As generative Artificial Intelligence (AI) technologies evolve, they offer
unprecedented potential to automate and enhance various tasks, including
coding. Natural Language-Oriented Programming (NLOP), a vision introduced in
this paper, harnesses this potential by allowing developers to articulate
software requirements and logic in their natural language, thereby
democratizing software creation. This approach streamlines the development
process and significantly lowers the barrier to entry for software engineering,
making it feasible for non-experts to contribute effectively to software
projects. By simplifying the transition from concept to code, NLOP can
accelerate development cycles, enhance collaborative efforts, and reduce
misunderstandings in requirement specifications. This paper reviews various
programming models, assesses their contributions and limitations, and
highlights that natural language will be the new programming language. Through
this comparison, we illustrate how NLOP stands to transform the landscape of
software engineering by fostering greater inclusivity and innovation.

通过自然语言导向的编程 (NLOP) 将软件需求和逻辑表达为自然语言，该方法简化了开发过程，降低了非专业人士参与软件项目的门槛，并加速了开发周期、增强了协作效果，并减少了需求规范中的误解。通过对各种编程模型的评估，本文强调自然语言将成为新的编程语言，并说明了 NLOP 如何促进软件工程的包容性和创新，从而改变软件工程的格局。

自然语言导向的编程（NLOP）：走向软件创造的民主化

Natural Language-Oriented Programming (NLOP): Towards Democratizing  Software Creation

We evaluate AI-assisted generative capabilities on fundamental numerical
kernels in high-performance computing (HPC), including AXPY, GEMV, GEMM, SpMV,
Jacobi Stencil, and CG. We test the generated kernel codes for a variety of
language-supported programming models, including (1) C++ (e.g., OpenMP
[including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g.,
OpenMP [including offload] and OpenACC), (3) Python (e.g., numba, Numba, cuPy,
and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and
KernelAbstractions.jl). We use the GitHub Copilot capabilities powered by
OpenAI Codex available in Visual Studio Code as of April 2023 to generate a
vast amount of implementations given simple <kernel> + <programming model> +
<optional hints> prompt variants. To quantify and compare the results, we
propose a proficiency metric around the initial 10 suggestions given for each
prompt. Results suggest that the OpenAI Codex outputs for C++ correlate with
the adoption and maturity of programming models. For example, OpenMP and CUDA
score really high, whereas HIP is still lacking. We found that prompts from
either a targeted language such as Fortran or the more general-purpose Python
can benefit from adding code keywords, while Julia prompts perform acceptably
well for its mature programming models (e.g., Threads and CUDA.jl). We expect
for these benchmarks to provide a point of reference for each programming
model's community. Overall, understanding the convergence of large language
models, AI, and HPC is crucial due to its rapidly evolving nature and how it is
redefining human-computer interactions.

本文通过使用 AI 辅助生成能力和 GitHub Copilot 生成的不同语言（C++，Fortran，Python 和 Julia）的高性能计算的基本数值内核代码，提出了一种衡量模型和编程模型之间关系的 “熟练度度量标准”，研究结果显示 OpenAI Codex 的输出与编程模型的使用和发展成熟度相关。

评估 OpenAI Codex 在 HPC 并行编程模型内核生成方面的表现

Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel  Generation

In the burgeoning field of artificial intelligence (AI), understanding the
capabilities and limitations of programming-oriented models is crucial. This
paper presents a novel evaluation of the programming proficiency of Generative
Pretrained Transformer (GPT) models, specifically GPT-3.5 and GPT-4, against
coding problems of varying difficulty levels drawn from Codewars. The
experiments reveal a distinct boundary at the 3kyu level, beyond which these
GPT models struggle to provide solutions. These findings led to the proposal of
a measure for coding problem complexity that incorporates both problem
difficulty and the time required for solution. The research emphasizes the need
for validation and creative thinking capabilities in AI models to better
emulate human problem-solving techniques. Future work aims to refine this
proposed complexity measure, enhance AI models with these suggested
capabilities, and develop an objective measure for programming problem
difficulty. The results of this research offer invaluable insights for
improving AI programming capabilities and advancing the frontier of AI
problem-solving abilities.

本研究评估了 GPT-3.5 和 GPT-4 编程模型的编程熟练度，这些模型对于不同难度级别的编程问题的求解存在明显的局限性，作者提出了一个融合了问题难度和解题时间的编程问题复杂性度量标准，并强调了需要在 AI 模型中加入验证和创造性思维能力以更好地模拟人类问题解决技术的必要性。