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

高端计算(HEC)设施的快速增长极大地促进了计算科学的进步。然而，端到端科学发现周期的持续加速依赖于存储、共享和分析由今天的超级计算机产生的TB和PB数据的能力。随着I/O系统和处理器/内存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更容易出现瓶颈。在该方案中，我们通过在HEC I/O堆栈的多个层(从高级科学数据库到二级存储设备和归档系统)进行自适应优化来解决I/O堆栈的性能问题，并提出了有效的通信方案来整合这些层间的优化。特别是，我们提出的PARIO(并行自适应I/O)框架探索了多层缓存/预取，以协调从处理器到磁带归档系统的各种存储资源。这种新颖的方法将在每个单独的层上连接现有的不连续的优化工作，并响应使用越来越深的HEC I/O堆栈来提高整体I/O系统性能的关键呼吁。

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.