How Cloud-Based Supercomputing Is Changing R&D
The cloud has made the processing power of the world’s most powerful computers accessible to a wider range of companies than ever before. Instead of having to architect, engineer, and build a supercomputer, companies can now rent hours on the cloud, making it possible bring tremendous computational power to bear on R&D. But where should companies start? What kinds of projects could benefit from this investment? There are a few common uses that have proven value: evaluating new designs through cloud-based simulation instead of physical prototyping, simulating a product’s interaction with real-world scenarios when physically prototyping is impractical, and predicting the performance of a full range of potential designs. It also opens up the possibilities for new products and services, which would have previously been impossible or impractical.
While the cloud is now ubiquitous in enterprise computing, there is one area where the shift to cloud has only just quietly begun: supercomputing. A catchall term for the world’s largest, most powerful computers, supercomputers were once available only to governments, research universities, and the most well-heeled corporations, and were used for cracking enemy codes, simulating weather, and designing nuclear reactors. But today, the cloud is bringing supercomputing into the mainstream.
This transition has the potential to accelerate (or disrupt) how businesses deliver complex engineered products, from designing rockets capable of reaching space and supersonic jets to creating new drugs and discovering vast pools of oil and gas hidden deep underground. Just as enterprise cloud computing created new ways for businesses to engage customers and disruptions from software-as-a-service to mobile computing, supercomputing will open up new possibilities for innovation breakthroughs by accelerating R&D speed and product development by orders of magnitude.
For example, the Concorde supersonic transport program took 25 years and $5 billion (adjusted for inflation) to launch its first commercial flight in 1976. Contrast that timeline with Boom Supersonic, a startup that promises to cut air travel time in half, shuttling passengers between New York and Paris in 3.5 hours. Only founded in 2014, it plans to deliver its Overture supersonic airliner in half the time, at a small fraction of the cost and personnel.
Boom’s rapid R&D speed was powered by cloud supercomputing. Rapid software simulations allowed the company to replace most of the physical prototyping and wind-tunnel testing required by the Concorde. Because of the cloud, Boom (which is a Rescale client) could afford to quickly run 53 million compute hours on Amazon Web Services (AWS) with plans to scale to more than 100 million compute hours. The company already has commitments from United to buy 15 of its supersonic transport jets, even though the aircraft has yet to fly. That’s how much confidence the airlines have in the millions of hours in computer simulation results produced to date.
So, given the potential of this technology, why are less than one in four supercomputers for simulations cloud based? The simple answer is that it’s hard. Computational engineering requires a complex and specialized technology stack, and few company IT organizations have the in-house expertise to set up a real R&D operation in the cloud.
There are a few reasons for this. First, high-performance computing infrastructure, which makes computational engineering possible, is a new offering for public cloud providers. Second, the simulation software required can be complex to set up and maintain. Third, choosing the right software/hardware combination and maintaining the proper configuration as IT technology advances is critical to achieve the optimal performance for computational engineering workloads.
