To evaluate the performance of the FEATURE project on AWS, the team used software profiling and I/O benchmarking to measure performance metrics. Petkovic explains, “The team has a small, 40-node in-house cluster. We compared this to the cloud and found that Amazon EC2 was vastly superior in terms of CPU cycles per cost, as well as providing the ability to scale up when needed. Experiments that used to take us weeks now run overnight. This means that our scientists are always engaged and not waiting for results. AWS greatly reduced our turnaround time for scientific inquiry.”

Professor Petkovic estimates that their computing costs have been reduced by about 20 times. “We estimate that a small, 40-node in-house cluster runs at $ 1.71 per computer unit per hour. In comparison, Amazon EC2 costs us only $0.08 per equivalent elastic computer unit (ECU) per hour,” he explains. In addition, Petkovic and his team are able to use billing alerts and other cost optimization tools that AWS provides to plan and manage the cost of using the service.

“AWS provides on-demand access to high performance resources, which enables us to focus on science, rather than the heavy lifting of maintaining server infrastructure. AWS helps us lift the ceiling on the size and scope of our machine learning experiments,” says Petkovic.