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Parallel Computing

7.2 GPGPU Applications

The computational capabilities of GPGPU have been applied to a wide range of fields, enabling advances in science and engineering. By providing high levels of parallel processing power at a low cost, GPUs allow researchers to address computationally intensive problems. GPGPU is now a key technology in many areas of computational science.

High-Performance Computing (HPC) and Scientific Simulation

Section titled “High-Performance Computing (HPC) and Scientific Simulation”

One of the primary applications of GPGPU is in High-Performance Computing (HPC). The architecture of supercomputers has been significantly influenced by GPUs. Most of the fastest supercomputers on the TOP500 list are GPU-accelerated, which demonstrates their power efficiency.1 GPUs are used to accelerate a variety of scientific and engineering simulations, such as computational fluid dynamics (CFD), weather and climate modeling, astrophysical simulations, and molecular dynamics.2

Case Study: Molecular Dynamics Simulation with Folding@home

Section titled “Case Study: Molecular Dynamics Simulation with Folding@home”

The distributed computing project Folding@home is a prominent example of GPGPU’s application in scientific research.

  • The Challenge: Understanding diseases such as Alzheimer’s, cancer, and COVID-19 requires simulating protein dynamics, specifically how proteins “fold” into their three-dimensional structures.3 Protein misfolding is a factor in many diseases.3 These simulations are computationally demanding, requiring significant processing power to model atomic interactions over time.
  • The GPGPU Solution: Folding@home, launched in 2000, uses distributed computing to perform these simulations, allowing individuals to contribute unused processing time from their personal computers.4 The project was an early adopter of GPUs for molecular dynamics simulations.5 The client software runs simulations on volunteers’ GPUs and returns the results to a central server for analysis.4
  • Quantifiable Impact: The parallel architecture of GPUs is well-suited for the force calculations in molecular dynamics. For these workloads, GPUs can provide a 20- to 30-fold speedup compared to contemporary CPUs.5 This acceleration significantly increased the project’s scientific output. During the COVID-19 pandemic, a large number of volunteers contributed their GPU power, creating a distributed supercomputer that surpassed an exaflop of performance in April 2020.6 This computational power was used to simulate the spike protein of the SARS-CoV-2 virus, which contributed to therapeutic research.7

Deep Learning and Neural Networks

Section titled “Deep Learning and Neural Networks”

Artificial intelligence, particularly deep learning, is a defining application of GPGPU. The development of modern deep learning is closely tied to the availability of powerful GPUs. The architecture of a GPU is well-matched to the computational patterns of neural networks, which involve a high volume of matrix multiplications and tensor operations. These are data-parallel tasks that can be efficiently mapped to the thousands of cores on a GPU.8

Case Study: AlexNet and the Acceleration of Deep Learning

Section titled “Case Study: AlexNet and the Acceleration of Deep Learning”

The year 2012 was a significant point in the development of deep learning, largely due to a GPGPU-enabled breakthrough.

  • The Context: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is an annual competition for image classification.9 Before 2012, progress was incremental, and top models were based on traditional computer vision methods.
  • The Breakthrough: In the 2012 ILSVRC, a deep convolutional neural network (CNN) called AlexNet achieved a top-5 error rate of 15.3%, a significant improvement over the runner-up’s 26.2%.9 This result demonstrated the potential of deep learning at scale and spurred a major increase in research and investment in the field.10
  • The GPGPU Enabler: The success of AlexNet was dependent on GPGPU. The model, with 60 million parameters, was too computationally intensive to be trained on CPUs in a practical amount of time.11 It was trained over several days on two NVIDIA GTX 580 GPUs.9 The model was split across the two GPUs, which communicated at specific layers.12 Without the parallel processing power of GPGPU, the training of AlexNet would not have been feasible at the time.

The success of AlexNet created a feedback loop that continues to drive AI development. It established a large commercial market for GPUs in data centers, which funded further research and development into AI-specific hardware features like NVIDIA’s Tensor Cores.813 This more powerful hardware has, in turn, enabled the development of larger and more complex AI models.

ModelYearArchitectureParameter CountKey GPGPU Enabler
AlexNet 112012CNN~60 MillionTraining feasibility on 2x NVIDIA GTX 580 GPUs.
VGG-16 142014CNN~138 MillionEnabled by more powerful and memory-rich GPUs.
ResNet-50 142015CNN (Residual)~25 MillionDeeper, more complex architectures made trainable by GPGPU.
Transformer 142017Attention-based~213 Million (Base)Parallelizable attention mechanism well-suited for GPUs.
GPT-2 152019Transformer1.5 BillionScaling of Transformer models on large GPU clusters.
GPT-3 152020Transformer175 BillionMassive-scale training across thousands of NVIDIA V100 GPUs.
PaLM 162022Transformer540 BillionFurther scaling enabled by next-gen hardware and infrastructure.

Accelerated Data Analytics

Section titled “Accelerated Data Analytics”

A significant part of the data science workflow involves data preparation, including ETL (Extract, Transform, Load) processes. On CPU-based systems, these steps can be a bottleneck due to slow I/O and data movement.17 GPGPU is now being used to accelerate the entire data analytics pipeline.

Case Study: The RAPIDS.ai Ecosystem

Section titled “Case Study: The RAPIDS.ai Ecosystem”

The RAPIDS open-source software suite, initiated by NVIDIA, is a key example of GPU-accelerated data analytics.

  • The Problem: Popular data science libraries like pandas and scikit-learn are CPU-based. In a typical workflow, data scientists perform ETL on the CPU and then transfer the data to the GPU for model training. This data transfer is a slow process that creates a bottleneck.17
  • The RAPIDS Solution: RAPIDS is a collection of libraries designed to execute the entire data science pipeline on the GPU, which minimizes or eliminates data transfers between the CPU and GPU.18 It provides libraries with APIs that are similar to the PyData stack:19
    • cuDF: A GPU DataFrame library with a pandas-like API.
    • cuML: A GPU-accelerated machine learning library with a scikit-learn-like API.
    • cuGraph: A GPU-accelerated graph analytics library with a NetworkX-like API.
  • Key Principle and Impact: The main principle of RAPIDS is to keep data in GPU memory throughout the workflow. It uses the Apache Arrow columnar memory format for efficient, zero-copy data interchange between processes on the GPU.17 Benchmarks indicate that RAPIDS can provide speedups of 50x or more on end-to-end data science workflows.17 By accelerating the entire pipeline, RAPIDS allows data scientists to iterate on models and explore large datasets more interactively.

Applications in Specialized Domains

Section titled “Applications in Specialized Domains”

GPGPU is also used in several specialized domains.

  • Financial Services: The finance industry uses GPGPU for tasks requiring real-time processing of large, parallel data streams. Applications include algorithmic trading, risk analysis using Monte Carlo simulations, portfolio optimization, and fraud detection.2 The ability to analyze large datasets and react to market changes quickly provides a competitive advantage.20
  • Cryptocurrency Mining: Cryptocurrency mining is another application that leverages the parallel processing power of GPUs.
    • Technical Fit: The hashing algorithms used in proof-of-work systems, such as those used by Bitcoin, are highly parallelizable and run much faster on GPUs than on CPUs.21 Other algorithms, like Ethereum’s Ethash, were designed to be “memory-hard,” meaning their performance is bound by memory bandwidth rather than raw computational speed.22 This design requires frequent access to a large dataset (the DAG).23
    • Market Impact: GPUs, with their high-bandwidth memory systems, are well-suited for memory-hard algorithms.23 The high profitability of mining on consumer graphics cards led to a surge in demand from large-scale mining operations, which in turn caused global GPU shortages and price increases in 2017-2018 and 2020-2021.21

The diverse applications of GPGPU are all based on its ability to accelerate data-parallel computations. The evolution of the GPU from a specialized graphics engine to a general-purpose parallel computing platform has expanded the range of computationally feasible problems.

References

Section titled “References”

Footnotes

Section titled “Footnotes”

  1. General-purpose computing on graphics processing units - Wikipedia, accessed October 6, 2025, https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units ↩

  2. What is GPGPU? - Supermicro, accessed October 6, 2025, https://www.supermicro.com/en/glossary/gpgpu ↩ ↩2

  3. Folding@home - Wikipedia, accessed October 6, 2025, https://en.wikipedia.org/wiki/Folding@home ↩ ↩2

  4. Folding@home - GPU-optimized AI, Machine Learning, & HPC Software | NVIDIA NGC, accessed October 6, 2025, https://catalog.ngc.nvidia.com/orgs/hpc/teams/foldingathome/containers/fah-gpu ↩ ↩2

  5. Does Folding@home run on my graphics chip or GPU? – Folding …, accessed October 6, 2025, https://foldingathome.org/faqs/running-foldinghome/foldinghome-run-graphics-chip-gpu/ ↩ ↩2

  6. Folding@Home Crowdsources GPU-accelerated exaFLOP Supercomputer for COVID-19 Research | NVIDIA Technical Blog, accessed October 6, 2025, https://developer.nvidia.com/blog/foldinghome-gpu-accelerated-exaflop/ ↩

  7. Covid-19 - Folding@home, accessed October 6, 2025, https://foldingathome.org/diseases/infectious-diseases/covid-19/ ↩

  8. Did some sort of GPU revolution happen in the last 10-15 years? Is there a good non-technical description of it? : r/AskComputerScience - Reddit, accessed October 6, 2025, https://www.reddit.com/r/AskComputerScience/comments/lkk0th/did_some_sort_of_gpu_revolution_happen_in_the/ ↩ ↩2

  9. The Story of AlexNet: A Historical Milestone in Deep Learning | by James Fahey | Medium, accessed October 6, 2025, https://medium.com/@fahey_james/the-story-of-alexnet-a-historical-milestone-in-deep-learning-79878a707dd5 ↩ ↩2 ↩3

  10. AlexNet: The First CNN to win Image Net - Great Learning, accessed October 6, 2025, https://www.mygreatlearning.com/blog/alexnet-the-first-cnn-to-win-image-net/ ↩

  11. AlexNet - Wikipedia, accessed October 6, 2025, https://en.wikipedia.org/wiki/AlexNet ↩ ↩2

  12. AlexNet and ImageNet: The Birth of Deep Learning - Pinecone, accessed October 6, 2025, https://www.pinecone.io/learn/series/image-search/imagenet/ ↩

  13. CPU vs. GPU for Machine Learning - Pure Storage Blog, accessed October 6, 2025, https://blog.purestorage.com/purely-technical/cpu-vs-gpu-for-machine-learning/ ↩

  14. Parameters in notable artificial intelligence systems - Our World in Data, accessed October 6, 2025, https://ourworldindata.org/grapher/artificial-intelligence-parameter-count ↩ ↩2 ↩3

  15. OpenAI Presents GPT-3, a 175 Billion Parameters Language Model | NVIDIA Technical Blog, accessed October 6, 2025, https://developer.nvidia.com/blog/openai-presents-gpt-3-a-175-billion-parameters-language-model/ ↩ ↩2

  16. Timeline of AI and language models – Dr Alan D. Thompson - LifeArchitect.ai, accessed October 6, 2025, https://lifearchitect.ai/timeline/ ↩

  17. RAPIDS Accelerates Data Science End-to-End | NVIDIA Technical Blog, accessed October 6, 2025, https://developer.nvidia.com/blog/gpu-accelerated-analytics-rapids/ ↩ ↩2 ↩3 ↩4

  18. RAPIDS AI - GeeksforGeeks, accessed October 6, 2025, https://www.geeksforgeeks.org/artificial-intelligence/rapids-ai/ ↩

  19. Learn More | RAPIDS | RAPIDS | GPU Accelerated Data Science, accessed October 6, 2025, https://rapids.ai/learn-more/ ↩

  20. 7 Potential Use Cases For GPUs In Finance - AceCloud, accessed October 6, 2025, https://acecloud.ai/blog/potential-use-cases-for-gpus-in-finance/ ↩

  21. GPU mining - Wikipedia, accessed October 6, 2025, https://en.wikipedia.org/wiki/GPU_mining ↩ ↩2

  22. Ethash - FinchTrade, accessed October 6, 2025, https://finchtrade.com/glossary/ethash ↩

  23. the Ethash Algorithm & Top Ethash Coins to Mine - CryptoMinerBros, accessed October 6, 2025, https://www.cryptominerbros.com/blog/what-is-the-ethash-algorithm/ ↩ ↩2