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Failures have expensive implications in HPC (High-Performance Computing) systems. Consequently, effective diagnosis of system failures is desired to help improve system reliability from both a remedial and preventive perspective. As HPC systems conduct extensive logging of resource usage and system events, parsing this data is an oft advocated basis for failure diagnosis. However, the high levels of concurrency that exist in HPC systems cause system events to frequently interleave in time and, as such, certain interactions appear or become indirect. which will be missed by current failure diagnostics techniques. To help uncover such indirect interactions, in this paper, we develop a novel approach that leverages the concept of partial correlation. The novel failure diagnostics workflow -called IFADE -extracts partial correlation of resource use counters and partial correlation of system errors. As part of our contributions, we (a) compare our diagnostics approach with current ones, (b) identify two previously unknown causes of system failures, validated by system designers and (c) provide insights into Lustre I/O and segmentation faults. IFADE has been put on the public domain to support system administrators in failure diagnosis.

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Recent work have used both failure logs and resource use data separately (and together) to detect system failureinducing 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.