Enterprise AI Analysis
ChatHPC: Building the Foundations for a Productive and Trustworthy AI-Assisted HPC Ecosystem
ChatHPC democratizes large language models for the high-performance computing (HPC) community by providing the infrastructure, ecosystem, and knowledge needed to apply modern generative AI technologies to rapidly create specific capabilities for critical HPC components while using relatively modest computational resources. Our divide-and-conquer approach focuses on creating a collection of reliable, highly specialized, and optimized AI assistants for HPC based on the cost-effective and fast Code Llama fine-tuning processes and expert supervision. We target major components of the HPC software stack, including programming models, runtimes, I/O, tooling, and math libraries. Thanks to AI, ChatHPC provides a more productive HPC ecosystem by boosting important tasks related to portability, parallelization, optimization, scalability, and instrumentation, among others. With relatively small datasets (on the order of KB), the AI assistants, which are created in a few minutes by using one node with two NVIDIA H100 GPUs and the ChatHPC library, can create new capabilities with Meta's 7-billion parameter Code Llama base model to produce high-quality software with a level of trustworthiness of up to 90% higher than the 1.8-trillion parameter OpenAI ChatGPT-40 model for critical programming tasks in the HPC software stack.
Executive Impact
ChatHPC provides a productive and scalable environment for the creation of new capabilities in the HPC software ecosystem by delivering higher trustworthiness levels than state-of-the-art LLMs (Code Llama and the much larger ChatGPT model) in critical HPC tasks, including parallelization, portability, optimization, scalability, and instrumentation. With relatively small training datasets (just a few KB), ChatHPC can rapidly create trustworthy AI assistants for specific HPC functions—including programming models (Kokkos, IRIS, OpenMP, SYCL, CUDA), I/O (ADIOS2), math libraries (MAGMA), and performance profilers (TAU)—on very modest computational resources (one node with two NVIDIA GPUs) by fine-tuning the open-source Code Llama LLM. Moreover, the HPC community can easily extend the existing ChatHPC portfolio with different AI assistants without replicating the base LLM model, providing a truly accessible method for leveraging AI to facilitate HPC software development.
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Deep Analysis & Enterprise Applications
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Programming Models
Scientific Libraries
I/O
Tooling
Programming Models: Focuses on providing AI assistance for parallelization, optimization, and porting applications across heterogeneous programming models like Kokkos, IRIS, and SYCL.
Scientific Libraries: Aims to ease the burden of porting and optimizing applications that use vendor-specific or legacy sequential math libraries to MAGMA.
I/O: Simplifies user interaction with ADIOS2 and improves productivity for building application workflows, particularly for parallel I/O and data compression.
Tooling: Helps users choose correct TAU options and generate proper profiles for performance monitoring needs.
ChatHPC AI Assistant Creation Process
Base Model (Code Llama)
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Fine-Tuning Data
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AI Assistants (Adapters)
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Testing Data
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Learning Gaps
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Refinement Data
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ChatHPC Model (Merge Model + Adapters)
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Speedup over CUDA for Dot-Product (ChatHPC for Kokkos)
| Context | Code Llama | ChatHPC for Kokkos (Initial) | ChatHPC for Kokkos (Refinement) | ChatGPT |
|---|---|---|---|---|
| Documentation | 9.50% | 89.00% | 81.00% | 81.00% |
| Installation | 27.20% | 78.00% | 45.50% | 45.50% |
| Parallelization | 0.00% | 45.50% | 90.90% | 66.70% |
| Translation | 0.00% | 55.58% | 85.85% | 33.40% |
| OpenACC* | 0.00% | 53.20% | 87.20% | 41.30% |
| CUDA* | 0.00% | 58.70% | 83.50% | 31.20% |
ADIOS2 Parallel I/O Scalability with ChatADIOS2
ChatADIOS2 successfully translates serial POSIX I/O codes to ADIOS2 parallel I/O, demonstrating significant scalability improvements. The original POSIX implementation stops at 128 processes due to memory requirements, while the ADIOS2 code scales effectively beyond this, achieving up to a 17.5x speedup over POSIX I/O and handling 100 million doubles (~800 MB) per MPI process. This enables large-scale data management workflows for HPC applications.
Advanced ROI Calculator
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Annual Savings
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Your Implementation Roadmap
A phased approach to integrate AI-assisted HPC development effectively into your enterprise.
Discovery & Strategy
Initial assessment of current HPC workflows, identification of key pain points, and definition of AI integration goals. Includes stakeholder interviews and technology landscape analysis. (2-4 Weeks)
Data Preparation & Model Training
Curate domain-specific HPC datasets, fine-tune Code Llama with expert supervision, and create specialized AI assistants for targeted tasks like parallelization or I/O optimization. (4-8 Weeks)
Integration & Testing
Integrate trained AI assistants into existing HPC development environments. Conduct rigorous testing and validation of AI-generated code for correctness, performance, and portability. (3-6 Weeks)
Deployment & Monitoring
Deploy AI-assisted tools within the HPC ecosystem. Implement continuous monitoring of performance, trustworthiness, and user adoption. Gather feedback for iterative improvements. (2-4 Weeks)
Iterative Refinement & Expansion
Ongoing process of learning from usage data, refining AI models, and expanding capabilities to new HPC components or programming models. Ensures long-term value and adaptation. (Ongoing)
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