Multi-dimensional numerical integration is a challenging computational problem that is encountered in many scientific computing applications. Many integrands can be too computationally intense and even unmanageable for state-of-the-art CPU-based numerical libraries. Such performance issues can be mitigated by using GPU accelerated methods such as PAGANI and m-CUBES, which implement parallel adaptive quadrature and Monte Carlo integration, respectively. Experimental results in the context of the DES analysis project show orders of magnitude speedup over sequential methods and improved performance in terms of maximum attainable precision.
Effective utilization of such technologies in existing software pipelines introduces additional difficulties pertaining to scalability, portability, and ease of use. The DES analysis is the first use case to execute PAGANI and m-CUBES to compute thousands of integrands associated with cosmology models. As we increase the scale of our computations, we expand and refine techniques used to develop and test both PAGANI and m-CUBES to accommodate user needs, maintain performance, and validate our experimental results. In this talk, I will briefly describe the two parallel integration algorithms and how I diagnosed and solved some critical performance issues this summer.