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Rescale > Case Studies > Leveraging Rescale to Remove Computational Bottlenecks: A Case Study with Resolved Analytics

Leveraging Rescale to Remove Computational Bottlenecks: A Case Study with Resolved Analytics

Technology Category

Analytics & Modeling - Digital Twin / Simulation
Infrastructure as a Service (IaaS) - Cloud Computing

Use Cases

Digital Twin
Virtual Reality

Services

Cloud Planning, Design & Implementation Services

The Challenge

Resolved Analytics, a boutique computational engineering and optimization consultant firm, faced a significant challenge in managing their fluctuating simulation needs. The number of customers and the computational demands of their problems varied from month to month, making it difficult to manage with their internal capability of 16 processors. Owning a standalone software license capable of handling the highest workloads was not economically viable, especially during months when only 8 or 16 processors were needed. This resulted in underutilized resources and inefficiencies. Furthermore, the firm found that 16 processors were insufficient for complex multi-physics problems, which required 64 or 128 processors for efficient execution.

About The Customer

Resolved Analytics is a boutique computational engineering and optimization consultant firm co-founded by Stewart Bible. The firm helps customers improve product and process performance with CFD, finite element analysis, and other dynamical simulation tools. Their simulation needs fluctuate depending on the number of customers they have at any one time and the computational demands that the customers’ problems require. The firm is small, with only three people, and their workload varies significantly. Sometimes they are very busy with four or five customer projects and might have three or four simulations running at a time.

The Solution

Resolved Analytics turned to Rescale, a cloud-based computing platform, to address their computational challenges. Rescale provided the firm with the flexibility to scale their computational resources according to their needs, eliminating the issue of underutilized resources. The firm used STAR-CCM+ on Rescale and found it very convenient and easy to set up. They also planned to explore the new ANSYS partnership and more open software like OpenFOAM. Rescale was used for overflow and big jobs that couldn't be run internally. The firm also planned to reserve their internal cores to run hard problems that require STAR-CCM+ and run more basic CFD analysis on OpenFOAM on Rescale resources. The availability of as many processors as needed allowed the firm to run many more iterations on trials and optimization.

Operational Impact

Using Rescale has significantly changed the way Resolved Analytics approaches simulation. The firm is no longer conservative about how many processors they run a problem on, as they now have access to as many processors as they want. This has allowed them to complete jobs much more quickly. The firm also anticipates that using Rescale will allow them to advertise greater capabilities and achieve a better bottom line. They see cloud computing as a long-term part of their business, enabling them to cost-effectively execute the most complex simulations and serve more clients by having access to supercomputing resources.

Quantitative Benefit

Access to as many processors as needed, eliminating the need for underutilized standalone software licenses
Ability to run more iterations on trials and optimization due to increased computational resources
Cost savings from using cloud-based computing resources instead of maintaining high-capacity internal resources