Efficient Computing and Storage

Arbeitsgruppe Brinkmann / Research group Brinkmann

 

High performance computing (HPC) is currently reaching the exascale level and compute clusters will combine the performance of millions of cores to perform more than 1018 operations per second soon. Additionally to the challenges resulting from going to exascale computing, HPC is in the transition from being compute-centric to being data-centric. The management and handling of data become more and more important, and it will be crucial to scale data capacity and data bandwidth.

Our group Efficient Computing and Storage at the Johannes Gutenberg University Mainz is focusing on the areas storage systems and scalable computing.

PetabyteWe are focusing both on block and file level storage. We are developing protocols and architectures, which are able to efficiently use the underlying storage medias and integrate these architectures within scalable environments. New storage technologies, like solid state disks (SSDs) and non-volatile main memory (NVMM), are integrated within these environments and help us to deliver optimized storage systems, e.g., in the context of parallel file systems, data deduplication, and backup.

Combining the performance of accelerators and processors is investigated in the context of different HPC codes and our extensions to these codes and to HPC middleware environments help applications to better utilize CPU and GPU computing resources, while improving energy efficiency is also investigated in the context of Cloud Computing, which helps to simplify the access to scientific applications.

Most recent publications

2024

  • Eric Borba, Reza Salkhordeh, Salim Mimouni, Eduardo Tavares, Paulo Maciel, Hossein Asadi, and André Brinkmann. 2024. A Hierarchical Modeling Approach for Assessing the Reliability and Performability of Burst Buffers. In Proceedings of the 37th GI/IT International Conference on Architecture of Computing Systems (ARCS), Potsdam, Germany, May 14th - 16th.
  • Hongming Huang, Peng Wang, Qiang Su, Hong Xu, Chun Jason Xue, and André Brinkmann. 2024. Palantir: Hierarchical Similarity Detection for Post-Deduplication Delta Compression. In Proceedings of the ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Volume 2, La Jolla, CA, USA, 27 April 2024- 1 May, 830–845. DOI
  • Shunkang Zhang, Ji Qi, Xin Yao, and André Brinkmann. 2024. Hyper: Achieving High-Performance and Memory-Efficient Learned Index Via Hybrid Construction. In To appear in International Conference on Management of Data (SIGMOD), Santiago, Chile, June 09 - 15.
  • Yingjin Qian, Marc-André Vef, Patrick Farrell, Andreas Dilger, Xi Li, Shuichi Ihara, Yinjin Fu, Wei Xue, and André Brinkmann. 2024. Combining Buffered I/O and Direct I/O in Distributed File Systems. In Proceedings of the 22nd USENIX Conference on File and Storage Technologies (FAST), Santa Clara, CA, USA, February 27-29, 17–33. Author/Publisher URL

2023

  • Nafiseh Moti, André Brinkmann, Marc-André Vef, Philippe Deniel, Jesus Carretero, Philip Carns, Jean-Thomas Acquaviva, and Reza Salkhordeh. 2023. The I/O Trace Initiative: Building a Collaborative I/O Archive to Advance HPC. In Proceedings of the SC ’23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. DOI Author/Publisher URL