UCSD CSE 160: Introduction to Parallel Computing
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This course aims to provide knowledge and hands-on experience in developing software for processors with massively parallel computing resources. A massively parallel processor has the hardware ability to allow many threads to execute simultaneously. Many commercial offerings from NVIDIA, Intel, AMD, Qualcomm, Google, and ARM offer such levels of concurrency. Effectively programming these processors requires in-depth knowledge of parallel programming principles and the parallelism models, communication models, and resource limitations of these processors. In addition, this course also prepares the students to use the parallelism models of modern programming languages. The target audiences of the course are students who want to develop applications for these processors for fun, work, and research, as well as those who want to develop programming tools and future processors.

The course involves a series of programming assignments of steadily growing complexity. All assignments will involve programming a massively parallel GPU system using OpenCL, a popular open source language extension of C/C++ for GPU programming. 

Gradescope Autograder
^^^^^^^^^^^^^^^^^^^^^
.. toctree::
   :maxdepth: 2
   :caption: Debugging Gradescope Autograder

   Gradescope
   Rate Limiting <rate_limit>

Data Science & Machine Learning Platform
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. toctree::
   :maxdepth: 2
   :caption: Getting Started with DSMLP

   DSMLP


PyTorch and OpenCL
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. toctree::
   :maxdepth: 2
   :caption: Getting Started with DSMLP

   PyTorch

Qualcomm Innovators Development Kit
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. toctree::
   :maxdepth: 2
   :caption: Getting Started with QIDK

   QIDK

.. toctree::
   :maxdepth: 2
   :caption: Getting Started with Qualcomm RB3 Gen2

   RB3_Linux
   RB3_Mac

Developing on your Computer
^^^^^
.. toctree::
   :maxdepth: 2
   :caption: Visual Studio Code with DevContainers

   devcontainers
   amdwsl

Assignments
^^^^^^^^^^^
.. toctree::
   :maxdepth: 2
   :caption: Programming Assignments
   
   PA1 - Device Query & Raytracer <PA1>
   PA2 - Vector Addition <PA2>
   PA3 - Matrix Multiplication <PA3>
   PA4 - Tiled Matrix Multiplication <PA4>
   PA5 - Convolution <PA5>
   PA6 - Reinventing the (Pytorch) Wheel (CNNs) <PA6>
   PA7 - Rounding Out the (Pytorch) Wheel <PA7>
   PA8 - Softmaxing the (Pytorch) Wheel  <PA8>
   
.. toctree::
   :maxdepth: 2
   :caption: Project Assignments

..


   
   PA9 - Implicit GeMM Convolution <PA9>