Christian Bischof | Searcharxiv

arXiv2026

Allocation Tracking and Parameter Checking for Parallel Programming Models using Contracts

Correctness checking tools for High-Performance Computing programs are typically limited to specific parallel programming models such as MPI or OpenSHMEM. The CoVer framework previously addressed this by introducing a generic, contract-based approach that decoupled API requirements from the core tool. However, CoVer's effectiveness remains bounded by the expressiveness of its underlying contract language, restricting the types of errors it can verify. This paper presents an extension to the CoVer contract language designed to capture and check a broader range of error classes. Our extensions introduce generic parameter checking and allocation tracking, while keeping generality across both programming model and language. We evaluate these extensions and demonstrate that analysis accuracy remains consistent across multiple languages, reinforcing the framework's general applicability. While the additional runtime analyses naturally incur a performance overhead, these improvements greatly enhance CoVer's utility with a significant accuracy improvement.

cs.DC↗

arXiv2026

Extending Contract Verification for Parallel Programming Models to Fortran

High-performance computing often relies on parallel programming models such as MPI for distributed-memory systems. While powerful, these models are prone to subtle programming errors, leading to development of multiple correctness checking tools. However, these are often limited to C/C++ codes, tied to specific library implementations, or restricted to certain error classes. Building on our prior work with CoVer, a generic, contract-based verification framework for parallel programming models, we extend CoVer's applicability to Fortran, enabling static and dynamic analysis across multiple programming languages. We adapted language-specific contract definitions and modified the analyses to support both C/C++ and Fortran programs. Our evaluation demonstrates that the enhanced version preserves CoVer's analysis accuracy and even revealed a bug in the MPI-BugBench testing framework, underscoring the effectiveness of the approach. The Fortran port of CoVer turns out to be substantially more efficient than the state-of-the-art tool MUST, while maintaining generality across languages.

cs.DC↗

arXiv2026

Dynamic Contract Analysis for Parallel Programming Models

Parallel programming in high-performance computing depends on low-level APIs such as MPI, requiring users to manage synchronization and resources manually. Several correctness checking tools exist to help bug-free code development, though most target a single programming model, limiting their applicability. Our previous work, the static analysis tool CoVer, leverages a contract-based approach enabling users to specify custom error-checking rules and support emerging or unconventional programming models without requiring extensive new tooling. However, static analysis cannot fully reason about runtime-dependent behavior such as pointer aliasing or indirect control flow. To address this, we present CoVer-Dynamic, a dynamic analysis extension that reuses CoVer's contract language to provide a unified static-dynamic verification framework. By enforcing the same contracts at runtime, CoVer-Dynamic improves classification accuracy and eliminates false positives on standardized MPI and OpenSHMEM benchmarks, while detecting errors beyond static analysis only. Our evaluation shows that CoVer-Dynamic consistently outperforms the state-of-the-art correctness checker MUST, averaging a 2x speedup. Finally, our results show limitations in the expressiveness of the contract language, motivating future work to support more error classes.

cs.DC↗

arXiv2025

cfdSCOPE: A Fluid-Dynamics Proxy App for Teaching Performance Engineering

Teaching performance engineering in high-performance computing (HPC) requires example codes that demonstrate bottlenecks and enable hands-on optimization. However, existing HPC applications and proxy apps often lack the balance of simplicity, transparency, and optimization potential needed for effective teaching. To address this, we developed cfdSCOPE, a compact, open-source computational fluid dynamics (CFD) proxy app specifically designed for educational purposes. cfdSCOPE simulates flow in a 3D volume using sparse linear algebra, a common HPC workload, and comprises fewer than 1,100 lines of code. Its minimal dependencies and transparent design ensure students can fully control and optimize performance-critical aspects, while its naive OpenMP parallelization provides significant optimization opportunities, thus making it an ideal tool for teaching performance engineering.

cs.DC↗

arXiv2023

A pragmatic workflow for research software engineering in computational science

University research groups in Computational Science and Engineering (CSE) generally lack dedicated funding and personnel for Research Software Engineering (RSE), which, combined with the pressure to maximize the number of scientific publications, shifts the focus away from sustainable research software development and reproducible results. The neglect of RSE in CSE at University research groups negatively impacts the scientific output: research data - including research software - related to a CSE publication cannot be found, reproduced, or re-used, different ideas are not combined easily into new ideas, and published methods must very often be re-implemented to be investigated further. This slows down CSE research significantly, resulting in considerable losses in time and, consequentially, public funding. We propose a RSE workflow for Computational Science and Engineering (CSE) that addresses these challenges, that improves the quality of research output in CSE. Our workflow applies established software engineering practices adapted for CSE: software testing, result visualization, and periodical cross-linking of software with reports/publications and data, timed by milestones in the scientific publication process. The workflow introduces minimal work overhead, crucial for university research groups, and delivers modular and tested software linked to publications whose results can easily be reproduced. We define research software quality from a perspective of a pragmatic researcher: the ability to quickly find the publication, data, and software related to a published research idea, quickly reproduce results, understand or re-use a CSE method, and finally extend the method with new research ideas.

cs.SE↗

arXiv2023

Runtime-Adaptable Selective Performance Instrumentation

Automated code instrumentation, i.e. the insertion of measurement hooks into a target application by the compiler, is an established technique for collecting reliable, fine-grained performance data. The set of functions to instrument has to be selected with care, as instrumenting every available function typically yields too large a runtime overhead, thus skewing the measurement. No "one-suits-all" selection mechanism exists, since the instrumentation decision is dependent on the measurement objective, the limit for tolerable runtime overhead and peculiarities of the target application. The Compiler-assisted Performance Instrumentation (CaPI) tool assists in creating such instrumentation configurations, by enabling the user to combine different selection mechanisms as part of a configurable selection pipeline, operating on a statically constructed whole-program call-graph. Previously, CaPI relied on a static instrumentation workflow which made the process of refining the initial selection quite cumbersome for large-scale codes, as the application had to be recompiled after each adjustment. In this work, we present new runtime-adaptable instrumentation capabilities for CaPI which do not require recompilation when instrumentation changes are made. To this end, the XRay instrumentation feature of the LLVM compiler was extended to support the instrumentation of shared dynamic objects. An XRay-compatible runtime system was added to CaPI that instruments selected functions at program start, thereby significantly reducing the required time for selection refinements. Furthermore, an interface to the TALP tool for recording parallel efficiency metrics was implemented, alongside a specialized selection module for creating suitable coarse-grained region instrumentations.

cs.PF↗

arXiv2022

A Research Software Engineering Workflow for Computational Science and Engineering

University research groups in Computational Science and Engineering (CSE) generally lack dedicated funding and personnel for Research Software Engineering (RSE), which, combined with the pressure to maximize the number of scientific publications, shifts the focus away from sustainable research software development and reproducible results. The neglect of RSE in CSE at University research groups negatively impacts the scientific output: research data - including research software - related to a CSE publication cannot be found, reproduced, or re-used, different ideas are not combined easily into new ideas, and published methods must very often be re-implemented to be investigated further. This slows down CSE research significantly, resulting in considerable losses in time and, consequentially, public funding. We propose a RSE workflow for Computational Science and Engineering (CSE) that addresses these challenges, that improves the quality of research output in CSE. Our workflow applies established software engineering practices adapted for CSE: software testing, result visualization, and periodical cross-linking of software with reports/publications and data, timed by milestones in the scientific publication process. The workflow introduces minimal work overhead, crucial for university research groups, and delivers modular and tested software linked to publications whose results can easily be reproduced. We define research software quality from a perspective of a pragmatic researcher: the ability to quickly find the publication, data, and software related to a published research idea, quickly reproduce results, understand or re-use a CSE method, and finally extend the method with new research ideas.

cs.SE↗