BeeGFS

Abstract: Modern high-performance computing (HPC) systems are increasingly using large amounts of fast storage, such as solid-state drives (SSD), to accelerate disk access times. This approach has been exemplified in the design of “burst buffers”, but more general caching systems have also been built. This paper proposes extending an existing parallel file system to provide such a file caching layer. The solution unifies data access for both the internal storage and external file systems using a uniform namespace. It improves storage performance by exploiting data locality across storage tiers, and increases data sharing between compute nodes and across applications. Leveraging data striping and meta-data partitioning, the system supports high speed parallel I/O for data intensive parallel computing. Data consistency across tiers is maintained automatically using a cache aware access algorithm. A prototype has been built using BeeGFS to demonstrate rapid access to an underlying IBM Spectrum Scale file system. Performance evaluation demonstrates a significant improvement in the efficiency over an external parallel file system.

Abstract: Large scale geophysical modeling uses high performance computing systems to expedite the solutions of very large, complex systems. High disk latencies, low IOPS, and low read/write data transfer rates are relegating many numerical simulations to I/O bound jobs, where the run time is bound not by CPU rate, but by I/O rate. In this paper we seek to improve the I/O of two geophysical modeling applications and take full advantage of the parallel nature of the programs, as well as the file management system for the large output files. Parallelizing output for these programs is achieved using PnetCDF, a parallel implementation of the netCDF format, and BeeGFS, an open-source parallel file system. Using these solutions, we have significantly decreased the amount of time spent saving data to disk, and give analysis of the features used in relation to PnetCDF with BeeGFS I/O optimization.

Abstract: In modern HPC systems, parallel (distributed) file systems are used to allow fast access from and to the storage infrastructure. However, I/O performance in large-scale HPC systems has failed to keep up with the increase in computational power. As a result, the I/O subsystem which also has to cope with a large number of demanding metadata operations is often the bottleneck of the entire HPC system. In some cases, even a single bad behaving application can be held responsible for slowing down the entire HPC system, disrupting other applications that use the same I/O subsystem. These kinds of situations are likely to become more frequent in the future with larger and more powerful HPC systems. In this work, we present a simple solution for applications with very high I/O demands. Our proposed solution is to create a private parallel file system on-demand for an HPC job and use the node-local storage devices, e.g. solid-state-disks (SSD). We show that this feature is easy to add to an existing HPC environment and requires only minimal configuration to the system. We conclude that the impact on running applications is manageable and the advantages to applications that generate a high load outweigh the disadvantages. We show that in some cases applications may run slower, but the reduction of load on the global file system is prevailing in these cases.