  We present a new adaptive parallel algorithm for the challenging problem of
multi-dimensional numerical integration on massively parallel architectures.
Adaptive algorithms have demonstrated the best performance, but efficient
many-core utilization is difficult to achieve because the adaptive work-load
can vary greatly across the integration space and is impossible to predict a
priori. Existing parallel algorithms utilize sequential computations on
independent processors, which results in bottlenecks due to the need for data
redistribution and processor synchronization. Our algorithm employs a
high-throughput approach in which all existing sub-regions are processed and
sub-divided in parallel. Repeated sub-region classification and filtering
improves upon a brute-force approach and allows the algorithm to make efficient
use of computation and memory resources. A CUDA implementation shows orders of
magnitude speedup over the fastest open-source CPU method and extends the
achievable accuracy for difficult integrands. Our algorithm typically
outperforms other existing deterministic parallel methods.
