Ing. Sebastián Krynski, MSc.

Proceedings paper

10.1145/3689490.3690404

Department of Theoretical Computer Science
Programming Research Lab

Just-in-time compilers enhance the performance of future invocations of a function by generating code tailored to past behavior. To achieve this, compilers use a data structure, often referred to as a feedback vector, to record information about each function’s invocations. However, over time, feedback vectors tend to become less precise, leading to lower-quality code – a phenomenon known as feedback vector pollution. This paper examines feedback vector pollution within the context of a compiler for the R language. We provide data, discuss an approach to reduce pollution in practice, and implement a proof-of-concept implementation of this approach. The preliminary results of the implementation indicate ∼30% decrease in polluted compilations and ∼37% decrease in function pollution throughout our corpus.

Article

10.1145/3622839

Department of Theoretical Computer Science
Programming Research Lab

Most code is executed more than once. If not entire programs then libraries remain unchanged from one run to the next. Just-in-time compilers expend considerable effort gathering insights about code they compiled many times, and often end up generating the same binary over and over again. We explore how to reuse compiled code across runs of different programs to reduce warm-up costs of dynamic languages. We propose to use speculative contextual dispatch to select versions of functions from an off-line curated code repository. That repository is a persistent database of previously compiled functions indexed by the context under which they were compiled. The repository is curated to remove redundant code and to optimize dispatch. We assess practicality by extending Ř, a compiler for the R language, and evaluating its performance. Our results suggest that the approach improves warmup times while preserving peak performance.

Proceedings paper

10.1145/3519939.3523729

Department of Theoretical Computer Science
Programming Research Lab

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.

Article

10.1145/3485478

Department of Theoretical Computer Science
Programming Research Lab

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.

Proceedings paper

10.1145/3426422.3426984

Programming Research Lab

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. We propose to employ a sampling-based profiler to monitor native code without any instrumentation. The absence of instrumentation means that when the profiler is not active, no overhead is incurred. We present an implementation is in the context of the A just-in-time, optimizing compiler for the R language. Based on the sampled profiles, we are able to detect when the native code produced by A is specialized for stale type feedback and recompile it to more type-specific code. We show that sampling adds an overhead of less than 3 © 2020 ACM.