Holger Pirk from Imperial College London is developing a novel approach to data management system composition called BOSS. The system uses a homoiconic representation of data and code and partial evaluation of queries by components, drawing inspiration from compiler-construction research. BOSS achieves a fully composable design that effectively combines different data models, hardware platforms, and processing engines, enabling features like GPU acceleration and generative data cleaning with minimal overhead. Why it matters: This research on composable database systems can broaden the applicability of data management techniques in the GCC region, enabling more flexible and efficient data processing for various applications.
A Duke University professor presented a data-centric approach to optimizing AI systems by addressing the memory capacity and bandwidth bottleneck. The presentation covered collaborative optimization across algorithms, systems, architecture, and circuit layers. It also explored compute-in-memory as a solution for integrating computation and memory. Why it matters: Optimizing AI systems through a data-centric approach can improve efficiency and performance, critical for advancing AI applications in the region.
A presentation discusses using programmable network devices to reduce communication bottlenecks in distributed deep learning. It explores in-network aggregation and data processing to lower memory needs and increase bandwidth usage. The talk also covers gradient compression and the potential role of programmable NICs. Why it matters: Optimizing distributed deep learning infrastructure is critical for scaling AI model training in resource-constrained environments.
The paper introduces Duet, a hybrid neural relation understanding method for cardinality estimation. Duet addresses limitations of existing learned methods, such as high costs and scalability issues, by incorporating predicate information into an autoregressive model. Experiments demonstrate Duet's efficiency, accuracy, and scalability, even outperforming GPU-based methods on CPU.
Prof. Chun Jason Xue from the City University of Hong Kong presented research on optimizing mobile memory and storage by analyzing mobile application characteristics, noting their differences from server applications. The research explores system software designs inherited from the Linux kernel and identifies optimization opportunities in mobile memory and storage management. Xue's work aims to enhance user experience on mobile devices through mobile application characterization, focusing on non-volatile and flash memories. Why it matters: Optimizing mobile systems based on the unique characteristics of mobile applications can significantly improve device performance and user experience in the region.