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Successful HPC software applications are long-lived. When ported across machines and their compilers, these applications often produce different numerical results, many of which are unacceptable. Such variability is also a concern while optimizing the code more aggressively to gain performance. Efficient tools that help locate the program units (files and functions) within which most of the variability occurs are badly needed, both to plan for code ports and to root-cause errors due to variability when they happen in the field. In this work, we offer an enhanced version of the open-source testing framework FLiT to serve these roles. Key new features of FLiT include a suite of bisection algorithms that help locate the root causes of variability. Another added feature allows an analysis of the tradeoffs between performance and the degree of variability. Our new contributions also include a collection of case studies. Results on the MFEM finite-element library include variability/performance tradeoffs, and the identification of a (hitherto unknown) abnormal level of result-variability even under mild compiler optimizations. Results from studying the Laghos proxy application include identifying a significantly divergent floating-point result-variability and successful root-causing down to the problematic function over as little as 14 program executions. Finally, in an evaluation of 4,376 controlled injections of floating-point perturbations on the LULESH proxy application, we showed that the FLiT framework has 100% precision and recall in discovering the file and function locations of the injections all within an average of only 15 program executions. 

An approach to reproducibility problems related to porting software across machines and compilers. 

The Exascale Computing Project (ECP) focuses on the development of future exascale‐capable applications. Most ECP applications use the message passing interface (MPI) as their parallel programming model with mini‐apps serving as proxies. This paper explores the explicit usage of MPI in such ECP proxy applications. We empirically analyze 14 proxy applications from the ECP Proxy Apps Suite. We use the MPI profiling interface (PMPI) to collect MPI usage patterns in ECP proxy apps. Our analysis shows that a small subset of features from MPI is commonly used in the proxies of exascale‐capable applications, even when they reference third‐party libraries. This study is intended to provide a better understanding of the use of MPI in current exascale applications. The findings can help focus software investments made for exascale systems in the MPI middleware including optimization, fault‐tolerance, tuning, and hardware‐offload.

 

Scientists from many different fields have been developing Bulk‐Synchronous MPI applications to simulate and study a wide variety of scientific phenomena. Since failure rates are expected to increase in larger‐scale future HPC systems, providing efficient fault‐tolerance mechanisms for this class of applications is paramount. The global‐restart model has been proposed to decrease the time of failure recovery in Bulk‐Synchronous applications by allowing a fast reinitialization of MPI. However, the current implementations of this model have several drawbacks: they lack efficiency; their scalability have not been shown; and they require the use of the MPI profiling interface, which precludes the use of tools. In this paper, we present EReinit, an implementation of the global‐restart model that addresses these problems. Our key idea and optimization is the co‐design of basic fault‐tolerance mechanisms such as failure detection, notification, and recovery between MPI and the resource manager in contrast to current approaches on which these mechanisms are implemented in MPI only. We demonstrate EReinitin three HPC programs and show that it is up to four times more efficient than existing solutions at 4,096 processes.