Comparative I/O workload characterization of two leadership class storage clusters

The Oak Ridge Leadership Computing Facility (OLCF) is a leader in large-scale parallel file system development, design, deployment and continuous operation. For the last decade, the OLCF has designed and deployed two large center-wide parallel file systems. The first instantiation, Spider 1, served the Jaguar supercomputer and its predecessor, Spider 2, now serves the Titan supercomputer, among many other OLCF computational resources. The OLCF has been rigorously collecting file and storage system statistics from these Spider systems since their transition to production state. In this paper we present the collected I/O workload statistics from the Spider 2 system and compare it to the Spider 1 data. Our analysis show that the Spider 2 workload is more more write-heavy I/O compared to Spider 1 (75% vs. 60%, respectively). The data also show the OLCF storage policies such as periodic purges are effectively managing the capacity resource of Spider 2. Furthermore, due to improvements in tdm_multipath and ib_srp software, we are utilizing the Spider 2 system bandwidth and latency resources more effectively. The Spider 2 bandwidth usage statistics shows that our system is working within the design specifications. However, it is also evident that our scientific applications can be more effectively served by a burst buffer storage layer. All the data has been collected by monitoring tools developed for the Spider ecosystem. We believe the observed data set and insights will help us better design the next-generation Spider file and storage system. It will also be helpful to the larger community for building more effective large-scale file and storage systems.

[1]  Buddy Bland,et al.  Titan - Early experience with the Titan system at Oak Ridge National Laboratory , 2012, 2012 SC Companion: High Performance Computing, Networking Storage and Analysis.

[2]  John Shalf,et al.  Characterizing and predicting the I/O performance of HPC applications using a parameterized synthetic benchmark , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

[3]  Don E Maxwell,et al.  Monitoring Tools for Large Scale Systems , 2010 .

[4]  Galen M. Shipman,et al.  Workload characterization of a leadership class storage cluster , 2010, 2010 5th Petascale Data Storage Workshop (PDSW '10).

[5]  Alma Riska,et al.  Evaluation of disk-level workloads at different time-scales , 2009, 2009 IEEE International Symposium on Workload Characterization (IISWC).

[6]  Jerome A. Rolia,et al.  Workload Analysis and Demand Prediction of Enterprise Data Center Applications , 2007, 2007 IEEE 10th International Symposium on Workload Characterization.

[7]  Saurabh Gupta,et al.  Best Practices and Lessons Learned from Deploying and Operating Large-Scale Data-Centric Parallel File Systems , 2014, SC14: International Conference for High Performance Computing, Networking, Storage and Analysis.

[8]  Alma Riska,et al.  Evaluation of disk-level workloads at different time scales , 2009, PERV.

[9]  Robert Latham,et al.  Understanding and improving computational science storage access through continuous characterization , 2011, 2011 IEEE 27th Symposium on Mass Storage Systems and Technologies (MSST).

[10]  Feiyi Wang,et al.  OLCF ’ s 1 TB / s , Next-Generation Lustre File System , 2013 .

[11]  Anand Sivasubramaniam,et al.  Synthesizing Representative I/O Workloads for TPC-H , 2004, 10th International Symposium on High Performance Computer Architecture (HPCA'04).

[12]  Qi Zhang,et al.  Characterization of storage workload traces from production Windows Servers , 2008, 2008 IEEE International Symposium on Workload Characterization.

[13]  Karsten Schwan,et al.  Flexible IO and integration for scientific codes through the adaptable IO system (ADIOS) , 2008, CLADE '08.

[14]  Galen M. Shipman,et al.  The Spider Center Wide File System; From Concept to Reality , 2009 .