Debugging Native Extensions of Dynamic Languages

Many dynamic programming languages such as Ruby and Python enable developers to use so called native extensions, code implemented in typically statically compiled languages like C and C++. However, debuggers for these dynamic languages usually lack support for also debugging these native extensions. GraalVM can execute programs implemented in various dynamic programming languages and, by using the LLVM-IR interpreter Sulong, also their native extensions. We added support for source-level debugging to Sulong based on GraalVM's debugging framework by associating run-time debug information from the LLVM-IR level to the original program code. As a result, developers can now use GraalVM to debug source code written in multiple LLVM-based programming languages as well as programs implemented in various dynamic languages that invoke it in a common debugger front-end.Comment: 7 pages, 7 figures, accepted at 15th International Conference on Managed Languages & Runtimes (ManLang'18

Array bounds check elimination in the context of deoptimization

AbstractWhenever an array element is accessed, Java virtual machines execute a compare instruction to ensure that the index value is within the valid bounds. This reduces the execution speed of Java programs. Array bounds check elimination identifies situations in which such checks are redundant and can be removed. We present an array bounds check elimination algorithm for the Java HotSpot™ VM based on static analysis in the just-in-time compiler.The algorithm works on an intermediate representation in static single assignment form and maintains conditions for index expressions. It fully removes bounds checks if it can be proven that they never fail. Whenever possible, it moves bounds checks out of loops. The static number of checks remains the same, but a check inside a loop is likely to be executed more often. If such a check fails, the executing program falls back to interpreted mode, avoiding the problem that an exception is thrown at the wrong place.The evaluation shows a speedup near to the theoretical maximum for the scientific SciMark benchmark suite and also significant improvements for some Java Grande benchmarks. The algorithm slightly increases the execution speed for the SPECjvm98 benchmark suite. The evaluation of the DaCapo benchmarks shows that array bounds checks do not have a significant impact on the performance of object-oriented applications

A Concurrency-Agnostic Protocol for Multi-Paradigm Concurrent Debugging Tools

Today's complex software systems combine high-level concurrency models. Each model is used to solve a specific set of problems. Unfortunately, debuggers support only the low-level notions of threads and shared memory, forcing developers to reason about these notions instead of the high-level concurrency models they chose. This paper proposes a concurrency-agnostic debugger protocol that decouples the debugger from the concurrency models employed by the target application. As a result, the underlying language runtime can define custom breakpoints, stepping operations, and execution events for each concurrency model it supports, and a debugger can expose them without having to be specifically adapted. We evaluated the generality of the protocol by applying it to SOMns, a Newspeak implementation, which supports a diversity of concurrency models including communicating sequential processes, communicating event loops, threads and locks, fork/join parallelism, and software transactional memory. We implemented 21 breakpoints and 20 stepping operations for these concurrency models. For none of these, the debugger needed to be changed. Furthermore, we visualize all concurrent interactions independently of a specific concurrency model. To show that tooling for a specific concurrency model is possible, we visualize actor turns and message sends separately.Comment: International Symposium on Dynamic Language

Speculation Without Regret: Reducing Deoptimization Meta-data

Abstract Speculative optimizations are used in most Just In Time (JIT) compilers in order to take advantage of dynamic runtime feedback. These speculative optimizations usually require the compiler to produce meta-data that the Virtual Machine (VM) can use as fallback when a speculation fails. This meta-data can be large and incurs a significant memory overhead since it needs to be stored alongside the machine code for as long as the machine code lives. The design of the Graal compiler leads to many speculations falling back to a similar state and location. In this paper we present deoptimization grouping, an optimization using this property of the Graal compiler to reduce the amount of meta-data that must be stored by the VM without having to modify the VM. We compare our technique with existing meta-data compression techniques from the HotSpot Virtual Machine and study how well they combine. In order to make informed decisions about speculation meta-data, we present an empirical analysis of the origin, impact and usages of this meta-data

Towards Meta-Level Engineering and Tooling for Complex Concurrent Systems

With the widespread use of multicore processors, software becomes more and more diverse in its use of parallel computing resources. To address all application requirements, each with the appropriate abstraction, developers mix and match various concurrency abstractions made available to them via libraries and frameworks. Unfortunately, today's tools such as debuggers and profilers do not support the diversity of these abstractions. Instead of enabling developers to reason about the high-level programming concepts, they used to express their programs, the tools work only on the library's implementation level. While this is a common problem also for other libraries and frameworks, the complexity of concurrency exacerbates the issue further, and reasoning on the higher levels of the concurrency abstractions is essential to manage the associated complexity. In this position paper, we identify open research issues and propose to build tools based on a common meta-level interface to enable developers to reasons about their programs based on the high-level concepts they used to implement them

Cross-Language Compiler Benchmarking: Are We Fast Yet?

Comparing the performance of programming languages is difficult because they differ in many aspects including preferred programming abstractions, available frameworks, and their runtime systems. Nonetheless, the question about relative performance comes up repeatedly in the research community, industry, and wider audience of enthusiasts. This paper presents 14 benchmarks and a novel methodology to assess the compiler effectiveness across language implementations. Using a set of common language abstractions, the benchmarks are implemented in Java, JavaScript, Ruby, Crystal, Newspeak, and Smalltalk. We show that the benchmarks exhibit a wide range of characteristics using language-agnostic metrics. Using four different languages on top of the same compiler, we show that the benchmarks perform similarly and therefore allow for a comparison of compiler effectiveness across languages. Based on anecdotes, we argue that these benchmarks help language implementers to identify performance bugs and optimization potential by comparing to other language implementations

Trace Register Allocation Policies: Compile-time vs. Performance Trade-offs

Register allocation is an integral part of compilation, regardless of whether a compiler aims for fast compilation or optimal code quality. State-of-the-art dynamic compilers often use global register allocation approaches such as linear scan. Recent results suggest that non-global trace-based register allocation approaches can compete with global approaches in terms of allocation quality. Instead of processing the whole compilation unit (i.e., method) at once, a trace-based register allocator divides the problem into linear code segments, called traces. In this work, we present a register allocation framework that can exploit the additional flexibility of traces to select different allocation strategies based on the characteristics of a trace. This provides us with fine-grained control over the trade-off between compile time and peak performance in a just-in-time compiler. Our framework features three allocation strategies: a linear-scan-based approach that achieves good code quality, a single-pass bottom-up strategy that aims for short allocation times, and an allocator for trivial traces. To demonstrate the flexibility of the framework, we select 8 allocation policies and show their impact on compile time and peak performance. This approach can reduce allocation time by 7–43 at a peak performance penalty of about 1–11 on average. For systems that do not focus on peak performance, our approach allows to adjust the time spent for register allocation, and therefore the overall compilation time, thus finding the optimal balance between compile time and peak performance according to an application's requirements

Efficient and Thread-Safe Objects for Dynamically-Typed Languages

We are in the multi-core era. Dynamically-typed languages are in widespread use, but their support for multithreading still lags behind. One of the reasons is that the sophisticated techniques they use to efficiently represent their dynamic object models are often unsafe in multithreaded environments. This paper defines safety requirements for dynamic object models in multithreaded environments. Based on these requirements, a language-agnostic and thread-safe object model is designed that maintains the efficiency of sequential approaches. This is achieved by ensuring that field reads do not require synchronization and field updates only need to synchronize on objects shared between threads. Basing our work on JRuby+Truffle, we show that our safe object model has zero overhead on peak performance for thread-local objects and only 3% average overhead on parallel benchmarks where field updates require synchronization. Thus, it can be a foundation for safe and efficient multithreaded VMs for a wide range of dynamic languages