arXiv:2608. 00130v2 Announce Type: replace-cross Abstract: Fortran has been the cornerstone of high-performance computing for decades and remains unmatched in many domains.
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arXiv:2606. 07681v1 Announce Type: cross Abstract: Differentiable programming offers transformative capabilities for scientific modeling, enabling gradient-based parameter estimation, sensitivity analysis, and data assimilation.
arXiv:2606. 09930v1 Announce Type: cross Abstract: The boundary between program execution and gradient-based optimization has long limited the use of code itself as a learnable scientific model.
arXiv:2607. 19104v1 Announce Type: cross Abstract: Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question.
JLIR is a Julia-native intermediate representation inspired by MLIR that enables multi-level, dialect-oriented compilation within the Julia ecosystem. It allows Julia programs to be represented before low-level lowering, supports extensible operations and transformation passes via Julia’s language mechanisms, and keeps partially typed programs transformable until concrete types are known. The framework includes built‑in dialects for arithmetic, control flow, functions, structured loops, and memory operations, and can be extended with new domain operations without altering the core system. JLIR was demonstrated by automatically generating JACC kernels for accelerators.
The survey reviews how Large Language Models (LLMs) are being used in High‑Performance Computing (HPC) programming, covering code generation, parallelization, frameworks, evaluation, and broader challenges. It finds that general‑purpose LLMs perform adequately on serial and OpenMP‑style tasks but struggle with distributed MPI workloads, while domain‑specialized models achieve higher accuracy yet are limited in scope and evaluation. The authors argue that LLMs will not replace HPC experts soon but can act as powerful collaborators, provided richer datasets, integration with performance tools, rigorous evaluation, and governance are developed.
arXiv:2606. 04023v1 Announce Type: cross Abstract: While large language models (LLMs) have been extensively evaluated on code generation tasks for general-purpose programming and GPU-accelerated environments (e.