An Adaptive Cache Coherence Protocol Specification for Parallel Input/Output Systems
IEEE Transactions on Parallel and Distributed Systems
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Many parallel scientific applications need high-performance I/O. Unfortunately, end-to-end parallel-I/O performance has not been able to keep up with substantial improvements in parallel-I/O hardware because of poor parallel file-system software. Many radical changes, both at the interface level and the implementation level, have recently been proposed. One such proposed interface is {\em collective I/O}, which allows parallel jobs to request transfer of large contiguous objects in a single request, thereby preserving useful semantic information that would otherwise be lost if the transfer were expressed as per-processor non-contiguous requests. Kotz has proposed {\em disk-directed I/O} as an efficient implementation technique for collective-I/O operations, where the compute processors make a single collective data-transfer request, and the I/O processors thereafter take full control of the actual data transfer, exploiting their detailed knowledge of the disk-layout to attain substantially improved performance. Recent parallel file-system usage studies show that writes to write-only files are a dominant part of the workload. Therefore, optimizing writes could have a significant impact on overall performance. In this paper, we propose ENWRICH, a compute-processor write-caching scheme for write-only files in parallel file systems. ENWRICH combines low-overhead write caching at the compute processors with high performance disk-directed I/O at the I/O processors to achieve both low latency and high bandwidth. This combination facilitates the use of the powerful disk-directed I/O technique independent of any particular choice of interface. By collecting writes over many files and applications, ENWRICH lets the I/O processors optimize disk I/O over a large pool of requests. We evaluate our design via simulated implementation and show that ENWRICH achieves high performance for various configurations and workloads.