Collaborative Research: Application-adaptive I/O Stack for Data-intensive Scientific Computing

高端计算（HEC）设施的快速增长大大加速了计算科学的进步。然而，端到端科学发现周期的持续加速依赖于存储、共享和分析当今超级计算机生成的TB和PB级数据的能力。随着I/O系统和处理器/存储器单元之间的性能差距越来越大，数据存储和访问不可避免地变得越来越容易出现数据校验。在这个建议中，我们解决的I/O堆栈性能问题，自适应优化在多个层次的HEC I/O堆栈（从高层次的科学数据库，二级存储设备和归档系统），并提出有效的通信方案，跨层集成这样的优化。特别是，我们提出的PATIO（并行AdapTive I/O）框架探讨了多层缓存/预取，协调存储资源，从处理器到磁带归档系统。这种新颖的方法将在每个单独的层上桥接现有的不相交的优化工作，并响应通过越来越深的HEC I/O堆栈来提高整体I/O系统性能的关键呼叫。

Advances in computational sciences have been greatly accelerated by the rapid growth of high-end computing (HEC) facilities. However, the continuous speedup of end-to-end scientific discovery cycles relies on the ability to store, share, and analyze the terabytes and petabytes of data generated by today's supercomputers. With the growing performance gap between I/O systems and processor/memory units, data storage and accesses are inevitably becoming more bottleneck-prone. In this proposal, we address the I/O stack performance problem with adaptive optimizations at multiple layers of the HEC I/O stack (from high-level scientific data libraries to secondary storage devices and archiving systems), and propose effective communication schemes to integrate such optimizations across layers. In particular, our proposed PATIO (Parallel AdapTive I/O) framework explores multi-layer caching/prefetching that coordinates storage resources ranging from processors to tape archiving systems. This novel approach will bridge existing disjoint optimization efforts at each individual layer and responds to the critical call of improving the overall I/O system performance with increasingly deep HEC I/O stacks.