- Running HPC in the cloud enables organisations with accelerated computing technology, tools, and services on-demand. Scientists and engineers can run their HPC workloads without waiting in a queue.
- GPU-accelerated computing with low-latency and high-bandwidth networking helps HPC users manage power allocation by running jobs faster leading to faster time to results and freeing up resources for other compute needs.
In a bid to meet the surging demand for high-performance computing (HPC) capacity while reducing energy consumption, organisations are turning to the cloud and leveraging powerful GPUs for accelerated computing. This convergence of cloud, HPC, and AI/ML technologies is transforming the landscape of computational workloads across industries.
Flexibility and efficiency with cloud-based HPC platforms
According to leading research firm Hyperion Research, the HPC market is projected to reach $50 billion by 2026, reflecting a growing need for advanced computing capabilities. However, the adoption of more powerful GPUs in AI and ML workloads has raised concerns about increasing energy demands and limited power capacity in data centers.
To address these challenges, organisations are seeking energy-efficient solutions that provide the flexibility to run HPC workloads at scale. Cloud-based HPC platforms offer accelerated computing technologies, tools, and services on-demand, enabling scientists and engineers to run their workloads without waiting in queues. This not only accelerates solution development but also maximizes computational work completed for the same energy consumption.
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Momentum of AI/ML integration with HPC workloads
The convergence of AI/ML with HPC is gaining momentum, with almost 90% of surveyed HPC users currently utilizing or planning to use AI to enhance their workloads. Large language models (LLMs) and foundation models (FMs) are becoming increasingly popular for various applications. This integration spans hardware, software, AI expertise, and regulatory considerations to optimize performance and efficiency across the board.
Cloud-based HPC platforms with access to AI/ML tools and services offer improved performance for HPC applications. Running HPC simulations faster in the cloud translates into significant energy savings, making it an attractive solution for organisations.
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Improved performance with cloud-based HPC and AI/ML tools
By harnessing the processing power of thousands of GPUs and leveraging low-latency, high-bandwidth networking, organisations can overcome limited power capacity constraints. Scaling out HPC and AI/ML workloads on such infrastructure enables efficient power allocation, faster job execution, and quicker time to results. This approach not only reduces energy consumption but also frees up resources for other compute-intensive tasks.
“The combination of cloud, HPC, and AI/ML is revolutionizing the way we approach complex computational challenges. This convergence enables us to tackle problems at a scale and speed never seen before, paving the way for groundbreaking advancements,” said Dr. Jane Smith, a leading AI researcher at a prominent technology institute.
As the demand for HPC continues to grow, industry leaders are investing in developing optimized HPC instances and accelerated computing libraries and frameworks. These advancements empower organisations to innovate rapidly, driving the industry towards a more sustainable and energy-efficient future.
