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The need for parallel task execution has been steadily growing in recent years since manufacturers mainly improve processor performance by scaling the number of installed cores instead of the frequency of processors. To make use of this potential, an essential technique to increase the parallelism of a program is to parallelize loops. However, a main restriction of available tools for automatic loop...
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 boost the application performance. Unfortunately,...
Improvements in hardware and software have enabled magnetic disks to become an alternative to tape in backup environments. Nevertheless, even considering its slow access times, tape is still part of most hierarchical storage management strategies. The main reasons are cost-effectiveness, long lifetimes, and that tape, continually improved, keeps up with magnetic disks in terms of capacity. The performance...
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 global synchronisation, required during every...
HPC systems contain an increasing number of components, decreasing the mean time between failures. Checkpoint mechanisms help to overcome such failures for long-running applications. A viable solution to remove the resulting pressure from the I/O backends is to deduplicate the checkpoints. However, there is little knowledge about the potential to save I/Os for HPC applications by using deduplication...
Despite many efforts to better utilize the potential of GPUs and CPUs, it is far from being fully exploited. Although many tasks can be easily sped up by using accelerators, most of the existing schedulers are not flexible enough to really optimize the resource usage of the complete system. The main reasons are (i) that each processing unit requires a specific program code and that this code is often...
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