Unlocking the Power of AI Through Optimized GPU Capacity Management

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About Author: Anchal Katyal is an experienced technology leader with more than a decade of expertise in building large scale software solutions and managing compute infrastructure capacity. Anchal is a passionate technology leader with the combination of fast-pace startup spirit and solid large-scale organization management skills. She has led & built several high-impact software projects from conception to launch. Her unique combination of technical expertise and leadership acumen enables her to drive innovation and digital transformation across organizations.

The rapid growth of artificial intelligence (AI) and machine learning adoption across industries has increased the demand for GPU computing power. Additionally, the continued need for high-performance graphics rendering and scientific computing applications has further driven the high demand for GPU capacity. Given the severe GPU supply shortage [1] and the exorbitant costs associated [2] with procuring GPU hardware, continuously expanding GPU infrastructure is neither a sustainable nor a cost-effective strategy for organizations and institutions. During his testimony before the US Senate in May 2023, Sam Altman, the visionary CEO of OpenAI, expressed the company’s struggle to keep up with the overwhelming number of requests for their chatbot tool (ChatGPT). “We’re so short on GPUs, the less people that use the tool, the better,” Altman said. [1] In the last decade, United States has funded approximately 4.6k AI companies, and is around 3.5 times the amount in China, and 7.4 times the amount in the United Kingdom.[3] Companies must first ensure they are using current GPU capacity to its full potential, maximizing utilization. 

The power of artificial intelligence (AI) lies in its ability to emulate human intelligence, enabling computer systems to learn, solve problems, and make decisions. It encompasses various subfields like machine learning, deep learning, natural language processing, computer vision, and robotics, unlock the potential for AI systems to analyze data, recognize patterns, and make predictions or decisions across numerous industries and applications. In recent years, the availability of powerful GPUs, with their ability to perform parallel computations and specialized matrix operations, has accelerated the training and deployment of sophisticated AI models, unlocking the true value of AI. [4]

AI enables businesses to optimize operations, automate tasks, launch new features and drive efficiencies that boost profitability and competitiveness. By leveraging AI’s data analysis, pattern recognition, content generation, and automation capabilities, companies can streamline processes, enhance customer experiences, gain a strategic advantage, and optimize operations for improved efficiency and profitability. This is achieved through AI-driven insights, predictive analytics, intelligent automation of repetitive tasks, and the deployment of advanced generative AI models capable of generating human-like content. Effective utilization of AI technologies allows companies to make informed decisions, improve productivity, deliver superior experiences, and drive business growth.

Optimizing GPU utilization is critical and essential as it allows businesses to maximize the performance and cost-effectiveness of their AI and machine learning workflows. This leads to faster training times, improved inference performance, increased operational and cost efficiency. At the ‘AI for Accelerating Progress on Sustainable Development Goals’ event in September 2023, Secretary of State, Antony J. Blinken said, “The decisions that all of us make collectively today about how we shape the future of AI – that is going to define our world for decades.  This is an immense responsibility, and it’s a responsibility that we have to take together as governments, the private sector, civil society, individual citizens, and communities.” [5]

With this in mind, we will dive deep into what GPU capacity is, its importance and mechanisms to optimizing its utilization.

What is GPU and its importance in AI models?

GPU (Graphics Processing Unit) is a specialized processor designed for handling computationally intensive tasks with parallel processing capabilities.[7] Its importance in AI models stems from its ability to accelerate operations like matrix multiplication and convolution, which are fundamental to many AI algorithms. GPUs offer high computational power, memory bandwidth, and energy efficiency, enabling faster training and deployment of complex deep learning models compared to traditional CPUs. The availability of GPU & GPU-accelerated libraries has played a crucial role in the rapid advancement of AI. GPUs have emerged as a rare resource and they are foundational for today’s generative AI era, enabling tasks that were previously deemed impossible.

GPUs can outperform CPUs in tasks that can be parallelized by leveraging their numerous processing cores, for example a team of teaching assistants can solve math problems for multiple students simultaneously, while a single teacher can only work on one problem at a time. The example illustrates that just like the teaching assistants divide the work and solve problems in parallel for their assigned groups of students, a GPU can distribute computationally intensive tasks across its thousands of cores, allowing for highly parallel processing. This parallel processing capability enables GPUs to complete tasks involving large datasets or complex calculations much faster than CPUs, which are limited to sequential processing.

There are three technical reasons why GPUs outperform other capacity [8] – 

1) GPUs employ parallel processing. 

2) GPU systems scale up to supercomputing heights.

3) The GPU software stack for AI is broad and deep.

Optimizing GPU capacity usage

We will explore strategies that can help optimize the utilization of GPU resources. The following approaches are widely applicable.

1) Employ parallel processing – Parallel processing is when you use multiple processing cores of the GPU capacity simultaneously to perform different tasks. This approach can significantly reduce the training time of the models and increase overall GPU utilization. 

2) Employ Data Caching – Data caching is when store data in a cache to optimize storage on instance and optimize processing. Most AI & machine learning models possess large data sets, break data into smaller chunks, store them in cache and send only batched data for processing. This minimizes GPU memory usage and optimizes processing.

3) Tuning to Precision – Adjust the precision of calculations, such as using mixed-precision training, to match your hardware capabilities and solve problems more effectively. This achieves faster model convergence and reduces memory requirements, ensuring efficient use of GPU.[9]

4) Optimal GPU Selection and Scalability – Choose the right GPU based on memory capacity, compute power, and scalability. Leverage multi-GPU setups or cloud-based GPU clusters to handle larger models and datasets

5) Optimize Batch Size – Adjust the batch size to fully utilize the GPU’s computational capacity. This improves resource utilization, leading to faster model training and lower costs.[10]

6) Efficient Memory Management – Optimize the allocation and utilization of GPU memory. This ensures smooth model training, avoids out-of-memory errors, reduces bottlenecks, and improves overall productivity.

7) Usage Monitoring – Regularly track and analyze GPU capacity utilization. This helps detect bottlenecks and inefficiencies, allowing for adjustments and optimizations.

8) Use Multiple Providers – Deploy multi-cloud strategy by pooling GPUs from multiple cloud providers. This offers flexibility, redundancy and optimizing for lowest cost provider.[11]

By implementing these strategies, organizations can ensure they are making the most of their GPU capacity, leading to improved performance, increased efficiency, and cost savings. There are several suppliers of GPU capacity on Cloud and due to the high price tag, effective utilization of GPU capacity is key. 

Conclusion 

GPUs provide the crucial computational power needed for deep learning and AI models, offering unparalleled computational power through parallel processing and scalability. By implementing the optimization techniques we discussed, organizations can maximize the value of their GPU investments. These techniques boost efficiency and productivity while reducing costs, giving organizations a competitive advantage. An effective GPU optimization strategy has become a top priority for organizations in the AI-driven era.

References

1. https://www.cnn.com/2023/08/06/tech/ai-chips-supply-chain/index.html
2. https://www.pcgamer.com/are-the-best-gpus-really-more-expensive-than-they-were-when-i-was-young-or-am-i-remembering-a-past-that-never-was/
3. https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report_2023.pdf
4. https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/global-perspectives-ai-adoption.html
5. https://www.state.gov/secretary-antony-j-blinken-at-the-ai-for-accelerating-progress-on-sustainable-development-goals-event/
6. https://www.marketsandmarkets.com/Market-Reports/micro-processors-market-28633568.html
7. https://developers.redhat.com/articles/2022/11/21/why-gpus-are-essential-computing#
8. https://blogs.nvidia.com/blog/why-gpus-are-great-for-ai/
9. https://openreview.net/pdf?id=r1gs9JgRZ
10. https://openreview.net/pdf?id=B1Yy1BxCZ
11. https://blog.ori.co/navigate-global-gpu-shortage-scale-ai-workloads