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Duke University Department of Computer Science Technical Report


Department of Computer Science


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. \par 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.


Technical Report by the Department of Computer Science, Duke University.