Dynamic interpretation for dynamic scripting languages

Abstract

Dynamic scripting languages offer programmers increased flexibility by allowing properties of programs to be defined at run-time. Typically, program execution begins with an interpreter where type checks are implemented using conditional statements. Recent JIT compilers have begun removing run-time checks by specializing native code to program properties discovered at JIT time.This paper presents a novel intermediate representation for scripting languages that explicitly encodes types of variables. The dynamic representation is a flow graph, where each node is a specialized virtual instruction and each edge directs program flow based on control and type changes in the program. The interpreter thus performs specialized execution of whole programs. We present techniques for the efficient interpretation of our representation showing speedups of greater than 2x over static interpretation, with an average speedup of approximately 1.3x. MotivationScripting language virtual machines (VMs) typically compile high-level source code into an array of low-level opcodes (bytecodes). They then use a standard interpretation loop to execute the array of opcodes. Run-time type checks are implemented with either conditional statements or switch statements. Performing these type checks forms a significant portion of program execution. These costs seem unnecessary especially when we realize the same variable types are inevitably rechecked as loop iterations repeat the execution of the same block of code.Recent research has shown the advantages of type specialization in JIT compilation [19,10]. However, little research exists on the specialization of interpreters for scripting languages. We propose a scripting language representation and interpretation technique that performs specialized execution of dynamic code. We present a dynamic intermediate representation (DIR) which explicitly encodes the types of variables at each point of execution. The DIR is a flow graph, where each node is a specialized virtual instruction and each edge directs program flow based on control and type changes in the program. The proposed DIR therefore has a specialized path in the graph for every sequence of control and type changes found during execution.In this paper we present the initial development of our prototype implementation in the Lua Virtual Machine [12,13]. We present the design of our dynamic representation as well as the techniques used for its efficient interpretation. We illustrate our representation with a motivating example and present our plans for future interpreter optimizations. We compare the interpretation of our representation to the standard Lua implementation and show early speedups reaching a max of 2.13x and an average of 1.3x. We present reductions in the total number of instructions executed including reductions in conditional branches (used in interpreter type checking) as well as decreases in data and instruction cache accesses.

Energy efficiency across programming languages: how do energy, time, and memory relate?

“…The CLBG benchmark solutions have already been used for validation purpose by several research works. Among other examples, CLGB was used to study dynamic behavior of non-Java JVM languages [20], to analyze dynamic scripting languages [36] and compiler optimizations [34], or even to benchmark a JIT compiler for PHP [16]. At the best of our knowledge, CLGB was only used once for energy consumption analysis.…”

Characteristics of dynamic JVM languages

“…Dynamic instruction frequencies and basic block hotness are the fundamental metrics that are used in tracing-based compilation approaches. These metrics are also used in the work by Williams et al [24], where the authors propose specializing native code to program properties found at JIT-compilation time, thus improving the overall program execution time.…”

Towards a Green Ranking for Programming Languages

“…The CLBG includes solutions for thirteen different problems, written in twenty eight different programming languages. These benchmarks have been used in several research works, to study the dynamic behavior of non-Java JVM languages [15], to analyze dynamic scripting languages [32] and compiler optimizations [30], to benchmark a JIT compiler for PHP [11], and in the design of the R language [20]. In fact, the repository of programs included in CLBG are also particularly suitable to analyze the energy efficiency of programming languages due to the following characteristics:…”