Presentation
High-Performance Computing Resilience Analysis Using Large Language Models
DescriptionThis doctoral showcase highlights three pivotal works conducted during my PhD that collectively advance the field of high-performance computing (HPC) resilience analysis using large language models (LLMs).
The first work introduces HAPPA, a modular platform for HPC Application Resilience Analysis. HAPPA integrates LLMs to understand long code sequences, employing innovative code representation techniques to predict resilience accurately. Through the DARE dataset, HAPPA demonstrates superior predictive accuracy over existing models, achieving a mean squared error (MSE) of 0.078 in Silent Data Corruption (SDC) prediction, significantly outperforming the PARIS model.
Building on this foundation, the second work investigates the resilience of loops in HPC programs through a semantic approach. By analyzing the computational patterns known as the 13 dwarfs of parallelism, this study quantifies the SDC rates for each pattern. Utilizing LLMs with prompt engineering, the research identifies loop semantics, providing insights into which loops are more error-prone and enhancing the development of resilient HPC applications.
Expanding the scope further, the third work evaluates the capabilities of LLMs in comprehending the syntax and semantics of Intermediate Representation (IR) code. The study conducts a comprehensive analysis using models like GPT-4o, GPT-3.5, and CodeLlama. By performing tasks such as decompiling IR code, generating CFGs, and simulating IR code execution, the research provides insights into the effectiveness of LLMs in handling low-level code analysis and their potential applications in program analysis.
These studies collectively demonstrate the potential of LLMs in enhancing the resilience of HPC applications through innovative analysis techniques and predictive modeling.
The first work introduces HAPPA, a modular platform for HPC Application Resilience Analysis. HAPPA integrates LLMs to understand long code sequences, employing innovative code representation techniques to predict resilience accurately. Through the DARE dataset, HAPPA demonstrates superior predictive accuracy over existing models, achieving a mean squared error (MSE) of 0.078 in Silent Data Corruption (SDC) prediction, significantly outperforming the PARIS model.
Building on this foundation, the second work investigates the resilience of loops in HPC programs through a semantic approach. By analyzing the computational patterns known as the 13 dwarfs of parallelism, this study quantifies the SDC rates for each pattern. Utilizing LLMs with prompt engineering, the research identifies loop semantics, providing insights into which loops are more error-prone and enhancing the development of resilient HPC applications.
Expanding the scope further, the third work evaluates the capabilities of LLMs in comprehending the syntax and semantics of Intermediate Representation (IR) code. The study conducts a comprehensive analysis using models like GPT-4o, GPT-3.5, and CodeLlama. By performing tasks such as decompiling IR code, generating CFGs, and simulating IR code execution, the research provides insights into the effectiveness of LLMs in handling low-level code analysis and their potential applications in program analysis.
These studies collectively demonstrate the potential of LLMs in enhancing the resilience of HPC applications through innovative analysis techniques and predictive modeling.