HPC and AI: A Powerful Combination
Our GPUs can be used to more effectively process AI-related algorithms, such as neural network models.
HPC provides the necessary computational power and scalability to handle large data sets, enhances accuracy and enables real-time predictions. That requires huge amounts of hardware power. Our HPC accelerates the training of AI models meaning you use less time on the project on hand.
High Performance Data Analytics (HPDA) are used to solve big data challenges, focusing on improving AI, especially large-scale deep learning models. They require more and more computing power. Before artificial intelligence (AI) came into the picture, High-Performance Computing (HPC) was already around. Combining these two areas means making some adjustments to how tasks are managed and the tools used.
An example of combining AI with HPC is using the laws of physics in models to create more realistic results. These models, called physics-informed neural networks (PINNs), follow rules like the conservation of mass and energy. PINNs can enhance or even substitute HPC methods in tasks like analyzing fluid flow, studying molecules, designing airfoils and jet engines, and exploring high-energy physics.
The problem is figuring out how to make AI work at the same level as High-Performance Computing (HPC). Training AI models in PINNs is computationally intensive. Going from understanding, learning and analyzing patterns in data to quickly decoding a genome in hours instead of weeks requires huge amount of computational power – and produce waste heat enough to warm a building or even a small town. HPC clusters with powerful GPUs, like Aura Computing, significantly accelerate the training process.
In recent years, deep learning (DL) methods have advanced significantly as well, sparking interest among research groups to integrate machine learning (ML) into their work. Because deep learning training requires a lot of computing power, it’s crucial for researchers to use efficient systems tailored for complex frameworks like TensorFlow, Keras, or PyTorch. Additionally, since Aura Computing uses different types of systems, our system supports frameworks optimized for both CPUs and GPUs.
Make no compromises on precision when training, infering or fine-tuning.
Faster training times, cost savings, increased revenue, and competitive advantage. Apply in any business!
Discover the advantages of collaborating with us!
- Deep understanding of your industry and business; we are business people who know technology
- Personal and relevant service to you; not some faceless global corporation
- Sustainability of our service is already taken care of on your behalf; ensuring CO2 reporting, compliance, and clean conscience
- Safe and secure cloud data center in a dependable country;
minimal business risks to you - Simple and cost-effective pricing model; our pricing is based on how much you use with minimal OPEX and no CAPEX investment is required
Scalable service with over 70 000 CPU & GPU cores available
The waste heat generated from the data centers is reused to heat properties in cold countries. Our data centers are mainly in Finland, one of the safest locations in the world (with minimal physical, geographical, and political risks).
Secure data centers with controlled and monitored access in secluded locations
Physical and digital asset protection and tracking; ISO 27001 and HDS certifications; NDA available per request; Private file servers and connection tunnels available
Save time and speed up work turnaround time
With the help of Aura Computing service, you can lift resource-intensive processes off your workstations. This frees up the workstation and the employee for more productive work. In addition, the cloud computing service calculates jobs much faster than even powerful workstations.