Ad Hoc File Systems for High-Performance Computing
Storage backends of parallel compute clusters are still based mostly on magnetic disks, while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory (NVRAM) are deployed within compute nodes. Including these new storage technologies into scientific workfl...
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| Abstract | Storage backends of parallel compute clusters are still based mostly on magnetic disks, while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory (NVRAM) are deployed within compute nodes. Including these new storage technologies into scientific workflows is unfortunately today a mostly manual task, and most scientists therefore do not take advantage of the faster storage media. One approach to systematically include nodelocal SSDs or NVRAMs into scientific workflows is to deploy ad hoc file systems over a set of compute nodes, which serve as temporary storage systems for single applications or longer-running campaigns. This paper presents results from the Dagstuhl Seminar 17202 “Challenges and Opportunities of User-Level File Systems for HPC” and discusses application scenarios as well as design strategies for ad hoc file systems using node-local storage media. The discussion includes open research questions, such as how to couple ad hoc file systems with the batch scheduling environment and how to schedule stage-in and stage-out processes of data between the storage backend and the ad hoc file systems. Also presented are strategies to build ad hoc file systems by using reusable components for networking and how to improve storage device compatibility. Various interfaces and semantics are presented, for example those used by the three ad hoc file systems BeeOND, GekkoFS, and BurstFS. Their presentation covers a range from file systems running in production to cutting-edge research focusing on reaching the performance limits of the underlying devices. |
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| AbstractList | Storage backends of parallel compute clusters are still based mostly on magnetic disks, while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory (NVRAM) are deployed within compute nodes. Including these new storage technologies into scientific workflows is unfortunately today a mostly manual task, and most scientists therefore do not take advantage of the faster storage media. One approach to systematically include nodelocal SSDs or NVRAMs into scientific workflows is to deploy ad hoc file systems over a set of compute nodes, which serve as temporary storage systems for single applications or longer-running campaigns. This paper presents results from the Dagstuhl Seminar 17202 "Challenges and Opportunities of User-Level File Systems for HPC" and discusses application scenarios as well as design strategies for ad hoc file systems using node-local storage media. The discussion includes open research questions, such as how to couple ad hoc file systems with the batch scheduling environment and how to schedule stage-in and stage-out processes of data between the storage backend and the ad hoc file systems. Also presented are strategies to build ad hoc file systems by using reusable components for networking and how to improve storage device compatibility. Various interfaces and semantics are presented, for example those used by the three ad hoc file systems BeeOND, GekkoFS, and BurstFS. Their presentation covers a range from file systems running in production to cutting-edge research focusing on reaching the performance limits of the underlying devices. Keywords parallel architectures, distributed file system, high-performance computing, burst buffer, POSIX (portable operating system interface) Storage backends of parallel compute clusters are still based mostly on magnetic disks, while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory (NVRAM) are deployed within compute nodes. Including these new storage technologies into scientific workflows is unfortunately today a mostly manual task, and most scientists therefore do not take advantage of the faster storage media. One approach to systematically include node-local SSDs or NVRAMs into scientific workflows is to deploy ad hoc file systems over a set of compute nodes, which serve as temporary storage systems for single applications or longer-running campaigns. This paper presents results from the Dagstuhl Seminar 17202"Challenges and Opportunities of User-Level File Systems for HPC"and discusses application scenarios as well as design strategies for ad hoc file systems using node-local storage media. The discussion includes open research questions, such as how to couple ad hoc file systems with the batch scheduling environment and how to schedule stage-in and stage-out processes of data between the storage backend and the ad hoc file systems. Also presented are strategies to build ad hoc file systems by using reusable components for networking and how to improve storage device compatibility. Various interfaces and semantics are presented, for example those used by the three ad hoc file systems BeeOND, GekkoFS, and BurstFS. Their presentation covers a range from file systems running in production to cutting-edge research focusing on reaching the performance limits of the underlying devices. Storage backends of parallel compute clusters are still based mostly on magnetic disks, while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory (NVRAM) are deployed within compute nodes. Including these new storage technologies into scientific workflows is unfortunately today a mostly manual task, and most scientists therefore do not take advantage of the faster storage media. One approach to systematically include nodelocal SSDs or NVRAMs into scientific workflows is to deploy ad hoc file systems over a set of compute nodes, which serve as temporary storage systems for single applications or longer-running campaigns. This paper presents results from the Dagstuhl Seminar 17202 “Challenges and Opportunities of User-Level File Systems for HPC” and discusses application scenarios as well as design strategies for ad hoc file systems using node-local storage media. The discussion includes open research questions, such as how to couple ad hoc file systems with the batch scheduling environment and how to schedule stage-in and stage-out processes of data between the storage backend and the ad hoc file systems. Also presented are strategies to build ad hoc file systems by using reusable components for networking and how to improve storage device compatibility. Various interfaces and semantics are presented, for example those used by the three ad hoc file systems BeeOND, GekkoFS, and BurstFS. Their presentation covers a range from file systems running in production to cutting-edge research focusing on reaching the performance limits of the underlying devices. |
| Audience | Academic |
| Author | Brinkmann, André Carns, Philip Cortes, Toni Klasky, Scott A. Pfreundt, Franz-Josef Ross, Robert B. Mohror, Kathryn Vef, Marc-André Yu, Weikuan Miranda, Alberto |
| AuthorAffiliation | Zentrum für Datenverarbeitung, Johannes Gutenberg University Mainz, Mainz 55128, Germany%Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA 94550, U.S.A.%Department of Computer Science, Florida State University, Tallahassee, FL 32306, U.S.A.%Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439, U.S.A.%Department of Computer Architecture, Universitat Politecnica de Catalunya, Barcelona 08034, Spain%Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, U.S.A.%Computer Science Department, Barcelona Supercomputing Center, Barcelona 08034, Spain%Fraunhofer Institute for Industrial Mathematics ITWM, Fraunhofer-Platz 1, Kaiserslautern 67663, Germany |
| AuthorAffiliation_xml | – name: Zentrum für Datenverarbeitung, Johannes Gutenberg University Mainz, Mainz 55128, Germany%Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA 94550, U.S.A.%Department of Computer Science, Florida State University, Tallahassee, FL 32306, U.S.A.%Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439, U.S.A.%Department of Computer Architecture, Universitat Politecnica de Catalunya, Barcelona 08034, Spain%Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, U.S.A.%Computer Science Department, Barcelona Supercomputing Center, Barcelona 08034, Spain%Fraunhofer Institute for Industrial Mathematics ITWM, Fraunhofer-Platz 1, Kaiserslautern 67663, Germany |
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| ContentType | Journal Article |
| Copyright | Institute of Computing Technology, Chinese Academy of Sciences & Springer Nature Singapore Pte Ltd. 2020 COPYRIGHT 2020 Springer Institute of Computing Technology, Chinese Academy of Sciences & Springer Nature Singapore Pte Ltd. 2020. Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DOI | 10.1007/s11390-020-9801-1 |
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| GrantInformation_xml | – fundername: This work has also been partially funded by the German Research Foundation through the German Priority Programme 1648"Software for Exascale Computing"; the ADA-FS project, and by the European Union's Horizon 2020 Research and Innovation Program under the NEXTGenIO Project under Grant No. 671591, the Spanish Ministry of Science and Innovation under Contract No. TIN2015-65316, and the Generalitat de Catalunya under Contract No. 2014-SGR-1051. This wo; also supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under Contract No. DE-AC02-06CH11357. This work is also supported in part by the National Science Foundation of USA under Grant Nos. 1561041, 1564647, 1744336, 1763547, and 1822737 funderid: (DFG) through the German Priority Programme 1648"Software for Exascale Computing"; (LLNL-JRNL-779789); also supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under Contract No. DE-AC02-06CH11357. This work is also supported in part by the National Science Foundation of USA under Grant Nos. 1561041, 1564647, 1744336, 1763547, and 1822737 |
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