Speaker
Description
Advanced light sources produce vast amounts of diverse, multi-modal data annually, with IO bottlenecks increasingly limiting scientific computational efficiency. To overcome this challenge, our approach introduces a threefold solution. First, we develop daisy-io, which has a unified IO interface designed for cross-disciplinary applications, which integrates accelerated data retrieval techniques such as parallel processing and prefetching to optimize access speeds across heterogeneous datasets. Second, we construct a data streaming platform that eliminates disk read/write bottlenecks through real-time data handling. This platform incorporates three core components: a stream ingestion module for dynamic data reception, a stream parsing module for on-the-fly structural processing, and a stream buffering module for temporary data staging. Finally, to further enhance data transmission efficiency, we implement a lightweight serialization protocol and domain-specific compression algorithms, minimizing latency and bandwidth demands. Collectively, these innovations not only accelerate data read/write operations but also abstract complexities arising from disparate data sources and formats, enabling seamless integration into scientific workflows while maintaining adaptability across experimental scenarios.
