Scaling Parallel I/O Performance through I/O Delegate and Caching System

Increasingly complex scientific applications require massive parallelism to achieve the goals of fidelity and high computational performance. Such applications periodically offload checkpointing data to file system for post-processing and program resumption. As a side effect of high degree of parallelism, I/O contention at servers doesn't allow overall performance to scale with increasing number of processors. To bridge the gap between parallel computational and I/O performance, we propose a portable MPI-IO layer where certain tasks, such as file caching, consistency control, and collective I/O optimization are delegated to a small set of compute nodes, collectively termed as I/O Delegate nodes. A collective cache design is incorporated to resolve cache coherence and hence alleviates the lock contention at I/O servers. By using popular parallel I/O benchmark and application I/O kernels, our experimental evaluation indicates considerable performance improvement with a small percentage of compute resources reserved for I/O.