Giuseppe Congiu

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As the number of client machines in high end computing clusters increases, the file system cannot keep up with the resulting volume of requests, using a centralized metadata server. This problem will be even more prominent with the advent of the exascale computing age. In this context, the centralized metadata server represents a bottleneck for the scaling(More)
The performance gap between processors and I/O represents a serious scalability limitation for applications running on computing clusters. Parallel file systems often provide mechanisms that allow programmers to disclose their I/O pattern knowledge to the lower layers of the I/O stack through a hints API. This information can be used by the file system to(More)
Collective I/O is a parallel I/O technique designed to deliver high performance data access to scientific applications running on high-end computing clusters. In collective I/O, write performance is highly dependent upon the storage system response time and limited by the slowest writer. The storage system response time in conjunction with the need for(More)
Before us, other works have used data prefetching to boost applications performance [1]-[8]. Our approach differs from these works since we do not rely on precise I/O pattern information to predict and prefetch every chunck of data in advance. Instead we use data prefetching to group many small requests in a few big ones, improving applications performance(More)
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