Case Study: QuantLib achieves 32.72% faster runtime with TurinTech's Artemis

$400K Savings Artemis Boosts QuantLib Runtime by 32.72%

The customer, QuantLib, an open-source library vital to financial institutions, faced significant performance bottlenecks in its extensive C++ codebase. Manually identifying the specific inefficiencies causing slow runtimes was a highly time-consuming challenge for developers, requiring weeks of expert profiling. They turned to TurinTech and its Artemis AI platform to automate this optimisation process.

TurinTech's Artemis AI platform analyzed the QuantLib codebase, using advanced techniques to pinpoint bottlenecks in minutes instead of weeks. The solution automatically generated, scored, and validated over a hundred code recommendations, leading to an optimal set of changes. This resulted in a 32.72% faster runtime for the library, which translates to potential annual compute cost savings of up to $400,000 for firms using it.

QuantLib