Explore the challenges and solutions in analyzing large-scale data for high-performance microprocessor design in this PyCon US talk. Dive into the Design Data (DD) data model, a technological breakthrough addressing key challenges in integrated circuit design, analysis, and debugging. Learn how this custom domain-specific read-only binary data model, implemented using C++, CPython, and Python method bindings, efficiently manages design components, reporting, data linking across time, and provides a reliable, scalable platform. Discover how the C++ implementation enables efficient graph traversal, custom interactive analysis, and design graph visualization. Understand the benefits of compressible binary format for version comparison in multi-year projects. Gain insights into integrating modern Free Open-Source Software technologies into complex Electronic Design Automation (EDA) ecosystems. Get inspired to experiment with C/C++ and CPython bindings in your application workflow and explore innovative ways to integrate Data Science methods into your domain.
Leveraging a Custom CPython Data Model for High Performance
Overview
Syllabus
Introduction
How we build a microprocessor
Microprocessor complexity
Micro architecture
Optimization
Complex design
Managing multiple versions
Design Data
Data Model Integration
Python Environment
Engineering View
Engineering View Example
Custom Analysis Example
Load Multiple Data Files
Developers Experience
Debugging
Checking for changes
Vectorized approach
democratized analysis
references
extensions
conclusion
Taught by
PyCon US
