MPI errors are challenging to identify despite the significant number of expert verification tools. Dynamic tools (i.e., requiring profiling) are computationally expensive and accurate in error detection, whereas static analysis (i.e., operating at source code or compilation) is computationally cheap but less accurate. Interestingly, the recent success of AI and LLMs offers an alternative to increase static analysis accuracy while preserving its low overhead. Yet current methods remain difficult to benchmark, too general, and poorly adapted to the specific challenges of high-performance computing.

Identifying errors in parallel MPI programs is a challenging task. Despite the growing number of verification tools, debugging parallel programs remains a significant challenge. This paper is the first to utilize embedding and deep learning graph neural networks (GNNs) to tackle the issue of identifying bugs in MPI programs. Specifically, we have designed and developed two models that can determine, from a code’s LLVM Intermediate Representation (IR), whether the code is correct or contains a known MPI error.We tested our models using two dedicated MPI benchmark suites for verification: MBI and MPI-CorrBench. By training and validating our models on the same benchmark suite, we achieved a prediction accuracy of 92% in detecting error types. Additionally, we trained and evaluated our models on distinct benchmark suites (e.g., transitioning from MBI to MPI-CorrBench) and achieved a promising accuracy of over 80%. Finally, we investigated the interaction between different MPI errors and quantified our models generalization capabilities over new unseen errors. This involved removing errors types during training and assessing whether our models could still predict them. The detection accuracy of removed errors vary significantly between 20% to 80%, indicating connected error patterns.

There is a large space of NUMA and hardware prefetcher configurations that can significantly impact the performance of an application. Previous studies have demonstrated how a model can automatically select configurations based on the dynamic properties of the code to achieve speedups. This paper demonstrates how the static Intermediate Representation (IR) of the code can guide NUMA/prefetcher optimizations without the prohibitive cost of performance profiling. We propose a method to create a comprehensive dataset that includes a diverse set of intermediate representations along with optimum configurations. We then apply a graph neural network model in order to validate this dataset. We show that our static intermediate representation based model achieves 80 % of the performance gains provided by expensive dynamic performance profiling based strategies. We further develop a hybrid model that uses both static and dynamic information. Our hybrid model achieves the same gains as the dynamic models but at a reduced cost by only profiling 30 % of the programs.

We present a dynamic program analysis approach to optimize communication overlap in scientific applications. Our tool instruments the code to generate a trace of the application's memory and synchronization behavior. An offline analysis determines the program optimal points for maximal overlap when considering several programming constructs: nonblocking one-sided communication operations, non-blocking collectives and bespoke synchronization patterns and operations. Feedback about possible transformations is presented to the user and the tool can perform the directed transformations, which are supported by a lightweight runtime. The value of our approach comes from: 1) the ability to optimize across boundaries of software modules or libraries, while specializing for the intrinsics of the underlying communication runtime; and 2) providing upper bounds on the expected performance improvements after communication optimizations. We have reduced the time spent in communication by as much as 64% for several applications that were already aggressively optimized for overlap; this indicates that manual optimizations leave untapped performance. Although demonstrated mainly for the UPC programming language, the methodology can be easily adapted to any other communication and synchronization API.

Scientific applications mainly rely on the MPI parallel programming model to reach high performance on supercomputers. The advent of manycore architectures (larger number of cores and lower amount of memory per core) leads to mix MPI with a thread-based model like OpenMP. But integrating two different programming models inside the same application can be tricky and generate complex bugs. Thus, the correctness of hybrid programs requires a special care regarding MPI calls location. For example, identical MPI collective operations cannot be performed by multiple non-synchronized threads. To tackle this issue, this paper proposes a static analysis and a reduced dynamic instrumentation to detect bugs related to misuse of MPI collective operations inside or outside threaded regions. This work extends PARCOACH designed for MPI-only applications and keeps the compatibility with these algorithms. We validated our method on multiple hybrid benchmarks and applications with a low overhead.

MPI-3 provide functions for non-blocking collectives. To help programmers introduce non-blocking collectives to existing MPI programs, we improve the PARCOACH tool for checking correctness of MPI call sequences. These enhancements focus on correct call sequences of all flavor of collective calls, and on the presence of completion calls for all non-blocking communications. The evaluation shows an overhead under 10% of original compilation time.

Collective MPI communications have to be executed in the same order by all processes in their communicator and the same number of times, otherwise a deadlock occurs. As soon as the control-flow involving these collective operations becomes more complex, in particular including conditionals on process ranks, ensuring the correction of such code is error-prone. We propose in this paper a static analysis to detect when such situation occurs, combined with a code transformation that prevents from deadlocking. We show on several benchmarks the small impact on performance and the ease of integration of our techniques in the development process.