2011/05/01 by Yong Chen, Xian‐He Sun, Rajeev Thakur +2 · 1 citation
Computer Science · #Advanced Data Storage Technologies #Parallel Computing and Optimization Techniques #Distributed and Parallel Computing Systems #Computer science #Parallel I/O #Concurrency #Exploit #File system #Parallel computing #Locality #Latency (audio) #Distributed computing #Concurrent computing #Concurrency control #Operating system #Database transaction #Database
paper · doi:10.1109/ipdps.2011.79
openalex publication_date 2011/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Parallel applications benefit considerably from the rapid advance of processor architectures and the available massive computational capability, but their performance suffers from large latency of I/O accesses. The poor I/O performance has been attributed as a critical cause of the low sustained performance of parallel systems. Collective I/O is widely considered a critical solution that exploits the correlation among I/O accesses from multiple processes of a parallel application and optimizes the I/O performance. However, the conventional collective I/O strategy makes the optimization decision based on the logical file layout to avoid multiple file system calls and does not take the physical data layout into consideration. On the other hand, the physical data layout in fact decides the actual I/O access locality and concurrency. In this study, we propose a new collective I/O strategy that is aware of the underlying physical data layout. We confirm that the new Layout-Aware Collective I/O (LACIO) improves the performance of current parallel I/O systems effectively with the help of noncontiguous file system calls. It holds promise in improving the I/O performance for parallel systems.