Advancing Parallel Programming with HPC-INSTRUCT: Optimizing Code LLMs for High Performance Computing

LLMs have revolutionized software development by automating coding tasks and bridging the gap between natural language and programming. While they are very effective for general-purpose programming, they struggle with specialized domains such as High Performance Computing (HPC), particularly in parallel code generation. This limitation arises from the scarcity of high-quality parallel code data in pre-training data sets and the inherent complexity of parallel programming. Addressing these challenges is critical, as creating HPC-specific LLMs can significantly improve developer productivity and accelerate scientific discoveries. To overcome these obstacles, researchers emphasize the need for curated data sets with better quality parallel code and improved training methodologies that go beyond simply increasing data volume.

Efforts to adapt LLMs for HPC have included fine-tuning specialized models such as HPC-Coder and OMPGPT. While these models show promise, many are based on outdated architectures or limited applications, limiting their effectiveness. Recent advances such as HPC-Coder-V2 leverage cutting-edge techniques to improve performance, achieving results comparable or superior to larger models while maintaining efficiency. The studies highlight the importance of data quality over quantity and advocate for specific approaches to improve parallel code generation. Future research aims to develop robust HPC-specific LLMs that bridge the gap between serial and parallel programming capabilities by integrating insights from synthetic data generation and focusing on high-quality data sets.

Researchers at the University of Maryland conducted a detailed study to refine a specialized HPC LLM for parallel code generation. They developed a synthetic dataset, HPC-INSTRUCT, containing high-quality instruction-response pairs derived from parallel code samples. Using this data set, they refined HPC-Coder-V2, which emerged as the best open source LLM for parallel code generation, with performance close to GPT-4 levels. Their study explored how data representation, training parameters, and model size influence performance, addressing key questions about data quality, strategy tuning, and scalability to guide future advances in computer-specific LLMs. HPC.

Improving code LLMs for parallel programming involves the creation of HPC-INSTRUCT, a large synthetic data set of 120,000 instruction-response pairs derived from open source parallel code snippets and LLM results. This dataset includes programming, translation, optimization, and parallelization tasks in languages ​​such as C, Fortran, and CUDA. We fine-tuned three pre-trained code LLMs (parameter models 1.3B, 6.7B, and 16B) on HPC-INSTRUCT and other datasets using the AxoNN framework. Through ablation studies, we examine the impact of data quality, model size, and fast formatting on performance, optimizing models for the ParEval benchmark to evaluate their ability to effectively generate parallel code.

To evaluate Code LLM for parallel code generation, the ParEval benchmark was used, which presents 420 diverse problems in 12 categories and seven execution models such as MPI, CUDA, and Kokkos. Performance was evaluated using the pass@k metric, which measures the probability of generating at least one correct solution in k trials. Ablation studies looked at the impact of base models, instruction masking, data quality, and model size. The results revealed that tuning the base models produced better performance than the instructional variants, high-quality data improved the results, and larger models showed diminishing returns, with a notable gain of 1.3 billion to 6. 7 billion parameters.

In conclusion, the study presents HPC-INSTRUCT, an HPC instruction dataset created using LLM synthetic data and open source parallel code. An in-depth analysis of the data, model, and prompt configurations was performed to identify factors that influence the performance of LLM code in parallel code generation. Key findings include the minimal impact of instruction masking, the advantage of tuning base models over instruction-tuned variants, and the diminishing returns of increasing training data or model size. Using these insights, three state-of-the-art HPC-specific LLMs (HPC-Coder-V2 models) were tuned to achieve superior performance on the ParEval benchmark. These models are efficient and outperform others in generating parallel code for high-performance computing.

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Sana Hassan, a consulting intern at Marktechpost and a dual degree student at IIT Madras, is passionate about applying technology and artificial intelligence to address real-world challenges. With a strong interest in solving practical problems, he brings a new perspective to the intersection of ai and real-life solutions.