Just-in-time compilation provides significant performance improvements for programs written in dynamic languages. These benefits come from the ability of the compiler to speculate about likely cases and generate optimized code for these. Unavoidably, speculations sometimes fail and the optimizations must be reverted. In some pathological cases, this can leave the program stuck with suboptimal code. In this paper we propose deoptless, a technique that replaces deoptimization points with dispatched specialized continuations. The goal of deoptless is to take a step towards providing users with a more transparent performance model in which mysterious slowdowns are less frequent and grave.

Just-in-time compilers for dynamic languages routinely generate code under assumptions that may be invalidated at run-time, this allows for specialization of program code to the common case in order to avoid unnecessary overheads due to uncommon cases. This form of software speculation requires support for deoptimization when some of the assumptions fail to hold. This paper presents a model just-in-time compiler with an intermediate representation that explicits the synchronization points used for deoptimization and the assumptions made by the compiler's speculation. We also present several common compiler optimizations that can leverage speculation to generate improved code. The optimizations are proved correct with the help of a proof assistant. While our work stops short of proving native code generation, we demonstrate how one could use the verified optimization to obtain significant speed ups in an end-to-end setting.

Function calls in the R language do not evaluate their arguments, these are passed to the callee as suspended computations and evaluated if needed. After 25 years of experience with the language, there are very few cases where programmers leverage delayed evaluation intentionally and laziness comes at a price in performance and complexity. This paper explores how to evolve the semantics of a lazy language towards strictness-by-default and laziness-on-demand. To provide a migration path, it is necessary to provide tooling for developers to migrate libraries without introducing errors. This paper reports on a dynamic analysis that infers strictness signatures for functions to capture both intentional and accidental laziness. Over 99% of the inferred signatures were correct when tested against clients of the libraries.

To efficiently execute dynamically typed languages, many language implementations have adopted a two-tier architecture. The first tier aims for low-latency startup times and collects dynamic profiles, such as the dynamic types of variables. The second tier provides high-throughput using an optimizing compiler that specializes code to the recorded type information. If the program behavior changes to the point that not previously seen types occur in specialized code, that specialized code becomes invalid, it is deoptimized, and control is transferred back to the first tier execution engine which will start specializing anew. However, if the program behavior becomes more specific, for instance, if a polymorphic variable becomes monomorphic, nothing changes. Once the program is running optimized code, there are no means to notice that an opportunity for optimization has been missed.

In order to generate efficient code, dynamic language compilers often need information, such as dynamic types, not readily available in the program source. Leveraging a mixture of static and dynamic information, these compilers speculate on the missing information. Within one compilation unit, they specialize the generated code to the previously observed behaviors, betting that past is prologue. When speculation fails, the execution must jump back to unoptimized code. In this paper, we propose an approach to further the specialization, by disentangling classes of behaviors into separate optimization units. With contextual dispatch, functions are versioned and each version is compiled under different assumptions. When a function is invoked, the implementation dispatches to a version optimized under assumptions matching the dynamic context of the call. As a proof-of-concept, we describe a compiler for the R language which uses this approach. Our implementation is, on average, 1.7× faster than the GNU R reference implementation. We evaluate contextual dispatch on a set of benchmarks and measure additional speedup, on top of traditional speculation with deoptimization techniques. In this setting contextual dispatch improves the performance of 18 out of 46 programs in our benchmark suite.

The R programming language combines a number of features considered hard to analyze and implement efficiently: dynamic typing, reflection, lazy evaluation, vectorized primitive types, first-class closures, and extensive use of native code. Additionally, variable scopes are reified at runtime as first-class environments. The combination of these features renders most static program analysis techniques impractical, and thus, compiler optimizations based on them ineffective. We present our work on PIR, an intermediate representation with explicit support for first-class environments and effectful lazy evaluation. We describe two dataflow analyses on PIR: the first enables reasoning about variables and their environments, and the second infers where arguments are evaluated. Leveraging their results, we show how to elide environment creation and inline functions.

Operating systems are traditionally implemented in low- level, performance-oriented programming languages. These languages typically rely on minimal runtime support and provide unfettered access to the underlying hardware. Tra- dition has benefits: developers control the resources that the operating system manages and few performance bottle- necks cannot be overcome with clever feats of programming. On the other hand, this makes operating systems harder to understand and maintain. Furthermore, those languages have few built-in barriers against bugs. This paper is an ex- periment in side-stepping operating systems, and pushing functionality into the runtime of high-level programming languages. The question we try to answer is how much sup- port is needed to run an application written in, say, Smalltalk or Python on bare metal, that is, with no underlying oper- ating system. We present a framework named NopSys that allows this, and we validate it with the implementation of CogNos a Smalltalk virtual machine running on bare x86 hardware. Experimental results suggest that this approach is promising.

High-performance dynamic language implementations make heavy use of speculative optimizations to achieve speeds close to statically compiled languages. These optimizations are typically performed by a just-in-time compiler that generates code under a set of assumptions about the state of the program and its environment. In certain cases, a program may execute code compiled under assumptions that are no longer valid. The implementation must then deoptimize the program on-the-fly; this entails finding semantically equivalent code that does not rely on invalid assumptions, translating program state to that expected by the target code, and transferring control. This paper looks at the interaction between optimization and deoptimization, and shows that reasoning about speculation is surprisingly easy when assumptions are made explicit in the program representation. This insight is demonstrated on a compiler intermediate representation, named sourir, modeled after the high-level representation for a dynamic language. Traditional compiler optimizations such as constant folding, unreachable code elimination, and function inlining are shown to be correct in the presence of assumptions. Furthermore, the paper establishes the correctness of compiler transformations specific to deoptimization: namely unrestricted deoptimization, predicate hoisting, and assume composition.