An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Blanco Alonso, James King, Michael Hines, Felix Schuermann
Ecole Polytechnique Federale de Lausanne (EPFL)
arXiv:1905.02241 [cs.MS], (6 May 2019)


   title={An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language},

   author={Kumbhar, Pramod and Awile, Omar and Keegan, Liam and Alonso, Jorge Blanco and King, James and Hines, Michael and Schuermann, Felix},






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 ~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 ~2x is observed.
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