High-performance computing codes often combine the Message-Passing Interface (MPI) with a shared-memory programming model, e.g., OpenMP, for efficient computations. These so-called hybrid models may issue MPI calls concurrently from different threads at the highest level of MPI thread support. The correct use of either MPI or OpenMP can be complex and error-prone. The hybrid model increases this complexity even further. While correctness analysis tools exist for both programming paradigms, for hybrid models, a new set of potential errors exist, whose detection requires combining knowledge of MPI and OpenMP primitives. Unfortunately, correctness tools do not fully support the hybrid model yet, and their current capabilities are also hard to assess. In previous work, to enable structured comparisons of correctness tools and improve their coverage, we proposed the MPI-CorrBench test suite for MPI. Likewise, others proposed the DataRaceBench test suite for OpenMP. However, for the particular error classes of the hybrid model, no such test suite exists. Hence, we propose a hybrid MPI-OpenMP test suite to (1) facilitate the correctness tool development in this area and, subsequently, (2) further encourage the use of the hybrid model at the highest level of MPI thread support. To that end, we discuss issues with this hybrid model and the knowledge correctness tools need to combine w.r.t. MPI and OpenMP to detect these. In our evaluation of two state-of-the-art correctness tools, we see that for most cases of concurrent and conflicting MPI operations, these tools can cope with the added complexity of OpenMP. However, more intricate errors, where user code interferes with MPI, e.g., a data race on a buffer, still evade tool analysis.

The Message Passing Interface (MPI) is the de-facto standard for distributed memory computing in high-performance computing (HPC). To aid developers write correct MPI programs, different tools have been proposed, e.g., Intel Trace Analyzer and Collector (ITAC), MUST, Parcoach and MPI-Checker. Unfortunately, the effectiveness of these tools is hard to compare, as they have not been evaluated on a common set of applications. More importantly, well-known and widespread benchmarks, which tend to be well-tested and error free, were used for their evaluation. To enable a structured comparison and improve the coverage and reliability of available MPI correctness tools, we propose MPI-CorrBench as a common test harness. MPI-CorrBench enables a structured comparison of the different tools available w.r.t. various types of errors. In our evaluation, we use MPI-CorrBench to provide a well-defined set of error-cases to MUST, ITAC, Parcoach and MPI-Checker. In particular, we find that ITAC and MUST complement each other in many cases. In general, MUST works better for detecting type errors while ITAC is better in detecting errors in non-blocking operations. Although the most-used functions of MPI are well supported, MPI-CorrBench shows that for one sided communication, the error detection capability of all evaluated tools needs improvement. Moreover, our experiments reveal a MPI standard violation in the MPICH test suite as well as several cases of discouraged use of MPI functionality.

The size and complexity of high-performance computing applications present a serious challenge to manual reasoning about program behavior. The vastness and diversity of code bases often break automatic analysis tools, which could otherwise be used. As a consequence, developers resort to mini-apps, i.e., trimmed-down proxies of the original programs that retain key performance characteristics. Unfortunately, their construction is difficult and time consuming and prevents their mass production. In this paper, we propose a systematic and tool-supported approach to extract mini-apps from large-scale applications that reduces the manual effort needed to create them. Our approach covers the stages kernel identification, data capture, code extraction and representativeness validation. We demonstrate it using an astrophysics simulation with ≈ 8.5 million lines of code and extract a mini-app with only ≈ 1, 100 lines of code. For the mini-app, we evaluate the reduction of code complexity and execution similarity, and show how it enables the tool-supported discovery of unexploited parallelization opportunities, reducing the simulation’s runtime significantly.