GPU Software Salary and Insights for 2025

Given the rapid development of various technologies and use cases today, one of the most important components of nearly every consumer and data center-oriented computing task is the Graphics Processing Unit or GPU. Originally developed as a way to control image outputs (like digital displays), GPUs are used everywhere from gaming to professional content creation, to machine learning workloads and blockchain transactions.

The GPU has evolved from being solely about display output into one of the most important shared memory systems in the world, leveraging traits in parallel computing and data parallelism to offer best-in-class performance for basic applications and breakthrough research alike. Tracing the history and use of GPUs illuminates the importance of this resource in modern computing, and how it might evolve into our future as well. GPUs lie at the heart of general-purpose parallel processing, but originally they were designed purely for graphics pipeline work in controlling image displays.

The Origin of the GPU

Before the emergence of GPUs, dot matrix displays started appearing on screens in the 1940s and ’50s, followed by vector and raster displays; and, until the arrival of GPUs, early video game consoles and PCs used a non-programmable device called a graphics controller to manage display to the screen. The graphics controller (sometimes with an on-chip processor) typically offloaded processing to the CPU, although some had on-chip processors.

Around the same time, a 3D imaging project producing a single pixel on a screen with a single processor also promoted the shift to quickly generating many pixels to create an image, a technique that became known as the GPU.

The first GPUs appeared in the late-1990s and were marketed to the niche gamers’ and computer-aided design (CAD) markets. The invention looked like a combination of the already existing, partially programmable graphics controller and a transformation and lighting engine and a rendering engine, which until then existed only in software.

Evolution of GPU Technology

This changed in 1999 with the debut of Nvidia’s single-chip GeForce 256 GPU (graphical processing unit). The last decade, the 2000s and 2010s, then, gave birth to the GPUs of today, with innovations including ray tracing, mesh shading, and hardware tessellation, all improving image generation and graphics performance.

In 2007, Nvidia created a software layer known as CUDA, which unlocked the parallel processing abilities inherent in GPUs. CUDA made GPU computing more accessible, and today, GPUs are practically indispensable for applications in blockchain, artificial intelligence, and machine learning (AI/ML).

Practical Applications of GPUs

GPUs play a role in compute-heavy business areas including finance, defense, and research. Here is an overview of some of their most common uses.

Gaming

The GPU expanded first from departmental and enterprise visualization applications into personal gaming, playing a key part for instance in gaming consoles of the 1980s and corresponding visual effects for gamers’ PCs to this day as well as much of today’s new-generation commercial gaming consoles.

Professional Visualization

GPUs are used in professional applications such as computer-aided design (CAD) drawing, video editing, product walkthroughs, medical imagery, seismic imaging, and other complex image and video editing and visualization tasks. In browser-based applications, GPUs can be accessed through libraries such as WebGL.

Machine Learning

Training ML models is extremely compute-intensive, so employing GPUs can supercharge this process. Training a model on a local machine may take days, whereas the same model can be trained on a GPU in the cloud in minutes.

Blockchain

Cryptocurrencies on the blockchain tend to have higher rates of utilisation on GPUs, particularly in proof-of-work algorithms. Application-specific integrated circuits (ASICs) are becoming the go-to replacement for the executions that GPUs do in the proof of workspace, but we’ve seen that the whole proof of work concept isn’t as prevalent as it used to be. Proof-of-stake algorithms don’t need as much computing power.

Simulation

GPUs are just as vital for molecular dynamics and the huge simulations in weather forecasting and astrophysics. Fluid dynamics applications for automotive and large vehicle design are also common.

How a GPU Works

A modern GPU (such as nVidias GeForce GTX 670) has multiple multiprocessors with multiprocessor shared memory, processors per multiprocessor, and registers per processor. And the GPU has board-level memory in the form of both constant memory and device memory.

Each GPU processes things in a slightly different way – depending on what it was built to do, which manufacturer made it, which chip was placed inside, and which coordinating software is used to oversee its operation. For instance, Nvidia’s CUDA parallel processing software service allows developers to program the GPU for any parallel processing application.

Some GPUs are self-contained physical computer chips, called discrete GPUs, while others are integrated into other computing hardware, called integrated GPUs.

Conclusion

Initially, GPUs were special-purpose execution engines designed to offload graphical tasks from the central processing unit (CPU). Now GPUs are the dominant parallel processing devices across many industries, enabling gaming, professional visualization, machine learning, blockchain technology, and scientific rapidly advancing simulations. For example, Nvidia brings vector-instruction processing to the GPU via its CUDA general-purpose computing utilisation programme in 2006. GPUs, in a very short time and with increasing maturity, allow more general-purpose uses of GPUs for high-performance computing. GPUs play a major role in machine learning applications and can be used in everything from medicinal research, vehicle-driving AI, science and engineering to video gaming and cryptocurrency mining such as Ethereum. GPUs have become central to many technologies of today’s world. Printed with permission from Springer Nature. Copyright © 2023 by the Author. All rights reserved. The story of GPUs gives us a few insights about their history, what they are, how they work, and how to use them. Most importantly, it should reflect on how they have transformed the modern computing world.