Modern programming languages offer special syntax and semantics for logical fork-join parallelism in the form of parallel loops, allowing them to be nested, e.g., a parallel loop within another parallel loop. This expressiveness comes at a price, however: on modern multicore systems, realizing logical parallelism results in overheads due to the creation and management of parallel tasks, which can wipe out the benefits of parallelism. Today, we expect application programmers to cope with it by manually tuning and optimizing their code. Such tuning requires programmers to reason about architectural factors hidden behind layers of software abstractions, such as task scheduling and load balancing. Managing these factors is particularly challenging when workloads are irregular because their performance is input-sensitive. This paper presents HBC, the first compiler that translates C/C++ programs with high-level, fork-join constructs (e.g., OpenMP) to binaries capable of automatically controlling the cost of parallelism and dealing with irregular, input-sensitive workloads. The basis of our approach is Heartbeat Scheduling, a recent proposal for automatic granularity control, which is backed by formal guarantees on performance. HBC binaries outperform OpenMP binaries for workloads for which even entirely manual solutions struggle to find the right balance between parallelism and its costs.

In the field of computational science, effectively supporting researchers necessitates a deep understanding of how they utilize computational resources. Building upon a decade-old survey that explored the practices and challenges of research computation, this study aims to bridge the understanding gap between providers of computational resources and researchers who rely on them. This study revisits key survey questions and gathers feedback on open-ended topics from over a hundred interviews. Quantitative analyses of present and past results illuminate the landscape of research computation. Qualitative analyses, including careful use of large language models, highlight trends and challenges with concrete evidence. Given the rapid evolution of computational science, this paper offers a toolkit with methodologies and insights to simplify future research and ensure ongoing examination of the landscape. This study, with its findings and toolkit, guides enhancements to computational systems, deepens understanding of user needs, and streamlines reassessment of the computational landscape.