对比GPT-4，评估DeepSeek生成高性能计算代码的能力
LLM & HPC:Benchmarking DeepSeek's Performance in High-Performance Computing Tasks
- 用DeepSeek生成C++、Fortran等语言的HPC代码
- 生成代码功能正确但扩展性与执行效率逊于GPT-4
- 适合关注LLM在科学计算中应用的研究者
大型语言模型（如GPT-4和DeepSeek）已广泛应用于软件工程领域，但在高性能计算（HPC）中的潜力仍待挖掘。本文评估了近期大模型DeepSeek在生成一系列HPC基准代码方面的能力，包括共轭梯度求解器、并行热方程、并行矩阵乘法、DGEMM及STREAM triad操作。测试涵盖C++、Fortran、Julia和Python等传统HPC语言，评估内容包括代码正确性、性能表现以及不同配置和矩阵规模下的可扩展性。同时，将DeepSeek与另一常用工具GPT-4进行详细对比。结果表明，DeepSeek虽能生成功能性代码，但在代码可扩展性和执行效率上仍落后于GPT-4。
原文摘要 · Abstract (English)
Large Language Models (LLMs), such as GPT-4 and DeepSeek, have been applied to a wide range of domains in software engineering. However, their potential in the context of High-Performance Computing (HPC) much remains to be explored. This paper evaluates how well DeepSeek, a recent LLM, performs in generating a set of HPC benchmark codes: a conjugate gradient solver, the parallel heat equation, parallel matrix multiplication, DGEMM, and the STREAM triad operation. We analyze DeepSeek's code generation capabilities for traditional HPC languages like Cpp, Fortran, Julia and Python. The evaluation includes testing for code correctness, performance, and scaling across different configurations and matrix sizes. We also provide a detailed comparison between DeepSeek and another widely used tool: GPT-4. Our results demonstrate that while DeepSeek generates functional code for HPC tasks, it lags behind GPT-4, in terms of scalability and execution efficiency of the generated code.
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