Alexander Hück

The MPI correctness benchmarks MPI-Corrbench and the MPI Bugs Initiative contain standardized test cases of correct and erroneous use of MPI, allowing MPI correctness tool developers to assess their tools’ performance and guide further development of their checking capabilities. Hence, the correctness benchmarks should encompass representative MPI (mis-)usage that mirrors real-world codes. To that end, we analyze the MPI usage of these correctness benchmarks at argument granularity and compare it to a previously collected data set of 96 HPC codes. This assessment measures the benchmarks’ proximity to real-world MPI usage patterns and offers insights for enhancing their coverage.

This study focuses on gaining insights into the usage of the Message-Passing Interface (MPI) in a large set of High-Performance Computing (HPC) codes by analyzing MPI function calls and their argument usage patterns. Previous work has focused on analyzing MPI feature usage by statically matching function calls. However, this approach does not reveal common argument-specific call patterns or cross-interactions between MPI functions. In particular, MPI exposes its internal data structures using handles, and users pass these handles to MPI constructor functions, e.g., to create custom communicators. Tracking the relevant MPI arguments of these constructors and cross-referencing them with other MPI calls in a target code can reveal common user interactions. These insights can be used to optimize, e.g., datatype construction at a library level or to extend MPI correctness debugging tools to verify correct construction of these data structures. To that end, we statically analyze codes to extract MPI function calls and their arguments, cross-reference them with other MPI calls, and provide statistics on common argument patterns and cross-use of MPI functions. We believe that these insights can guide further development within the MPI community to ultimately benefit users.

Hybrid MPI–OpenMP applications employ message-passing interface (MPI)-enabled process-level, distributed computations on many compute nodes in conjunction with OpenMP shared-memory, thread-level parallelism for the most efficient computation. This poses challenges on the dynamic MPI correctness tool MUST and TypeART, its memory allocation tracking sanitizer extension based on the LLVM compiler framework. In particular, at the thread-level granularity of a process, MPI calls and memory allocations, which are both tracked for our analysis, can occur concurrently. To correctly handle this situation, we: 1) extended our compiler extension to handle OpenMP and 2) introduced thread-safety mechanisms to our runtime libraries, thus keeping the tracking of data consistent and avoiding data races. Our approach exhibits acceptable runtime and memory overheads, both typically below 30%.