Computer Architecture: From Pipelines to Parallelism

This foundational lecture establishes how compilers translate code and the inherent limitations, setting the stage for understanding how hardware design must accommodate software.

Building on compiler understanding, this lecture demonstrates how data-oriented design principles directly impact memory footprint and execution speed, crucial for efficient hardware utilization.

With a grasp of data layout, this step introduces cache-oblivious algorithms, illustrating how to design software that performs well across different memory hierarchies without explicit tuning.

Moving beyond memory, this lecture delves into advanced branch prediction, a critical processor optimization technique that significantly impacts instruction pipeline efficiency and overall performance.

Leveraging knowledge of processor optimizations and memory, this lecture explores GPU-native SQL engines, showcasing how specialized architectures handle massive parallel data processing.

Finally, this step examines Mojo, a language designed for AI hardware, integrating concepts of compilers, data layout, and specialized accelerators for cutting-edge computational demands.