Cloud computing hosts a wide variety of online services that we use on a daily basis, including web search, social networks, and video streaming. This course will cover how datacenter hardware, systems software, and application frameworks are designed for the cloud.

This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones.

The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular.

This seminar covers core concepts and ideas in the general area of machine learning systems, ranging from distributed and federated learning systems, DevOps systems for ML, life cycle and data management systems for ML, etc.

Introduction to systems programming. C and assembly language,floating point arithmetic, basic translation of C into assembler,compiler optimizations, manual optimizations. How hardware featureslike superscalar architecture, exceptions and interrupts, caches,virtual memory, multicore processors, devices, and memory systemsfunction and affect correctness, performance, and optimization.

The seminar covers core concepts and research developments in hardware/software infrastructure for large-scale AI model training and serving. During the seminar, students will present research papers in groups, based on a list of papers that will be provided at the beginning of the course.