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EECS E4750: Heterogeneous Computing for Signal and Data Processing (Fall 2024)

This is the course wiki. In the sidebar you will find navigation links to reference material, including environment setup instructions, tutorials and upcoming (and eventually, past) assignments.

Here are some quick links that you may be looking for:

• PyCUDA Tutorial
• PyOpenCL Tutorial
• Video tutorials for recitations - will be updated on courseworks

Course Information

Instructor

Zoran Kostic, PhD

Professor of Professional Practice, Electrical Engineering Dept., Columbia University.

Office Hours: time, location see on website.

For email communication use the heading subject "E4750 Heterogeneous Computing Student Question".

Email: zk2172@columbia.edu

Teaching Assistant(s)

2024 Fall: Pranav Kumar Kota

Office Hours: TBD, link on courseworks

Course Assistant email: pkk2125@columbia.edu

Course Overview

Methods for deploying signal and data processing algorithms on contemporary general purpose graphics processing units (GPGPUs) and heterogeneous computing infrastructures. Using programming languages such as OpenCL and CUDA for computational speedup in audio, image and video processing and computational data analysis. Course engagement through assignments, and a midterm. Significant design project expected.

Course Content

• Applications of Parallel Computing

• Graphics Processing Unit (GPU) architecture and programming

• Heterogeneous Parallel Computing (HPC)

• Parallel SW development in OpenCL and CUDA, discussion of other similar standards

• Motivating examples from imaging, audio, multimedia, deep learning

• Cross section of mobile processor architectures: Nvidia, AMD, Intel

• General Purpose Processors, Graphic Processing Units (GPU), DSPs ARM architecture

• Parallel programming concepts for mobile platforms CUDA and OpenCL language

• Tools: development environments, code development, profiling