GPT-Driven Source-to-Source Transformation for Generating Compilable Parallel CUDA Code for Nussinov’s Algorithm

Designing automatic optimizing compilers is an advanced engineering process requiring a great deal of expertise, programming, testing, and experimentation. Maintaining the approach and adapting it to evolving libraries and environments is a time-consuming effort. In recent years, OpenAI has presente...

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Vydané v:Electronics (Basel) Ročník 13; číslo 3; s. 488
Hlavní autori: Palkowski, Marek, Gruzewski, Mateusz
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.02.2024
Predmet:
Algorithms
Artificial intelligence
Chatbots
Codes
Compilers
Compiling (Electronic computers)
Computer graphics
Computer programming
Computer science
Coroutines
Dependence
Dynamic programming
Experiments
High performance computing
Image processing
Language
Languages
Learning processes
Libraries
Linguistics
Machine translation
Mathematical optimization
Medicine
Methods
Optimization
Programming languages
Python
Scripts
Transformation
Poland
ISSN:2079-9292, 2079-9292
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Shrnutí:Designing automatic optimizing compilers is an advanced engineering process requiring a great deal of expertise, programming, testing, and experimentation. Maintaining the approach and adapting it to evolving libraries and environments is a time-consuming effort. In recent years, OpenAI has presented the GPT model, which is designed for many fields like computer science, image processing, linguistics, and medicine. It also supports automatic programming and translation between programming languages, as well as human languages. This article will verify the usability of the commonly known LLM model, GPT, for the non-trivial NPDP Nussinov’s parallel algorithm code within the OpenMP standard to create a parallel equivalent of CUDA for NVIDIA graphics cards. The goal of this approach is to avoid creating any post-processing scripts and writing any lines of target code. To validate the output code, we compare the resulting arrays with the ones calculated by the optimized code for the CPU generated employing the polyhedral compilers. Finally, the code will be checked for scalability and performance. We will concentrate on assessing the capabilities of GPT, highlighting common challenges that can be refined during future learning processes. This will enhance code generation for various platforms by leveraging the outcomes from polyhedral optimizers.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13030488