An ARM Backend for PyPyls Tracing JIT - STUPS Group

12 3 JUST-IN-TIME COMPILATIONwhen the execution count for a certain block of code passed a defined threshold thisblock would be further optimized and transformed to machine code. Another projectthat innovated in the area of dynamic compilation is the SELF 3 project. SELF is a dynamicobject oriented language, inspired in many ways – such as message passing – bySmalltalk, it was built on a prototype based object model [SU94, UCCH91]. Because ofthe dynamic nature of the language it is difficult to optimize. SELF developed differentapproaches, finally creating a compiler that, based on runtime profiling, generatedfor one SELF method different optimized code paths depending on the types of the inputarguments [CUL89]. For a in depth historic overview of dynamic compilation see[Ayc03], which also contains a more detailed overview as well as different categorizationapproaches for JIT compilers.There are two main groups in which JIT compilers can be classified depending on howthey perform profiling and optimize the instructions before generating low level code.3.1 Method Based Just-In-Time CompilationMethod based JITs, as the name suggests, perform profiling and compilation on a permethod basis. There are different approaches in this area, some systems compile methodsto lower level code when a method is first executed, such as some Smalltalk systems[DS84]. Other systems perform profiling by counting the executions of methods to selectcandidate methods for compilation such as the Rubinius Ruby VM [Pho10]. And finallythere are approaches such as the Java HotSpot server compiler and Google’s V8 Javascriptengine which do not contain an interpreter but perform two step compilation by firstcompiling a version of the code with a fast compiler that does not perform all possibleoptimizations, then in a second step the generated code is profiled to select candidatesthat are compiled performing more aggressive optimizations. [PVC01, V8 11]3.2 Trace Based Just-In-Time CompilationAn alternative approach to profile and optimize programs at runtime is taken by tracingJITs. Tracing JITs operate take loops as the basic unit for profiling and optimization. Theexact definition of a loop can vary depending on the VM, but for bytecode based VMsis usually is defined as a stream of bytecodes ending with a backwards jump, the jumptarget is then the beginning of the loop. The basic assumptions of tracing JITs are that:• A program spends most of its time in loops.• Iterations of a loop usually take similar paths through the code.A tracing JIT runs a program by interpreting it, first converting it to bytecode and recordingprofiling information on the interpreted code. Once a certain threshold is passed thetracing JIT records the operations performed during one execution of the loop. This listof operations that corresponds to one possible path through the loop is called a trace.3 http://labs.oracle.com/self/