  We present a highly-scalable framework that targets problems of interest to
the numerical relativity and broader astrophysics communities. This framework
combines a parallel octree-refined adaptive mesh with a wavelet adaptive
multiresolution and a physics module to solve the Einstein equations of general
relativity in the BSSN formulation. The goal of this work is to perform
advanced, massively parallel numerical simulations of Intermediate Mass Ratio
Inspirals (IMRIs) of binary black holes with mass ratios on the order of 100:1.
These studies will be used to generate waveforms as used in LIGO data analysis
and to calibrate semi-analytical approximate methods. Our framework consists of
a distributed memory octree-based adaptive meshing framework in conjunction
with a node-local code generator. The code generator makes our code portable
across different architectures. The equations corresponding to the target
application are written in symbolic notation and generators for different
architectures can be added independent of the application. Additionally, this
symbolic interface also makes our code extensible, and as such has been
designed to easily accommodate many existing algorithms in astrophysics for
plasma dynamics and radiation hydrodynamics. Our adaptive meshing algorithms
and data-structures have been optimized for modern architectures with deep
memory hierarchies. This enables our framework to have achieve excellent
performance and scalability on modern leadership architectures. We demonstrate
excellent weak scalability up to 131K cores on ORNL's Titan for binary mergers
for mass ratios up to 100.
