Recent advances in personalized arrhythmia risk prediction show that computational models can provide not only safer but also more accurate results than invasive procedures. However, biophysically accurate simulations require solving linear systems over fine meshes and time resolutions, which can take hours or even days. This limits the use of such simulations in the clinic where diagnosis and treatment planning can be time sensitive, even if it is just for the reason of operation schedules. Furthermore, the non-interactive, non-intuitive way of accessing simulations and their results makes it hard to study these collaboratively. Overcoming these limitations requires speeding up computations from hours to seconds, which requires a massive increase in computational capabilities.
Fortunately, the cost of computing has fallen dramatically in the past decade. A prominent reason for this is the recent introduction of manycore processors such as GPUs, which by now power the majority of the world’s leading supercomputers. These devices owe their success to the fact that they are optimized for massively parallel workloads, such as applying similar ODE kernel computations to millions of mesh elements in scientific computing applications. Unlike CPUs, which are typically optimized for sequential performance, this allows GPU architectures to dedicate more transistors to performing computations, thereby increasing parallel speed and energy efficiency.
In this talk, we present ongoing work on the parallelization of finite volume computations over an unstructured mesh as well as the challenges involved in building scalable simulation codes and discuss the steps needed to close the gap to accurate real-time computations.