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.

Compiler research and development has treated computation as the primary driver of performance improvements in C/C++ programs, leaving memory optimizations as a secondary consideration. Developers are currently handed the arduous task of describing both the semantics and layout of their data in memory, either manually or via libraries, prematurely lowering high-level data collections to a low-level view of memory for the compiler. Thus, the compiler can only glean conservative information about the memory in a program, e.g., alias analysis, and is further hampered by heavy memory optimizations. This paper proposes the Memory Object Intermediate Representation (MEMOIR), a language-agnostic SSA form for sequential and associative data collections, objects, and the fields contained therein. At the core of Memoir is a decoupling of the memory used to store data from that used to logically organize data. Through its SSA form, Memoir compilers can perform element-level analysis on data collections, enabling static analysis on the state of a collection or object at any given program point. To illustrate the power of this analysis, we perform dead element elimination, resulting in a 26.6% speedup on mcf from SPECINT 2017. With the degree of freedom to mutate memory layout, our Memoir compiler performs field elision and dead field elimination, reducing peak memory usage of mcf by 20.8%.

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.