Welcome

Introduction to Artificial Intelligence

• Nikolaos Bakas (GRNET)

● Definition and Scope of AI. Introduction to AI and its goal of simulating human intelligence through machines. ● Data Mining and Big Data. Explanation of data mining and its crucial role in analyzing vast datasets to extract valuable insights. ● Subfields and Techniques of AI. Overview of core AI subfields: machine learning, neural networks, robotics, and their methodologies. ● Historical Evolution and Milestones. Journey of AI from its mythological roots to contemporary breakthroughs like Deep Blue, AlphaGo, and Large Language Models. ● AI Winters. Discussion on the AI Winters and the factors that led to periods of stagnation. ● The Rise of Machine Learning. Focus on machine learning's pivotal role in the revival of AI and foundational advances in neural networks. ● Contemporary AI Applications. Practical uses of AI in natural language processing, computer vision, autonomous vehicles, and more. ● Ethical Considerations and Challenges. Addressing ethics, privacy, and societal impacts associated with the advancement of AI technologies. ● Future of AI with Supercomputing. Exploring how supercomputers enhance AI development, providing the computational power for complex tasks.

Introduction to High-Performance Computing

• Nikolaos Bakas (GRNET)

High Performance Computing (HPC) is a crucial field that leverages advanced computing power to solve complex problems in science, engineering, and business. This 30-minute talk will cover fundamental concepts, the significance of HPC, its evolution, and the core technologies involved. The presentation showcases HPC as a transformative force, highlighting its foundational principles, historical evolution, and technological innovations that drive modern scientific and engineering breakthroughs. ● Definition and Importance of HPC. HPC aggregates massive computing power to deliver greater performance than standard computers, enabling breakthroughs in fields like climate research and molecular modeling. ● Components of an HPC System. Key components include compute nodes, high-speed networking, vast storage systems, and specialized software for HPC tasks. ● Parallel Computing. HPC relies on parallel computing, which involves multiple computations simultaneously to solve complex tasks faster using models like bit-level, instruction-level, data parallelism, and task parallelism. ● Scalability and Speedup. Scalability indicates a system’s ability to handle an increasing load, while speedup measures performance gains in parallel versus serial execution. ● Amdahl's and Gustafson's Laws. Amdahl's Law provides a theoretical speedup limit due to serial task portions, while Gustafson's Law suggests problem size can scale with the number of processors. ● HPC Programming Models. Models like OpenMP (shared-memory), MPI (message passing), and GPU programming, enable optimizing parallel processing and leveraging specific hardware capabilities. ● Evolution of HPC. From the first supercomputers like CDC 6600 and Cray-1, through the advent of parallel processing and clusters, to the future of exascale computing and AI. ● Moore's Law. Describes the doubling of transistors in an integrated circuit every two years, indirectly predicting computational power growth impacting HPC advancements. ● HPC Performance Metrics. Key metrics include FLOPS for computational capacity, latency, and bandwidth for data transfer, all essential for evaluating HPC efficiency. ● HPC in AI and Future Applications. Rapid advancements in computing power underline the role of HPC in AI developments, with the potential for revolutionary changes in multiple disciplines.

AI on HPC: Revolutionizing Machine Learning Applications

• Nikolaos Bakas (GRNET)

● Introduction to AI & HPC. Discuss the synergy of AI and High-Performance Computing (HPC), focusing on their combined role in accelerating data processing and enabling complex computations. ● Benefits of AI and HPC Integration. Highlight advantages such as faster processing of large datasets and enhanced innovation in sectors like healthcare, finance, and environmental science through precise simulations and data analysis. ● Overcoming Challenges in AI & HPC. Address key issues like energy consumption, data management, and security. Discuss how integrated solutions are fostering more sustainable and secure computing practices. ● Future Advancements. Explore anticipated developments in AI algorithms and HPC infrastructure, predicting their impact on scientific research, engineering, and business analytics, and the potential for new computational possibilities. ● Stochastic Gradient Descent (SGD). Explain the concept of SGD, its benefits in optimization for AI, and how the stochastic nature aids in escaping local minima for better results in non-convex problems. ● Parallel Processing and Scalability in HPC. Discuss the role of parallel computing in enhancing AI training efficiency, especially through technologies like Parallel Stochastic Gradient Descent (PSGD) across distributed GPU and CPU resources. ● Challenges in Hyperparameter Tuning. Address the complexity of hyperparameter tuning in machine learning within HPC environments and strategies to optimize this process efficiently. ● Real-World Applications of AI & HPC. Provide examples of practical implementations such as generative AI, large language models, time-series analysis, and optimization, demonstrating the transformative potential across different industries. ● The Infinite Possibilities. Conclude with a discussion on the endless applications of AI and HPC—from predictive analytics to autonomous vehicles—underlining the role of this integration in shaping future technologies and societal advancements.

How to Apply for HPC Access

• Nikolaos Bakas (GRNET)

1. Understanding HPC Access Requirements ○ Overview of High-Performance Computing (HPC) systems and their importance for research and industry. ○ Familiarity with terms like LINPACK, and petaflops. 2. EuroHPC Joint Undertaking Initiatives ○ Brief on EuroHPC JU and how it democratizes access to HPC resources in Europe. ○ Introduction to the EuroCC project which supports access across academia, industry, and public administration. 3. Benchmark and Development Access ○ Explain the purpose of the EuroHPC JU Benchmark and Development Access calls for testing applications. ○ Highlight the time frames: benchmark (3 months) vs. development access (renewable up to 1 year). 4. Regular and Extreme Scale Access Modes ○ Describe the process and expectations for Regular Access and its significance for large-scale projects. ○ Discuss Extreme Scale Access for high-impact research with resources from pre-exascale systems. 5. Specific Tracks & Resources Distribution ○ Scientific, Industry, and Public Administration Access. ○ Significance of detailed resource allocation plans in the application. 6. AI and Data-Intensive Application Calls ○ Focus on AI, data-intensive applications, foundation models, and their requirement for supercomputing. 7. Scalability and Performance ○ Importance of including scalability tests in applications to demonstrate effective use of HPC resources. 8. Optimization and Code Development ○ Discuss optimization strategies for codes and how to highlight them in proposals. 9. Administrative Details ○ Ensure all data handling and privacy consent forms are completed as part of the application. 10. Useful Resources and Contacts ○ Share URLs and contact information for further guidance and support in the submission processes.

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