Graphical Computing — How Video Games Elevate Science

Game developers have always tried to push graphics to the limit. The foundation of gaming graphics began with the commercial success of pong — simple 4 bit paddles and a ball that bounced back and forth. These days, players can see even the finest details such as threads in fabric or strands of hair. Advancements in computing power allow for the processing of millions of pixels in moments using Graphical Processing Units (GPUs). GPUs were specifically designed to compute complex math equations instantly — something Computer Processing Units (CPUs) don’t do so efficiently. Other industries have taken advantage of the advancements in cheap, fast GPUs to create 3D models. The biotech industry, for example, models molecules in programs like Pymol to visualize the microscopic world. The impact of GPU development on biotech is best understood by following its progression through the gaming industry.

Image for post
Image for post
RAM slots on a modern gaming motherboard. Photo by Pedro Sandrini from Pexels.

Rise of the GPU

IBM created some of the first graphic display machines in 1965 with the release of the IBM 2250. The 2250 used a cathode ray tube to manipulate images on a screen using a light pen — the original touch screen. The drawing done on the screen was processed through specific subroutines which returned characters to the screen. Display was limited to 63 characters, but could be reprogrammed. The price tag of $280,000 put it outside the range of most gamers, however. The first personal computers — Apple Lisa and MacIntosh — appeared in the late 80s with video cards that used Random Access Memory (RAM) to handle 2D graphics. Pixel manipulation was input through a mouse or controller, processed in RAM, and displayed. Unlike the 2250, which was used for writing characters, the MacIntosh displayed a “desktop” with graphical representations of file folders and sheets of paper in windows — the interface we still recognize today. A Central Processing Unit (CPU) was used for all the computations which heavily taxed the computer and prevented it from doing any additional work.

Image for post
Image for post
An old IBM computer with cathode tube display. Photo by bert sz on Unsplash.
Image for post
Image for post
A modern graphics card by NVidia. Photo by Christian Wiediger on Unsplash.

Scientific Application

The scientific community realized GPUs excelled at performing the same operation hundreds of thousands of times with unbelievable speed and accuracy. This was perfect for the Human Genome Project which became a dramatic scientific race between private companies and the public project to align a library of DNA sequences. GPUs easily out-matched multi-core machines in their duties. As the gaming community continued to call for better graphical processing, GPU manufacturer NVidia released the Tesla series GPUs which quadrupled the double precision performance of their equivalent GeForce cards—a function that was well-suited for scientific computation. Seeing a new market in scientific research, AMD designed the Radeon Instinct GPU series to center its function on deep-learning by optimizing Machine Intelligence Open (MIOpen) libraries. Additionally, AMD developed the competitive edge in CPU multi-threaded processing with their EPYC server processors. These CPUs have up to 32 available cores and 64 threads! AMD’s OpenCL driver package allows their GPUs and CPUs to share an OpenCL platform — a simple approach to optimizing utilization. At Macromoltek, we enjoy both the deep-learning capabilities of NVidia’s CUDA technology as well as the multi-threaded performance of AMD’s hardware to perform in-depth calculations for Simulated Annealing, Molecular Dynamics Simulation, and Molecule Interaction.

Links and Citations

  1. Game On, Science https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0057990
  2. The History of the Modern Graphics Processor https://www.techspot.com/article/650-history-of-the-gpu/
  3. Graphics Processing Units in Bioinformatics https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862309/
Image for post
Image for post

Written by

Welcome to the Macromoltek blog! We're an Austin-based biotech firm focused on using computers to further the discovery and design of antibodies.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store