This course is designed to provide students with an in-depth understanding and practical skills necessary to enhance the performance and efficiency of diverse computing systems. Through a holistic approach, students will delve into various aspects of optimization techniques across multiple domains. The course begins by exploring strategies for optimizing multi-core and CPU-intensive programs, emphasizing the importance of efficient garbage collection mechanisms. Students will then advance to mastering techniques for optimizing multi-threaded applications and IO-intensive programs, with a focus on leveraging Just-In-Time (JIT) compilation for improved performance.

Furthermore, the course delves into database optimization methodologies, covering topics such as hardware support and caching mechanisms to enhance database performance. Students will also explore advanced concepts in network programming optimization, including the utilization of Content Delivery Networks (CDNs) to improve network efficiency. Lastly, the course provides insights into advanced machine learning (ML) optimization techniques, such as ML Operations (MLOps) frameworks like LORA and optimizations for Non-Uniform Memory Access (NUMA), enabling students to optimize ML workflows for efficient computation. By the end of the course, students will possess the expertise to analyze performance bottlenecks, devise optimization strategies, and implement solutions to enhance the efficiency and scalability of complex computing systems.

Check the primary course page (https://kronos-192081.github.io/DOCS-2024/) maintained by Prof. Mainack Mondal for more details.