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GPU Computing and Its Significance in the Era of Generative AI
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GPU Computing and Its Significance in the Era of Generative AI.md
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GPU Computing and Its Significance in the Era of Generative AI
Shashi Kumar Nagulakonda
2023-27-10

GPU Computing and Its Significance in the Era of Generative AI

Here is a comprehensive blog post on GPU computing and its significance in the era of generative AI:

GPU Computing and Its Significance in the Era of Generative AI

Introduction

GPU (Graphics Processing Unit) computing has become extremely important in recent years, especially with the rise of generative AI models like DALL-E 2, GPT-3, and Stable Diffusion. In this post, we will dive into:

  • What is GPU computing and how it works
  • GPU architecture and how it differs from CPUs
  • Why GPUs are well-suited for generative AI
  • Latest GPU hardware innovations
  • Real-world examples and benchmarks

What is GPU Computing?

A GPU is a specialized electronic circuit that can rapidly process and manipulate memory to accelerate the creation of images and video. While originally designed just for graphics rendering, GPUs have evolved into extremely powerful parallel processing units that can accelerate various compute-intensive workloads.

Some key aspects of GPU computing:

  • Massively parallel architecture: GPUs have thousands of smaller, more efficient cores compared to CPUs. This allows them to handle multiple tasks simultaneously.

  • More computational power: Modern GPUs can deliver up to 10x more floating point operations per second (FLOPS) than CPUs.

  • High memory bandwidth: GPUs are designed with high-speed GDDR memory delivering up to 1 TB/s bandwidth to feed the many cores.

  • Programming models: APIs like CUDA and frameworks like PyTorch enable General Purpose GPU (GPGPU) computing.

GPU Architecture

The underlying architecture of GPUs gives them the ability to efficiently process highly parallel workloads[1].

GPU Architecture

Some key aspects:

  • Streaming Multiprocessors (SMs): Each SM has multiple smaller cores optimized for floating point math. For example, Nvidia’s latest H100 GPU has 132 SMs with 64 FP32 cores each, totaling 8448 cores.

  • Memory: GPUs have various types of high-bandwidth memory including GDDR6/HBM2 to feed the SMs. The 40GB HBM2 on A100 delivers 2.4 TB/s bandwidth.

  • NVLink: High-speed interconnect for scaling multi-GPU systems.

  • Tensor Cores: Specialized cores for accelerated deep learning matrix arithmetic.

Why GPUs for Generative AI?

Generative AI refers to models that can create new content like images, text, audio and video based on learning from vast datasets.

Training and running these models requires:

  • Processing huge datasets
  • Massive computational power for training
  • Low latency for inference

GPUs are perfectly suited to deliver on these requirements with their parallel architecture, high FLOPS, and high memory bandwidth[2].

Some examples:

  • The AI system DALL-E 2 was trained on millions of image-text pairs using hundreds of GPUs. Running inference on a 24GB A100 GPU gives low latency results.

  • GPT-3 was trained on 45TB of text data. Fine-tuning it on 8 V100 GPUs gives good performance for text generation applications.

  • Nvidia’s NeMo Megatron framework leverages GPU clusters to train huge language models with trillions of parameters.

Latest GPU Innovations

GPU hardware keeps evolving to push the boundaries of generative AI[3]:

  • Nvidia H100 GPU - Up to 32x faster than A100 for AI training and inference with new Transformer Engine and over 80 billion transistors.

  • Hopper Architecture - Improves multi-GPU scaling and doubles memory bandwidth vs Ampere.

  • NVLink-C2C - 900GB/s chip-to-chip bandwidth allows connecting 8 GPUs with full bi-directional bandwidth.

  • Multi-Instance GPU (MIG) - Partitions a GPU into separate GPU instances for shared use.

Real-World Impact

The combination of advanced GPU hardware and software is enabling breakthroughs in generative AI:

  • Healthcare: Nvidia Clara Discovery uses AI to find potential drug compounds. Running on only 4 DGX A100 systems, it evaluated 21 billion compounds in just 3 days vs months on a CPU cluster[4].

  • Conversational AI: Microsoft leveraged thousands of GPUs to create a conversational AI system that can chat about nearly any topic.

  • Drug discovery: BenevolentAI developed a novel drug for osteoarthritis up to 10 times faster by leveraging GPUs.

Conclusion

In summary, GPU computing delivers the performance, scalability and efficiency needed for the data and compute intensive training and inference of generative AI models. Rapid advances in GPU hardware, software libraries, and cloud acceleration services are enabling new possibilities and applications using AI. GPUs will continue playing a pivotal role as generative AI progresses from research into wider real-world use.

References

[1] Nvidia, “NVIDIA Ampere Architecture In-Depth”, 2020.

[2] T. Young et al., “Recent Trends in Deep Learning Based Natural Language Processing”, IEEE Computational Intelligence Magazine, 2018.

[3] Nvidia, “NVIDIA H100 Tensor Core GPU Architecture”, 2022.

[4] Nvidia, “Clara Discovery Reduces Drug Discovery Time from Months to Days”, 2022.

Citations:
[1] https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/53174/b9066485-256a-482b-8d3f-4f6782dfb42e/What Every Developer Should Know About GPU Computing.pdf
[2] https://www.cherryservers.com/blog/introduction-to-gpu-programming-with-cuda-and-python
[3] https://www.icdrex.com/the-power-of-generative-ai-and-gpus/
[4] https://www.cdw.com/content/cdw/en/articles/hardware/cpu-vs-gpu.html
[5] https://openmetal.io/docs/product-guides/private-cloud/gpu-parallel-computing/
[6] https://www.boston.co.uk/info/nvidia-kepler/what-is-gpu-computing.aspx
[7] https://gcore.com/blog/gen-ai-ipu-gpu/
[8] https://aws.amazon.com/compare/the-difference-between-gpus-cpus/
[9] https://towardsdatascience.com/parallel-computing-upgrade-your-data-science-with-a-gpu-bba1cc007c24
[10] https://researchcomputing.princeton.edu/support/knowledge-base/gpu-computing
[11] https://www.weka.io/learn/ai-ml/gpus-for-machine-learning/
[12] https://www.heavy.ai/technical-glossary/cpu-vs-gpu
[13] https://www.cherryservers.com/blog/everything-you-need-to-know-about-gpu-architecture
[14] https://www.turing.com/kb/understanding-nvidia-cuda
[15] https://ai.plainenglish.io/from-gaming-to-ai-how-gpus-are-driving-the-technology-till-artificial-intelligence-b5cc81759120
[16] https://www.intel.com/content/www/us/en/products/docs/processors/cpu-vs-gpu.html
[17] http://www.irisa.fr/alf/downloads/collange/cours/ppar2020_gpu_1.pdf
[18] https://lorenabarba.com/gpuatbu/Program_files/Cruz_gpuComputing09.pdf
[19] https://www.fool.com/investing/2023/06/21/how-nvidia-is-using-generative-ai-to-accelerate-it/
[20] https://www.weka.io/learn/ai-ml/cpu-vs-gpu/
[21] https://www.infoworld.com/article/3299703/what-is-cuda-parallel-programming-for-gpus.html
[22] https://nyu-cds.github.io/python-gpu/01-introduction/
[23] https://blogs.nvidia.com/blog/2009/12/16/whats-the-difference-between-a-cpu-and-a-gpu/
[24] https://hpc-wiki.info/hpc/GPU_Tutorial
[25] https://superuser.com/questions/308771/why-are-we-still-using-cpus-instead-of-gpus

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