Recent work have used both failure logs and resource use data separately (and together) to detect system failure-inducing errors and to diagnose system failures. System failure occurs as a result of error propagation and the (unsuccessful) execution of error recovery mechanisms. Knowledge of error propagation patterns and unsuccessful error recovery is important for more accurate and detailed failure diagnosis, and knowledge of recovery protocols deployment is important for improving system reliability. This paper presents the CORRMEXT framework which carries failure diagnosis another significant step forward by analyzing and reporting error propagation patterns and degrees of success and failure of error recovery protocols. CORRMEXT uses both error messages and resource use data in its analyses. Application of CORRMEXT to data from the Ranger supercomputer have produced new insights. CORRMEXT has: (i) identified correlations between resource use counters that capture recovery attempts after an error, (ii) identified correlations between error events to capture error propagation patterns within the system, (iii) identified error propagation and recovery paths during system execution to explain system behaviour, (iv) showed that the earliest times of change in system behaviour can only be identified by analyzing both the correlated resource use counters and correlated errors. CORRMEXT will be installed on the HPC clusters at the Texas Advanced Computing Center in Autumn 2017.

With the growth of data in science and engineering fields and the I/O intense technologies used to carry out research with these massive datasets, it has become clear new solutions to support data research is required. In support of this, the Texas Advanced Computing Center presents Wrangler, the first open science research platform built from the ground up in support of data. Wrangler features a replicated 10 PB Lustre based parallel file system, compute capacity of 120 Intel Haswell nodes and 15 TB of RAM. In addition to the base system, Wrangler features a unique NAND flash-based storage system from DSSD, providing users with 0.5 PB of storage 1 TB/s bandwidth and 250 million IOP/s across the cluster. Supporting Hadoop, but not just Hadoop, Wrangler will provide current and future researchers with an environment supporting the most I/O intensive workflows in fields from astronomy to paleontology. With data at the forefront of Wrangler's mission, support for ETL workflows, data curation, and data publication will enable users as they both discover new results and publish their own research. Support for both SQL and noSQL databases and GIS based extensions will also be provided, allowing users to leverage these tools for both data cataloging and cross-study integration. Wrangler will allow users to focus more on what is most important to them, the data and knowledge gained from its analysis, and less on the details of curation and I/O optimization.

A goal for the analysis of supercomputer logs is to establish causal relationships among events which reflect significant state changes in the system. Establishing these relationships is at the heart of failure diagnosis. In principle, a log analysis tool could automate many of the manual steps systems administrators must currently use to diagnose system failures. However, supercomputer logs are unstructured, incomplete and contain considerable ambiguity so that direct discovery of causal relationships is difficult. This paper describes the second generation FDiag log-based failure diagnostics framework that provides automation of the manual failure diagnosis process and determines with high confidence, the likely cause of the failure, the components involved and the event sequences which contain the times of the causal and terminal events. FDiag extracts relevant events from the system logs, performs correlation analysis on these events and from these correlations determines the components involved and the event sequences. The diagnostics capabilities of FDiag are validated by comparing its assessments on known instances of recurrent failures on the Ranger supercomputer at the University of Texas at Austin. We believe FDiag is the first log analyzer to demonstrate this level of diagnostics capability from the system logs of an open source software stack incorporating Linux and the Lustre file system. FDiag will be put into production use for support of failure diagnosis on Ranger in September, 2011.