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Forward inference techniques such as sequential Monte Carlo and particleMarkov chain Monte Carlo for probabilistic programming can be implemented inany programming language by creative use of standardized operating systemfunctionality including processes, forking, mutexes, and shared memory.Exploiting this we have defined, developed, and tested a probabilisticprogramming language intermediate representation language we call probabilisticC, which itself can be compiled to machine code by standard compilers andlinked to operating system libraries yielding an efficient, scalable, portableprobabilistic programming compilation target. This opens up a new hardware andsystems research path for optimizing probabilistic programming systems.

A Compilation Target for Probabilistic Programming Languages