range of isolation levels, ensuring the absence of both false positives and missed bugs in collected DBMS execution histories. IsoVista exhibits superior checking efficiency, compared to the state-of-the-art, and visualizes violation scenarios, facilitating the understanding of bugs found. It also supports profiling and benchmarking the performance of isolation checkers under various workloads, assisting developers of both DBMSs and checkers. We showcase all these features through user-friendly interfaces.

This paper reports our efforts on refactoring and optimizing the Community Atmosphere Model (CAM) on the Sunway TaihuLight supercomputer, which uses a many-core processor that consists of management processing elements (MPEs) and clusters of computing processing elements (CPEs). To map the large code base of CAM to the millions of cores on the Sunway system, we take OpenACC-based refactoring as the major approach, and apply source-to-source translator tools to exploit the most suitable parallelism for the CPE cluster, and to fit the intermediate variable into the limited on-chip fast buffer. For individual kernels, when comparing the original ported version using only MPEs and the refactored version using both the MPE and CPE clusters, we achieve up to 22× speedup for the compute-intensive kernels. For the 25km resolution CAM global model, we manage to scale to 24,000 MPEs, and 1,536,000 CPEs, and achieve a simulation speed of 2.81 model years per day.