Dynamic Compilation Research Articles

Teams need to be healthy, too: toward a definition and model of healthy teams

PurposeTeams across a wide range of contexts must look beyond task performance to consider the affective, cognitive and behavioral health of their members. Despite much interest in team health in practice, consideration of team health has remained scant from a research perspective. The purpose of this paper is to address these issues by advancing a definition and model of team health.Design/methodology/approachThe authors review relevant literature on team stress, processes and emergent states to propose a definition and model of team health.FindingsThe authors advance a definition of team health, or the holistic, dynamic compilation of states that emerge and interact as a team resource to buffer stress. Further, the authors argue that team health improves outcomes at both the individual and team level by improving team members’ well-being and enhancing team effectiveness, respectively. In addition, the authors propose a framework integrating the job demands-resources model with the input-mediator-output-input model of teamwork to illustrate the behavioral drivers that promote team health, which buffers teams stress to maintain members’ well-being and team effectiveness.Originality/valueThis work answers calls from multidisciplinary industries for work that considers team health, providing implications for future research in this area.

AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. The significance of this work lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model’s exceptional performance, achieving a notable 96.04% accuracy compared to existing studies. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.

Modularized and scalable compilation for double quantum dot quantum computing

Any quantum program on a realistic quantum device must be compiled into an executable form while taking into account the underlying hardware constraints. Stringent restrictions on architecture and control imposed by physical platforms make this very challenging. In this paper, based on the quantum variational algorithm, we propose a novel scheme to train an Ansatz circuit and realize high-fidelity compilation of a set of universal quantum gates for singlet-triplet qubits in semiconductor double quantum dots, a fairly heavily constrained system. Furthermore, we propose a scalable architecture for a modular implementation of quantum programs in this constrained systems and validate its performance with two representative demonstrations, the Grover’s algorithm for the database searching (static compilation) and a variant of variational quantum eigensolver for the Max-Cut optimization (dynamic compilation). Our methods are potentially applicable to a wide range of physical devices. This work constitutes an important stepping-stone for exploiting the potential for advanced and complicated quantum algorithms on near-term devices.

Formally Verified Native Code Generation in an Effectful JIT: Turning the CompCert Backend into a Formally Verified JIT Compiler

Modern Just-in-Time compilers (or JITs) typically interleave several mechanisms to execute a program. For faster startup times and to observe the initial behavior of an execution, interpretation can be initially used. But after a while, JITs dynamically produce native code for parts of the program they execute often. Although some time is spent compiling dynamically, this mechanism makes for much faster times for the remaining of the program execution. Such compilers are complex pieces of software with various components, and greatly rely on a precise interplay between the different languages being executed, including on-stack-replacement. Traditional static compilers like CompCert have been mechanized in proof assistants, but JITs have been scarcely formalized so far, partly due to their impure nature and their numerous components. This work presents a model JIT with dynamic generation of native code, implemented and formally verified in Coq. Although some parts of a JIT cannot be written in Coq, we propose a proof methodology to delimit, specify and reason on the impure effects of a JIT. We argue that the daunting task of formally verifying a complete JIT should draw on existing proofs of native code generation. To this end, our work successfully reuses CompCert and its correctness proofs during dynamic compilation. Finally, our prototype can be extracted and executed.

Compilation Forking: A Fast and Flexible Way of Generating Data for Compiler-Internal Machine Learning Tasks

Compiler optimization decisions are often based on hand-crafted heuristics centered around a few established benchmark suites. Alternatively, they can be learned from feature and performance data produced during compilation. However, data-driven compiler optimizations based on machine learning models require large sets of quality data for training in order to match or even outperform existing human-crafted heuristics. In static compilation setups, related work has addressed this problem with iterative compilation. However, a dynamic compiler may produce different data depending on dynamically-chosen compilation strategies, which aggravates the generation of comparable data. We propose compilation forking, a technique for generating consistent feature and performance data from arbitrary, dynamically-compiled programs. Different versions of program parts with the same profiling and compilation history are executed within single program runs to minimize noise stemming from dynamic compilation and the runtime environment. Our approach facilitates large-scale performance evaluations of compiler optimization decisions. Additionally, compilation forking supports creating domain-specific compilation strategies based on machine learning by providing the data for model training. We implemented compilation forking in the GraalVM compiler in a programming-language-agnostic way. To assess the quality of the generated data, we trained several machine learning models to replace compiler heuristics for loop-related optimizations. The trained models perform equally well to the highly-tuned compiler heuristics when comparing the geometric means of benchmark suite performances. Larger impacts on few single benchmarks range from speedups of 20% to slowdowns of 17%. The presented approach can be implemented in any dynamic compiler. We believe that it can help to analyze compilation decisions and leverage the use of machine learning into dynamic compilation.

Настройка критериев планировщика СУБД с учётом динамической компиляции

Dynamic compilation of certain operator compositions might have a drastic impact on overall query performance, but not be considered during optimal query plan selection by DBMS planner due to lack of knowledge. To tackle this problem, we propose to extend cost model with criteria that make dynamic compilation overhead relevant. The necessity to set up the optimizer criteria is due to the fact that the properties of various execution models impose restrictions on the efficiency of query plan execution using certain operator nodes. For example, the push-model used in the dynamic compiler is advantageous when executing queries using sequential scan. So, dynamic compilation makes sequential scan more efficient than index scan. Using index nodes in such a plan makes the value of the dynamic compilation method diminishing. To overcome these problems, it is proposed to configure the DBMS optimizer, so that it evaluates and takes into account the efficiency of using certain types of nodes when building a query plan with its subsequent dynamic compilation. This paper discusses the modification of the PostgreSQL planner to select the most efficient query execution plan based on hardware characteristics and the execution model of operator nodes with interpretation or compilation.

Dynamic Compilation of Pattern based clustering and Volumetric Probabilistic Mining for Network Routing in Cognitive Radio Sensor Networks

Objectives: The key objective is to investigate the current state of the art in web service prediction systems and also to improve the retrieving process with improved accuracy and to reduce the searching time. As well as to enhance the performance of data validation, quality of services and speed of the process. Method: In this study, an advanced model of the Dynamic Compilation of Pattern (DCP) method with a Lexical Subgroup (LS) system was used to estimate the similarity between the request data and the entire network. These are all indexed and grouped as a cluster to form a paged format of network structure which can reduce the computation time during the searching period. Also, with the help of prediction, the relevancy of feature attributes in the network is predicted, and the matching index is sorted to provide the recommended data for given request data. This was achieved by using Volumetric Probabilistic Mining (VPM). Findings: The performance of the proposed DCP-VPM is proved through extensive simulations and compared to those of the state-of-the-art methods. On the average, it is realized that the DCP-VPM always outperforms EACRP, ERP, ESUCR and ESAC related to minimizing average energy consumption, packet delivery ratio, end-to-end delay at different number of clusters by 10.2%, 18.6%, 11.3%, 12.5% compared to EACRP, ERP, ESUCR and ESAC respectively. Proposed cluster-based routing technique out performs all other routing techniques. Novelty: Route based request prediction system was focused to predict and analyse data from the network. That is why enhanced clustering, distance-based similarity and retrieving mechanism are used. Irrelevant parameters removal and ordering are performed using DCP with LS system. Then nodes are processed for learning model using VPM prediction model. Finally, as the recommended result for the routing application, the matched data that is related to the request input is listed. Keywords: Dynamic Compilation of Pattern; Lexical Subgroup system; Volumetric Probabilistic Mining; Request prediction; Cognitive Radio

Plasticity-on-Chip Design: Exploiting Self-Similarity for Data Communications

With the increasing demand for distributed big data analytics and data-intensive programs which contribute to large volumes of packets among processing elements (PEs) and memory banks, we witness a pressing need for new mathematical models and algorithms that can engineer a brain-inspired plasticity into the computing platforms by mining the topological complexity of high-level programs (HLPs) and exploiting their self-similar and fractal characteristics for designing reconfigurable domain-specific computing architectures. In this article, we present Plasticity-on-Chip (PoC) by engineering plasticity into ”artificial brains” to mine and exploit the self-similarity of HLPs. First, we present a communication modeling of HLPs (e.g., C/C++ implementations of various applications) that relies on static and dynamic compiler analysis of programs with varying input seeds, performing comprehensive program analysis of all traces, and representing the HLPs as weighted directed acyclic graphs while capturing the intrinsic timing constraints and data/control flow requirements. Second, we propose a rigorous mathematical framework for determining the optimal parallel degree of executing a set of interacting HLPs (by partitioning them into clusters of densely interconnected supernodes - tasks) which helps us decide the number of available heterogeneous PEs, the amount of required memory and the structure of the synthesized deadlock-free irregular NoC topology that offers an efficient communication medium. These clusters serve as abstract models of computation for the synthesized PEs within the parallel execution model. Finally, exploiting the fractal and complex networks concepts, we extract in-depth features from graphs that serve as inputs for distributed reinforcement learning. Our experimental results on synthesized PEs and NoCs show performance improvements as high as 7.61x when compared to the traditional NoC and 2.6x compared to gem5-Aladdin.

Pattern-Based Dynamic Compilation System for CGRAs With Online Configuration Transformation

Prevailing data-intensive applications, such as artificial intelligence and internet of things, demand considerable compute capability. Coarse-grained reconfigurable architectures (CGRAs) can meet this demand via providing abundant compute resources. However, compilation has become an essential problem because the increasing resources need to be orchestrated efficiently. Static compilation is insufficient due to conservative resource allocation and exponentially increasing time cost while state-of-the-art dynamic compilation still performs poorly in both generality and efficiency. This article proposes a dynamic compilation system for CGRAs through online pattern-based configuration transformation, which enables virtualization to improve resource utilization and flexibility. It utilizes statically-generated patterns to straightforwardly determine dynamic placement of registers and operations so that the transformation algorithm has a low complexity. Domain-specific features are extracted by a k-means clustering algorithm to help improve the quality of patterns. The experimental results show that statically compiled applications can be transformed onto arbitrary resources at runtime, reserving 73.5 (22.8-163.3 percent) of the original performance/resource on average, 9.1 (0-52.9 percent) better than the state-of-theart non-general methods.

Smart selection of optimizations in dynamic compilers

SummaryDynamic compilers perform compilation and generation of target code during runtime, implying that the compilation time is added into the program runtime. Thus, to build a high‐performing dynamic compilation system, it is crucial to be able to generate high‐quality code and, at the same time, have a small compilation cost. In this article, we present an approach that uses machine learning to select sequences of optimization for dynamic compilation that considers both code quality and compilation overhead. Our approach starts by training a model, offline, with a knowledge bank of those sequences with low overhead and high‐quality code generation capability using a genetic heuristic. Then, this bank is used to guide the smart selection of optimizations sequences for the compilation of code fragments during the emulation of an application. We evaluate the proposed strategy in two LLVM‐based dynamic binary translators, namely OI‐DBT and HQEMU, and show that these two translators can achieve average speedups of 1.26x and 1.15x in MiBench and Spec Cpu benchmarks, respectively.

Кэширование машинного кода в динамическом компиляторе SQL-запросов для СУБД PostgreSQL

As the efficiency of main and external memory grows, alongside with decreasing hardware costs, the performance of database management systems (DBMS) on certain kinds of queries is more determined by CPU characteristics and the way it is utilized. Relational DBMS utilize diverse execution models to run SQL queries. Those models have different properties, but in either way suffer from substantial overhead during query plan interpretation. The overhead comes from indirect calls to handler functions, runtime checks and large number of branch instructions. One way to solve this problem is dynamic query compilation that is reasonable only in those cases when query interpretation time is larger than the time of compilation and optimized machine code execution. This requirement can be satisfied only when the amount of data to be processed is large enough. If query interpretation takes milliseconds to finish, then the cost of dynamic compilation can be hundreds of times more than the execution time of generated machine code. To pay off the cost of dynamic compilation, the generated machine code has to be reused in subsequent executions, thus saving the cost of code compilation and optimization. In this paper, we examine the method of machine code caching in our query JIT-compiler for DBMS PostgreSQL. The proposed method allows us to eliminate compilation overhead. The results show that dynamic compilation of queries with machine code caching feature gives a significant speedup on OLTP queries.

Динамическая компиляция пользовательских функций на языке PL/pgSQL

Many modern RDBMS provide procedural extensions for SQL programming language, which allow users to perform server-side complex computations. Use of such extensions improves modularity and code reuse, simplifies programming of application logic, and helps developers to avoid network overhead and enhance performance. Interpretation is mostly used to execute SQL queries and procedural extensions code, resulting in significant computational overhead because of indirect function calls and performing of generic checks. Moreover, most RDBMS use different engines for SQL queries execution and procedural extensions code execution, and it is necessary to perform additional computations to switch between different engines. Thus, interpretation of SQL queries combined with interpretation of procedural extensions code may drastically degrade performance of RDBMS. One solution is to use a dynamic compilation technique. In this paper, we describe the technique of dynamic compilation of PL/pgSQL procedural language for the PostgreSQL database system using LLVM compiler infrastructure. Dynamic compiler of PL/pgSQL procedural language is developed as part of PostgreSQL queries dynamic compiler. Proposed technique helps to get rid of computational overhead caused by interpretation usage. Synthetic performance tests show that the developed solution speeds up SQL queries execution by several times.

Adaptive Compiler Strategies for Mitigating Timing Side Channel Attacks

Existing compiler techniques can transform code to make its timing behavior independent of sensitive values to prevent information leakage through time side channels. Those techniques are hampered, however, by their static nature and dependence on details of the processor targeted during the compilation. This paper presents a dynamic compiler approach based on offline profiles and JIT compiler strategies. This approach reduces overhead significantly and enables a trade-off between provided protection and overhead. Furthermore, it supports adaptive policies in which the protection adapts to run-time changes in the requirements. A prototype implementation in the Jikes Research VM is evaluated on RSA encryption, HMAC key verification, and IDEA encryption.

Compiler-Based Techniques to Secure Cryptographic Embedded Software Against Side-Channel Attacks

Side-channel attacks are a concrete and practical threat to the security of computing systems, ranging from high performance platforms to embedded devices. In this paper, we will provide a brief systematization of the current existing approaches to analyze the side-channel vulnerability of an implementation, or automatically implement countermeasures, relying on methodologies typical of compiler systems. We will dedicate a spotlight to a significant progress in the countermeasures techniques which is represented by the application of dynamic compilation techniques to prevent a side-channel attacker from devising a model of the attacked application. We conclude the work highlighting promising research directions in this field.

Cross-Language Interoperability in a Multi-Language Runtime

In large-scale software applications, programmers often combine different programming languages because this allows them to use the most suitable language for a given problem, to gradually migrate existing projects from one language to another, or to reuse existing source code. However, different programming languages have fundamentally different implementations, which are hard to combine. The composition of language implementations often results in complex interfaces between languages, insufficient flexibility, or poor performance. We propose TruffleVM, a virtual machine (VM) that can execute different programming languages and is able to compose them in a seamless way. TruffleVM supports dynamically-typed languages (e.g., JavaScript and Ruby) as well as statically typed low-level languages (e.g., C). It consists of individual language implementations, which translate source code to an intermediate representation that is executed by a shared VM. TruffleVM composes these different language implementations via generic access . Generic access is a language-agnostic mechanism that language implementations use to access foreign data or call foreign functions. It features language-agnostic messages that the TruffleVM resolves to efficient foreign-language-specific operations at runtime. Generic access supports multiple languages, enables an efficient multi-language development, and ensures high performance. We evaluate generic access with two case studies. The first one explains the transparent composition of JavaScript, Ruby, and C. The second one shows an implementation of the C extensions application programming interface (API) for Ruby. We show that generic access guarantees good runtime performance. It avoids conversion or marshalling of foreign objects at the language boundary and allows the dynamic compiler to perform its optimizations across language boundaries.

Improving on Linear Scan Register Allocation

Register allocation is a major step for all compilers. Various register allocation algorithms have been developed over the decades. This work describes a new class of rapid register allocation algorithms and presents experimental data on their behavior. Our research encourages the avoidance of graphing and graph-coloring based on the fact that precise graph-coloring is nondeterministic polynomial time-complete (NP-complete), which is not suitable for real-time tasks. In addition, practical graph-coloring algorithms tend to use polynomial-time heuristics. In dynamic compilation environments, their super linear complexity makes them unsuitable for register allocation and code generation. Existing tools for code generation and register allocation do not completely fulfill the require- ments of fast compilation. Existing approaches either do not allow for the optimization of register allocation to be achieved compre- hensively with a sufficient degree of performance or they require an unjustifiable amount of time and/or resources. Therefore, we pro- pose a new class of register allocation and code generation algorithms that can be performed in linear time. These algorithms are based on the mathematical foundations of abstract interpretation and the computation of the level of abstraction. They have been implemen- ted in a specialized library for just-in-time compilation. The specialization of this library involves the execution of common intermedi- ate language (CIL) and low level virtual machine (LLVM) with a focus on embedded systems.

Библиотека программирования гетерогенных архитектур

Embedded platforms with heterogeneous architecture, considered in this paper, consist of one primary and one or more secondary processors. Development of software systems for these platforms poses substantial difficulties, requiring a distinct set of tools for each constituent of the heterogeneous system. It also makes achieving high efficiency the more difficult task. Moreover, many use cases of embedded systems require runtime configuration, that cannot be easily achieved with usual approaches. This work presents a C-like metaprogramming DSL and a library that provides a unified interface for programming secondary processors of heterogeneous systems with this DSL. Together they help to resolve aforementioned problems. The DSL is embedded in C++ and allows to freely manipulate its expressions and thus embodies the idea of generative programming, when the expressive power of high-level C++ language is used to compose pieces of low-level DSL code. Together with other features, such as generic DSL functions, it makes the DSL a flexible and powerful tool for dynamic code generation. The approach behind the library is dynamic compilation: the DSL is translated to LLVM IR and then compiled to native executable code at runtime. It opens a possibility of dynamic code optimizations, e.g. runtime function specialization for specific parameters known only at runtime. Flexible library architecture allows simple extensibility to any target platform supported by LLVM. At the end of the paper a system approbation on different platforms and a demonstration of dynamic optimizations capability are presented.

Virtual machine warmup blows hot and cold

Virtual Machines (VMs) with Just-In-Time (JIT) compilers are traditionally thought to execute programs in two phases: the initial warmup phase determines which parts of a program would most benefit from dynamic compilation, before JIT compiling those parts into machine code; subsequently the program is said to be at a steady state of peak performance. Measurement methodologies almost always discard data collected during the warmup phase such that reported measurements focus entirely on peak performance. We introduce a fully automated statistical approach, based on changepoint analysis, which allows us to determine if a program has reached a steady state and, if so, whether that represents peak performance or not. Using this, we show that even when run in the most controlled of circumstances, small, deterministic, widely studied microbenchmarks often fail to reach a steady state of peak performance on a variety of common VMs. Repeating our experiment on 3 different machines, we found that at most 43.5% of <VM, Benchmark> pairs consistently reach a steady state of peak performance.

AOT vs. JIT: impact of profile data on code quality

Just-in-time (JIT) compilation during program execution and ahead-of-time (AOT) compilation during software installation are alternate techniques used by managed language virtual machines (VM) to generate optimized native code while simultaneously achieving binary code portability and high execution performance. Profile data collected by JIT compilers at run-time can enable profile-guided optimizations (PGO) to customize the generated native code to different program inputs. AOT compilation removes the speed and energy overhead of online profile collection and dynamic compilation, but may not be able to achieve the quality and performance of customized native code. The goal of this work is to investigate and quantify the implications of the AOT compilation model on the quality of the generated native code for current VMs. First, we quantify the quality of native code generated by the two compilation models for a state-of-the-art (HotSpot) Java VM. Second, we determine how the amount of profile data collected affects the quality of generated code. Third, we develop a mechanism to determine the accuracy or similarity for different profile data for a given program run, and investigate how the accuracy of profile data affects its ability to effectively guide PGOs. Finally, we categorize the profile data types in our VM and explore the contribution of each such category to performance.

3D model as a dynamic compilation of knowledge: interim results on the city of Alet

The late Iron Age capital of the Coriosolites, City of Alet, scattered today in a neighbourhood of Saint-Malo( France), has become since 2015 the subject of experimentations in digital archaeology. These have led us to define as objective a relevant three-dimensional (3D)reconstruction integrating different buildings, but also a precise topography, aspart of the coastal geomorphology, environmental vegetation and naval activities. First pillar of this reconstruction, 3D laser and magnetic surveys were conducted in order to get some digitised work supports, but also to raise some scientific issues. As part of the main Roman cities of Brittany, the historical study of the City began at least at the end of the 19thcentury and archaeological excavations began in the 1970s. So knowledge present in a wide spectrum of archaeological and historical references was then compiled. 3D reconstruction of the City, editable to adapt to new contributions and scientific discoveries, allows a dynamic synthesis of archaeological knowledge accumulated along the years. Because it was done within an archaeology laboratory, it was directly monitored and controlled iteratively by researchers in the field (site, area and period). This work currently allows archaeologists to face limits and constraints regarding this scientific process and better understand the organizational aspects of the City.