  Domain-specific languages (DSLs) play an increasingly important role in the
generation of high performing software. They allow the user to exploit specific
knowledge encoded in the constructs for the generation of code adapted to a
particular hardware architecture; at the same time, they make it easier to
generate optimized code for a multitude of platforms as the transformation has
to be encoded only once. Here, we describe a new code generation framework
(NMODL) for an existing DSL in the NEURON framework, a widely used software for
massively parallel simulation of biophysically detailed brain tissue models.
Existing NMODL DSL transpilers lack either essential features to generate
optimized code or capability to parse the diversity of existing models in the
user community. Our NMODL framework has been tested against a large number of
previously published user models and offers high-level domain-specific
optimizations and symbolic algebraic simplifications before target code
generation. Furthermore, rich analysis tools are provided allowing the
scientist to introspect models. NMODL implements multiple SIMD and SPMD targets
optimized for modern hardware. Benchmarks were performed on Intel Skylake,
Intel KNL and AMD Naples platforms. When comparing NMODL-generated kernels with
NEURON we observe a speedup of up to 20x, resulting into overall speedups of
two different production simulations by $\sim$10x. When compared to a
previously published SIMD optimized version that heavily relied on
auto-vectorization by the compiler still a speedup of up to $\sim$2x is
observed.
