Modern shared-memory parallel programming models, such as OpenMP and Cilk, enable developers to encode a parallel execution plan within their code. Existing compilers, including Clang and GCC, directly lower or add additional compatible parallelism on top of the developers’ plan. However, when better parallel execution plans exist that are incompatible with the original plan, compilers lack the capability of disregarding it and replacing it with a better one. To address this problem, this paper introduces the parallel-semantics program dependence graph (PS-PDG), an extension of the program dependence graph (PDG) abstraction that can simultaneously represent parallel semantics derived from both the developer’s original plan and the compiler’s own analysis. To demonstrate the power of PS-PDG, this paper also introduces GINO, an LLVM-based compiler capable of optimizing parallel execution plans using PS-PDG. Through exploring, reasoning, and implementing better parallel execution plans unlocked by PS-PDG, GINO outperforms the developer’s original parallel execution plan by 46.6% at most, and by 15% on average over 56 cores across 8 benchmarks from the NAS benchmark suite.

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.

Memory profiling captures programs’ dynamic memory behavior, assisting programmers in debugging, tuning, and enabling advanced compiler optimizations like speculation-based automatic parallelization. As each use case demands its unique program trace summary, various memory profiler types have been developed. Yet, designing practical memory profilers often requires extensive compiler expertise, adeptness in program optimization, and significant implementation effort. This often results in a void where aspirations for fast and robust profilers remain unfulfilled. To bridge this gap, this paper presents PROMPT, a framework for streamlined development of fast memory profilers. With PROMPT, developers need only specify profiling events and define the core profiling logic, bypassing the complexities of custom instrumentation and intricate memory profiling components and optimizations. Two state-of-the-art memory profilers were ported with PROMPT where all features preserved. By focusing on the core profiling logic, the code was reduced by more than 65% and the profiling overhead was improved by 5.3× and 7.1× respectively. To further underscore PROMPT’s impact, a tailored memory profiling workflow was constructed for a sophisticated compiler optimization client. In 570 lines of code, this redesigned workflow satisfies the client’s memory profiling needs while achieving more than 90% reduction in profiling overhead and improved robustness compared to the original profilers.

Modern and emerging architectures demand increasingly complex compiler analyses and transformations. As the emphasis on compiler infrastructure moves beyond support for peephole optimizations and the extraction of instruction-level parallelism, compilers should support custom tools designed to meet these demands with higher-level analysis-powered abstractions and functionalities of wider program scope. This paper introduces NOELLE, a robust open-source domain-independent compilation layer built upon LLVM providing this support. NOELLE extends abstractions and functionalities provided by LLVM enabling advanced, program-wide code analyses and transformations. This paper shows the power of NOELLE by presenting a diverse set of 11 custom tools built upon it.