Research Statement
Vittal Siddaiah
Title: Leveraging Machine Learning for High-Performance Computing Optimization
1. Introduction
High-Performance Computing (HPC) plays a critical role in addressing the computational challenges posed by large-scale scientific simulations, big data analytics, and artificial intelligence applications. As the complexity of HPC systems and workloads continues to grow, there is an escalating need for innovative approaches to optimize their performance, energy efficiency, and resource utilization. In this context, my research aims to leverage machine learning techniques to develop intelligent optimization strategies for HPC systems, with a focus on enhancing the performance, scalability, and sustainability of these systems.
2. Research Goals
My research is focused on the following key goals:
a) Performance prediction and modeling: Develop machine learning-based models for predicting the performance of HPC applications on various architectures, considering factors such as hardware configurations, workload characteristics, and runtime parameters. These models can be used to guide performance optimization and resource allocation decisions.
b) Auto-tuning and algorithm Selection: Design machine learning-based approaches for automatic selection and tuning of HPC algorithms and software libraries, considering factors such as problem size, hardware capabilities, and accuracy requirements. This can help to identify the most suitable algorithms and configurations for a given problem and computing environment, thus improving overall performance.
c) Resource management and Scheduling: Investigate machine learning techniques for intelligent resource management and job scheduling in HPC systems, with the goal of maximizing resource utilization, minimizing energy consumption, and reducing job completion times.
d) Fault Detection and Reliability: Apply machine learning methods to predict and detect hardware failures, performance anomalies, and other issues in HPC systems, enabling proactive fault mitigation and improved system reliability.
e) Machine learning for HPC system design: Explore the use of machine learning techniques in the design of next-generation HPC architectures, including processor architectures, memory hierarchies, and interconnects, to optimize performance, energy efficiency, and scalability.
3. Methodology
My research methodology involves a combination of machine learning algorithm development, HPC system modeling, and empirical evaluation. With my relevant work experience in developing measurement and benchmarking tools, I will start by comprehensively reviewing the state-of-the-art machine learning techniques and HPC optimization strategies. Based on this analysis, I will identify opportunities to apply machine learning techniques for HPC system optimization and develop novel methods to address these challenges. I will then implement and evaluate the proposed methods using real-world HPC applications and platforms, comparing their effectiveness against existing optimization strategies.
4. Expected Outcomes
The expected outcomes of my research include the following:
Novel machine learning-based approaches for HPC optimization, addressing various aspects such as performance prediction, auto-tuning, resource management, fault detection, and system design.
Open-source software tools and libraries that the HPC community can adopt to improve their systems' performance, scalability, and sustainability.
Collaborations with researchers and industry partners to apply the developed machine learning techniques to address real-world HPC challenges and improve the performance of large-scale scientific simulations, data analytics, and AI applications.
Peer-reviewed publications and conference presentations that contribute to the advancement of knowledge in the field of machine learning for HPC optimization.
5. Conclusion
By focusing on developing machine learning-based optimization strategies for HPC systems, my research aims to contribute to the ongoing efforts to enhance the performance, scalability, and sustainability of these systems, paving the way for more efficient and effective large-scale computing. Through this research, I aim to drive innovation in the HPC domain and enable the scientific community to tackle increasingly complex and data-intensive problems, ultimately leading to discoveries and advancements across various disciplines.