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.
Programmer controlled compilation has been added to the JIT in Lancet @cite_17 , which exposes an API for the JIT compiler to the programmer, so that the programmer can control compilation. Our work is similar, but since we target only inference algorithms, we provide higher-level optimizations that are more accessible for an algorithm writer.
@cite_17
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Just-in-time (JIT) compilation of running programs provides more optimization opportunities than offline compilation. Modern JIT compilers, such as those in virtual machines like Oracle's HotSpot for Java or Google's V8 for JavaScript, rely on dynamic profiling as their key mechanism to guide optimizations. While these JIT compilers offer good average performance, their behavior is a black box and the achieved performance is highly unpredictable. In this paper, we propose to turn JIT compilation into a precision tool by adding two essential and generic metaprogramming facilities: First, allow programs to invoke JIT compilation explicitly. This enables controlled specialization of arbitrary code at run-time, in the style of partial evaluation. It also enables the JIT compiler to report warnings and errors to the program when it is unable to compile a code path in the demanded way. Second, allow the JIT compiler to call back into the program to perform compile-time computation. This lets the program itself define the translation strategy for certain constructs on the fly and gives rise to a powerful JIT macro facility that enables "smart" libraries to supply domain-specific compiler optimizations or safety checks. We present Lancet, a JIT compiler framework for Java bytecode that enables such a tight, two-way integration with the running program. Lancet itself was derived from a high-level Java bytecode interpreter: staging the interpreter using LMS (Lightweight Modular Staging) produced a simple bytecode compiler. Adding abstract interpretation turned the simple compiler into an optimizing compiler. This fact provides compelling evidence for the scalability of the staged-interpreter app