Inference algorithms in probabilistic programming languages (PPLs) can be thought of as interpreters, since an inference algorithm traverses a model given evidence to answer a query. As with interpreters, we can improve the efficiency of inference algorithms by compiling them once the model, evidence and query are known. We present SIMPL, a domain specific language for inference algorithms, which uses this idea in order to automatically specialize annotated inference algorithms. Due to the approach of specialization, unlike a traditional compiler, with SIMPL new inference algorithms can be added easily, and still be optimized using domain-specific information. We evaluate SIMPL and show that partial evaluation gives a 2-6x speedup, caching provides an additional 1-1.5x speedup, and generating C code yields an additional 13-20x speedup, for an overall speedup of 30-150x for several inference algorithms and models.
Broadway @cite_12 uses annotations to provide semantic information about a library in order to optimize it. It replaces general library calls with manually written specialized versions, whereas we want to specialize the code automatically.
@cite_12
2112569315
This paper introduces an annotation language and a compiler that together can customize a library implementation for specific application needs. Our approach is distinguished by its ability to exploit high level, domain-specific information in the customization process. In particular, the annotations provide semantic information that enables our compiler to analyze and optimize library operations as if they were primitives of a domain-specific language. Thus, our approach yields many of the performance benefits of domain-specific languages, without the effort of developing a new compiler for each domain. This paper presents the annotation language, describes its role in optimization, and illustrates the benefits of the overall approach. Using a partially implemented compiler, we show how our system can significantly improve the performance of two applications written using the PLAPACK parallel linear algebra library.