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In contemporary compilers, the determination of the loop unrolling factor is traditionally based on manually crafted heuristic rules. This approach heavily relies on human intuition, which limits its ability to achieve optimized performance across diverse architectures and can sometimes even lead to performance declines. Additionally, developers face challenges in achieving cross-platform compatibility, often necessitating extensive redesign efforts. In response, this study introduces a method leveraging the XGBoost algorithm to predict the optimal loop unrolling factor for compiler optimization, thereby aiming to replace human thinking with machine learning methods and standardize development processes. Initially, the study gathers data on the loop unrolling factors as determined by profile guided optimization technology, analyzes program-specific loop feature vectors and employs cross-validation, including the Pearson correlation coefficient and feature importance ranking, to construct a dataset. Subsequent use of XGBoost to train this dataset models the decision-making process for selecting the most effective loop unrolling factor. The final step involves integrating XGBoost's trained decision tree model into GCC to calculate the optimal loop unrolling factor during actual compilation. Empirical results on the RISC-V platform indicate that this new method, when tested against the SPEC CPU 2006 benchmark suite, offers up to 6.18% improvement in performance over the existing heuristic approach. It provides a new method for loop unrolling in compilers, and provides an innovative guide for the application of machine learning in compilers. [ABSTRACT FROM AUTHOR]

This paper presents a framework that integrates Large Language Models (LLMs) into translation validation, targeting LLVM compiler transformations where formal verification tools fall short. Our framework utilizes the existing tools, like Alive2, to perform initial validation. For transformations deemed unsolvable by traditional methods, our approach leverages fine-tuned LLMs to predict soundness or unsoundness, with subsequent fuzzing applied to identify counterexamples for unsound transformations. Our approach has proven effective in complex scenarios, such as deep-learning accelerator designs, enhancing the reliability of compiler transformations. [ABSTRACT FROM AUTHOR]

The primary objective of the paper is to predict the price of the "Toyota Cars" based on the requirement of the customer. The primary need is to identify the minimum price of the "Toyota Cars" based on the 37 key attributes/factors. The levels of each factor in the data set varies from 2 to 312, moreover the data sets used for the analysis, contain both feature as well as numerical based data. The data sets are taken from the "KAGGLE" an open-source real time data provider. The total number of data available for the analysis is 1442, since the data are large and the factors are more with different levels, the prediction done using the Machine Learning (ML) approach. Ridge regression, a supervised machine learning algorithm is used to predict the sales price of the "Toyota Cars", thereby identifying the minimum price based on different combinations of attributes. The prime novelty lies on the usage of the Ridge regression, a linear regression algorithm for the prediction process. The ridge regression model was developed by means of the Python programming and Google Colab, an open-source online compiler for the ML was used for the code compilation process. Based on the result, the minimum price was found to be 6520.8 dollars, with the basic customer requirements such as Model: Corolla 1.6-16v, Type: Sedan, 4 Doors, CNG, 69hp, 1854 cc, with air conditioner, centre lock and power steering. [ABSTRACT FROM AUTHOR]

Leakage-resilient cryptography aims to protect cryptographic primitives from so-called "side channel attacks" that exploit their physical implementation to learn their input or secret state. Starting from the works of Ishai, Sahai and Wagner (CRYPTO'03) and Micali and Reyzin (TCC'04), most works on leakage-resilient cryptography either focus on protecting general computations, such as circuits or multiparty computation protocols, or on specific non-interactive primitives such as storage, encryption, and signatures. This work focuses on leakage resilience for the middle ground, namely for distributed and interactive cryptographic primitives. Our main technical contribution is designing the first secret sharing scheme that is equivocal, resists adaptive probing of a constant fraction of bits from each share, while incurs only a constant blowup in share size. Equivocation is a strong leakage-resilience guarantee, recently introduced by Hazay et al. (ITC, 2021). Our construction is obtained via a general compiler which we introduce, that transforms any secret sharing scheme into an equivocal scheme against adaptive leakage. An attractive feature of our compiler is that it respects additive reconstruction; namely, if the original scheme has additive reconstruction, then the transformed scheme has linear reconstruction. We extend our compiler to a general paradigm for protecting distributed primitives against leakage and show its applicability to various primitives, including secret sharing, verifiable secret sharing, function secret sharing, distributed encryption and signatures, and distributed zero-knowledge proofs. For each of these primitives, our paradigm transforms any construction of the primitive into a scheme that resists adaptive party corruptions, as well as adaptive probing leakage of a constant fraction of bits in each share when the share is stored in memory (but not when it is used in computations). Moreover, the transformation incurs only a constant blowup in the share size and respects additive reconstruction—an important feature for several of these primitives, such as function secret sharing and distributed encryption. [ABSTRACT FROM AUTHOR]

CodePeer is a static analysis tool based on compiler optimization techniques such as static single assignment and value numbering. CodePeer infers and reports on implicit preconditions for each function of the program, based on limitations it identifies within the algorithm of the function. Presuming these inferred preconditions are satisfied, CodePeer then simulates the execution of each function, and identifies places where a program might still fail at run time, due to violating some run-time check or an error that leads to undefined behavior. CodePeer uses static single assignment and global value numbering to ensure that the determination of possible run-time values of each variable and expression encountered during the simulation of the execution of each function is both sound and precise. The approximations performed to ensure that the determination of possible values converges in the face of loops and recursion are systematic and based on the kinds of conservative analysis performed by compiler optimizers. The output of CodePeer includes for each function an enumeration of its global inputs and global outputs and inferred preconditions and postconditions. The output also includes, interspersed within a listing of the source of the function, an identification of the places where possible run-time failures or undefined behavior could occur. This output is designed to support code review, which gives the tool its CodePeer name. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has significantly advanced artificial intelligence (AI); however, frameworks such as PyTorch, ONNX, and TensorFlow are optimized for general‐purpose GPUs, leading to inefficiencies on specialized accelerators such as neural processing units (NPUs) and processing‐in‐memory (PIM) devices. These accelerators are designed to optimize both throughput and energy efficiency but they require more tailored optimizations. To address these limitations, we propose the NEST compiler (NEST‐C), a novel DL framework that improves the deployment and performance of models across various AI accelerators. NEST‐C leverages profiling‐based quantization, dynamic graph partitioning, and multi‐level intermediate representation (IR) integration for efficient execution on diverse hardware platforms. Our results show that NEST‐C significantly enhances computational efficiency and adaptability across various AI accelerators, achieving higher throughput, lower latency, improved resource utilization, and greater model portability. These benefits contribute to more efficient DL model deployment in modern AI applications. [ABSTRACT FROM AUTHOR]

As Graphics Processing Units (GPUs) evolve for general-purpose computations besides inherently fault-tolerant graphics programs, soft error reliability becomes a first-class citizen in program design. Especially, safety-critical systems utilizing GPU devices need to employ fault-tolerance techniques to recover from errors in hardware components. While software-level redundancy approaches, based on the replication of the application code, offer high reliability for safe program execution, it is essential to perform redundancy by utilizing parallel execution units in the target architecture not to hurt performance with redundant computations. In this work, we propose redundancy approaches using the parallel GPU cores and implement a compiler-level redundancy framework that enables the programmer to configure the target GPGPU program for redundant execution. We run redundant executions for GPGPU programs from the PolyBench benchmark suite by applying our kernel-level redundancy approaches and evaluate their performance by considering the parallelism level of the programs. Our results reveal that redundancy approaches utilizing parallelism offered by GPU cores yield higher performance for redundant executions, while the programs that already make use of parallel GPU cores in their original form suffer from overhead caused by contention among redundant threads. [ABSTRACT FROM AUTHOR]

We discuss a novel approach for constructing deterministic reactive systems that revolves around a temporal model that incorporates a multiplicity of timelines. This model is central to Lingua Franca (LF), a polyglot coordination language and compiler toolchain we are developing for the definition and composition of concurrent components called reactors, which are objects that react to and emit discrete events. Our temporal model differs from existing models like the logical execution time (LET) paradigm and synchronous languages in that it reflects that there are always at least two distinct timelines involved in a reactive system; a logical one and a physical one—and possibly multiple of each kind. This article explains how the relationship between events across timelines facilitates reasoning about consistency and availability across components in cyber-physical systems (CPSs). [ABSTRACT FROM AUTHOR]

In recent years, various solutions have been developed to control power electronic converters using devices available on the market that are powerful and easy to use. These solutions, in most cases, offer high performance. However, these have high implementation costs because the required devices are expensive. For this reason, this document presents the implementation of two discrete-time controllers widely used in the literature for a boost converter implemented on a low-cost platform. The objective is to obtain a constant voltage at the converter's output for photovoltaic system applications. The proportional-integral control is implemented as the first case, and the second case is a sliding mode control. In addition, a prior analysis is presented through simulation. Both control algorithms are implemented on the TMS320F28379D microcontroller from Texas Instruments through the same manufacturer's integrated development software based on an optimized C/C++ language compiler. The results of the non-linear algorithm reveal better performance in reducing the time response, the overshoot of the transient state, and the steady-state error. Finally, the significant economic savings associated with the implementation costs of the controllers tested on a low-cost platform differentiate this work from other similar ones. [ABSTRACT FROM AUTHOR]

Formally verified compilers and formally verified static analyzers are a solution to the problem that certain industries face when they have to demonstrate to authorities that the object code they run truly corresponds to its source code and that it satisfies certain properties. From a scientific and technological point of view, they are a challenge: not only a number of nontrivial invariants and algorithms must be proved to be correct, but also the implementation must be reasonably effective so that the tools operate within reasonable time. Many optimizations in compilers rely on static analysis, and thus a formally verified compiler entails formally verified static analyses. In this paper, we explain some difficulties, possible solutions, design choices, and trade-offs pertaining to verified static analysis, in particular, when the solution of the analysis is expressed as some form of tree, map, or set. [ABSTRACT FROM AUTHOR]

Summary: Containers have taken over large swaths of cloud computing as the most convenient way of packaging and deploying applications. The features that containers offer for packaging and deploying applications translate to high performance computing (HPC) as well. At The National Oceanic and Atmospheric Administration, containers provide an easy way to build and distribute complex HPC applications, allowing faster collaboration, portability, and experiment computer environment reproducibility amongst the scientific community. The challenge arises when applications rely on message passing interface (MPI). This necessitates investigation into how to properly run these applications with their own unique requirements and produce performance on par with native runs. We investigate the MPI performance for benchmarks and containerized climate models for various containers covering selection of compiler and MPI library combinations from the Cray provided programming environments on the Cray XC supercomputer GAEA. Performance from the benchmarks and the climate models shows that for the most part containerized applications perform on par with the natively built applications when the system optimized Cray MPICH libraries are bound into the container, and the hybrid model containers have poor performance in comparison. We also describe several challenges and our solutions in running these containers, particularly challenges with heterogeneous jobs for the containerized model runs. [ABSTRACT FROM AUTHOR]

While the Xunzi 荀子 is often taken as a coherent text authored by the historical Xun Kuang 荀況 (ca. 314–ca. 238 BCE), it exhibits discontinuities and high rates of repetition, suggesting that many chapters are compilations, not single-author texts. Some meaningful parallels indicate the presence of a skillful compiler who collected variations on the same topic. The best candidate for such an editor appears to be Liu Xiang 劉向 (79–8 BCE). While Liu claimed to have deleted a large number of "duplicates" from his materials, he also seems to have purposefully integrated some of them into his final version of the text. Thereafter, a pian 篇 (chapter) was no longer the same as its pre-Han ancestor; it had become longer and more comprehensive, yet not a linear unit of meaning. [ABSTRACT FROM AUTHOR]

Function layout, also known as function reordering or function placement, is one of the most effective profile-guided compiler optimizations. By reordering functions in a binary, compilers can improve the performance of large-scale applications or reduce the compressed size of mobile applications. Although the technique has been extensively studied in the context of large-scale binaries, no study has thoroughly investigated function layout algorithms on mobile applications. In this article, we develop the first principled solution for optimizing function layouts in the mobile space. To this end, we identify two key optimization goals: reducing the compressed code size and improving the cold start-up time of a mobile application. Then, we propose a formal model for the layout problem, whose objective closely matches our goals, and a novel algorithm for optimizing the layout. The method is inspired by the classic balanced graph partitioning problem. We have carefully engineered and implemented the algorithm in an open-source compiler, Low-level Virtual Machine (LLVM). An extensive evaluation of the new method on large commercial mobile applications demonstrates improvements in start-up time and compressed size compared to the state-of-the-art approach.1 [ABSTRACT FROM AUTHOR]

The research paper represents a novel approach to the design and optimization of a compiler for a domain-specific language (DSL) focused on geometric shape creation and manipulation. The primary objective is to develop a compiler capable of generating efficient machine code while offering users a high level of abstraction. The paper begins with an overview of DSLs and compilers, emphasizing their importance in software development. Next, it outlines the specific requirements of the geometric shape DSL and proposes a compiler design that addresses them. This innovative approach considers DSL's unique features, such as shape creation and manipulation, and aims to generate high-quality machine code. The paper also discusses optimization techniques to enhance the generated code's quality and performance, including loop unrolling and instruction scheduling. These optimizations are particularly suited to the DSL, which focuses on geometric shape creation and manipulation and are integral to achieving efficient machine code generation. In conclusion, the paper emphasizes the novelty of this approach to DSL compiler design and anticipates exciting results from testing the compiler developed for the geometric shape DSL. [ABSTRACT FROM AUTHOR]

Binary code similarity detection plays a crucial role in various applications within binary security, including vulnerability detection, malicious software analysis, etc. However, existing methods suffer from limited differentiation in binary embedding representations across different compilation environments, lacking dynamic high-level semantics. Moreover, current approaches often neglect multi-level semantic feature extraction, thereby failing to acquire precise semantic information about the binary code. To address these limitations, this paper introduces a novel detection solution called BinBcla. This method employs an enhanced pre-training model to generate instruction embeddings with dynamic semantics for binary functions. Subsequently, multi-feature fusion technique is utilized to extract local semantic information and long-distance global features from the code, respectively, employing self-attention to comprehend the structure information of the code. Finally, an improved cosine similarity method is employed to learn relationships among all elements of the distance vectors, thereby enhancing the model's robustness to new sample functions. Experiments are conducted across different architectures, compilers, and optimization levels. The results indicate that BinBcla achieves higher accuracy, precision and F1 score compared to existing methods. [ABSTRACT FROM AUTHOR]

Mainstream compilers perform a multitude of analyses and optimizations on the given input program. Each analysis (such as points-to analysis) may generate a program-abstraction (such as points-to graph). Each optimization is typically composed of multiple alternating phases of inspection of such program-abstractions and transformations of the program. Upon transformation of a program, the program-abstractions generated by various analyses may become inconsistent with the modified program. Consequently, the correctness of the downstream inspection (and consequent transformation) phases cannot be ensured until the relevant program-abstractions are stabilized; that is, the program-abstractions are either invalidated or made consistent with the modified program. In general, the existing compiler frameworks do not perform automated stabilization of the program-abstractions and instead leave it to the compiler pass writers to deal with the complex task of identifying the relevant program-abstractions to be stabilized, the points where the stabilization is to be performed, and the exact procedure of stabilization. In this article, we address these challenges by providing the design and implementation of a novel compiler-design framework called Homeostasis. Homeostasis automatically captures all the program changes performed by each transformation phase, and later, triggers the required stabilization using the captured information, if needed. We also provide a formal description of Homeostasis and a correctness proof thereof. To assess the feasibility of using Homeostasis in compilers of parallel programs, we have implemented our proposed idea in IMOP, a compiler framework for OpenMP C programs. Furthermore, to illustrate the benefits of using Homeostasis, we have implemented a set of standard data-flow passes, and a set of involved optimizations that are used to remove redundant barriers in OpenMP C programs. Implementations of none of these optimizations in IMOP required any additional lines of code for stabilization of the program-abstractions. We present an evaluation in the context of these optimizations and analyses, which demonstrates that Homeostasis is efficient and easy to use. [ABSTRACT FROM AUTHOR]

In this paper, algorithms are used to compute distance-based topological indices for the Complete Binary Tree (CBT) and the Complete Ternary Tree (CTT). Computation of distance-based topological indices is complex for varied heights of CBT and CTT. Hence designed algorithms to compute distance between any-to-any vertex made this possible to compute the required topological indices for CBT and CTT. The distance calculator algorithm designed for this study can also be customized in digital chemical structures, mathematical chemistry, network traffic control in wireless networks, search applications, high bandwidth routing, parse construction in compilers, and memory management. [ABSTRACT FROM AUTHOR]

Solidity, the language utilized for developing smart contracts, has been gaining increased importance in blockchain system. Ensuring bug-free of its accompanying language compiler, which converts the contract source codes into executables finally deployed on the blockchain, is thus of paramount importance. This study presents DeSCDT, a Deep learning-based Solidity Compiler Differential Testing approach, to explore possible defects in Solidity compiler. At the core lies a well-behaving deep contract generator following the Transformer architecture and learnt with diverse contract code. From an initial seed pool of contracts carefully picked through semantic encoding and clustering, the generator is capable of stably producing highly syntactic-valid and functional-rich smart contracts, with three meticulously formulated generation strategies and a set of mutation operations. Subsequently, in the meantime of compiling these generated contracts to trigger compiler crashes, a differential testing environment is set up to explore misoptimization bugs, by observing the inconsistencies between the outcomes and the aspects including opcode size of the optimized and non-optimized bytecodes. For the experiments, the syntactic validity and diversity of the contracts generated with DeSCDT, as well as its ability in discovering compiler defects, are investigated. The findings indicate that DeSCDT can effectively generate syntactically correct contracts with a pass rate of 90.8% alongside high diversity. Among the contracts tested for a 24-h running of DeSCDT, 37.4% of them expose inconsistencies across the optimized and non-optimized version of the same contract. Six bugs that could trigger direct crashing of the compiler have also been detected. [ABSTRACT FROM AUTHOR]

In error-resilience applications, Approximate Multipliers (AMs) play an essential role, having a trade-off between design metrics (DM) and error metrics (EM). Thus, rounding and recursive-based AMs is developed in this paper in order to enhance both DM and EM. Initially, the process entails selecting the least significant and greater significant bits from the n-bit input operand in the n/2-bit stage. These selected bits undergo rounding to the nearest power of two within the architecture of the proposed AMs. Subsequently, the final output multiplication product is derived through a combination of rounding, adders, and shifters. To evaluate the efficacy of this approach, all proposed and prior AMs are implemented in Verilog Code, covering input sizes extending from 8 to 32 bits. These implementations are then synthesized with the Cadence RTL compiler and simulated using both MATLAB and Vivado for image smoothing filter (ISF) applications. Additionally, the DM and EM values of the proposed AMs provide improvement in contrast to prior AMs. Furthermore, the proposed AMs offer an average delay reduction of 21.35% and a 40% reduction in power consumption compared to previous AMs. Moreover, when integrated into the ISF, the proposed AMs exhibit better maximal peak signal-to-noise ratio and structural similarity-index metric compared to existing alternatives, rendering them well-suited for Error- Resilience and energy efficient applications. [ABSTRACT FROM AUTHOR]

Widely used compilers like GCC and LLVM usually have hundreds of optimizations controlled by optimization flags, which are enabled or disabled during compilation to improve the runtime performance (e.g., small execution time) of the compiler program. Due to the large number of optimization flags and their combination, it is difficult for compiler users to manually tune compiler optimization flags. In the literature, a number of autotuning techniques have been proposed, which tune optimization flags for a compiled program by comparing its actual runtime performance with different optimization flag combinations. Due to the huge search space and heavy actual runtime cost, these techniques suffer from the widely recognized efficiency problem. To reduce the heavy runtime cost, in this article we propose a lightweight learning approach that uses a small number of actual runtime performance data to predict the runtime performance of a compiled program with various optimization flag combinations. Furthermore, to reduce the search space, we design a novel particle swarm algorithm that tunes compiler optimization flags with the prediction model. To evaluate the performance of the proposed approach, CompTuner, we conduct an extensive experimental study on two popular C compilers, GCC and LLVM, with two widely used benchmarks, cBench and PolyBench. The experimental results show that CompTuner significantly outperforms the six compared techniques, including the state-of-the-art technique BOCA. [ABSTRACT FROM AUTHOR]

Edge devices, due to their limited computational and storage resources, often require the use of compilers for program optimization. Therefore, ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI. One widely used testing method for this purpose is fuzz testing, which detects bugs by inputting random test cases into the target program. However, this process consumes significant time and resources. To improve the efficiency of compiler fuzz testing, it is common practice to utilize test case prioritization techniques. Some researchers usemachine learning to predict the code coverage of test cases, aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases. Nevertheless, these methods can only forecast the code coverage of the compiler at a specific optimization level, potentially missing many optimization-related bugs. In this paper, we introduce C-CORE (short for Clustering by Code Representation), the first framework to prioritize test cases according to their code representations, which are derived directly from the source codes. This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs. Specifically, we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer. Using this pre-trained model, we then train two downstreammodels: one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs. Subsequently, we cluster the test cases according to their code representations and select the highest-scoring test case fromeach cluster as the high-quality test case. This reduction in redundant testing cases leads to time savings. Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities, and C-CORE significantly enhances testing efficiency. Across four datasets, C-CORE increases the average of the percentage of faults detected (APFD) value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases. When compared to the best results fromapproaches using predicted code coverage, C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%. [ABSTRACT FROM AUTHOR]

In the landscape of software development, the selection of compilation tools and settings plays a pivotal role in the creation of executable binaries. This diversity, while beneficial, introduces significant challenges for reverse engineers and security analysts in deciphering the compilation provenance of binary code. To this end, we present MulCPI, short for Multi-representation Fusion-based Compilation Provenance Identification, which integrates the features collected from multiple distinct intermediate representations of the binary code for better discernment of the fine-grained function-level compilation details. In particular, we devise a novel graph-oriented neural encoder improved upon the gated graph neural network by subtly introducing an attention mechanism into the neighborhood nodes' information aggregation computation, in order to better distill the more informative features from the attributed control flow graph. By further integrating the features collected from the normalized assembly sequence with an advanced Transformer encoder, MulCPI is capable of capturing a more comprehensive set of features manifesting the multi-faceted lexical, syntactic, and structural insights of the binary code. Extensive evaluation on a public dataset comprising 854,858 unique functions demonstrates that MulCPI exceeds the performance of current leading methods in identifying the compiler family, optimization level, compiler version, and the combination of compilation settings. It achieves average accuracy rates of 99.3%, 96.4%, 90.7%, and 85.3% on these tasks, respectively. Additionally, an ablation study highlights the significance of MulCPI's core designs, validating the efficiency of the proposed attention-enhanced gated graph neural network encoder and the advantages of incorporating multiple code representations. [ABSTRACT FROM AUTHOR]

Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices' hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems' development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity's complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs. [ABSTRACT FROM AUTHOR]

The study's object is the process of extracting the characteristics of lambda terms, which indicate the optimality of the reduction strategy and increase the productivity of compilers and interpreters. The solution to the problem of extracting specific strategy priority data from lambda terms using Machine Learning methods was considered. Such data was extracted using the large language model Microsoft CodeBERT, which was trained to solve the problem of summarizing the software code. The resulting matrices of embeddings were used to obtain vectors of average embeddings of size 768 and a latent space of size 8 thousand. Further, vectors of average embeddings were used for cluster analysis using the DBSCAN and Hierarchical Agglomerative clustering methods. The most informative variables affecting clustering were determined. Next, the clustering results were compared with the priorities of reduction strategies, which showed the impossibility of separating terms with RI priority. A feature of the obtained results is using machine learning methods to obtain knowledge. The clustering results showed many of the same informative variables, which is explained by the similar shape of the obtained clusters. The results of comparing the clustering values with the real priority are explained by the impossibility of clearly determining the priority and the use of the Microsoft CodeBERT model, which was not trained for the analysis of lambda terms. The proposed approach can find application in the development of compilers and interpreters of functional programming languages, allowing to analyze the code and extract important data to optimize the execution of programs. The obtained data can be used to develop rules aimed at improving the efficiency of compilation and interpretation. [ABSTRACT FROM AUTHOR]

The Java virtual machine (JVM) is the cornerstone of the Java platforms. A JVM's exception handling implementation interrupts, when the objective application encounters an exception (or an error), the normal execution of the application and performs specific handling tasks. However, little research has been done in systematically validating JVMs' exception handling implementations—test programs or even applications need to be carefully designed for throwing/catching exceptions at runtime; a JVM's exception handling implementation is also complicated, making it challenging to design tests for testing all of its functionalities. Inspired by the recent success of fuzz testing of compilers and JVM implementations, we introduce EHCBuilder, the first technique for fuzzing JVMs' exception handling implementations. The key idea is to construct exception handling chains, each of which abstracts a program's execution into a sequence of exception throwings, catchings, and/or handlings. A classfile seed can then be mutated into test programs with diverse exception handling chains, enabling (1) exceptions to be continuously thrown and caught at runtime, and (2) JVMs' exception handling implementations to be much more thoroughly tested. We have implemented EHCBuilder and evaluated EHCBuilder on popular JVM implementations including OpenJDK's HotSpot, Eclipse's OpenJ9, Azul's Zulu, and Oracle's GraalVM. Our results show that EHCBuilder can generate programs with very intricate exception handling chains and reveal differences among JVMs' exception handling implementations: Up to thousands of lines of source code in HotSpot's exception handling implementation are covered more than the original benchmarks; during 39 K iterations, EHCBuilder generates exception handling chains of different lengths, revealing 258 runtime differences. We classify the differences into four categories, and reveal a fast throw issue confirmed by HotSpot developers and another initCause issue confirmed by the OpenJ9 community. [ABSTRACT FROM AUTHOR]

We present a methodology to develop verified smart contracts. We write smart contracts, their specifications and implementations in the verification-friendly language Dafny. In our methodology the ability to write specifications, implementations and to reason about correctness is a primary concern. We propose a simple, concise, yet powerful solution for reasoning about contracts that have external calls. This includes arbitrary re-entrancy, which is a major source of bugs and attacks in smart contracts. Although we do not yet have a compiler from Dafny to Ethereum Virtual Machine bytecode, the results we obtain from the Dafny code can reasonably be assumed to translate to contracts written in languages like Solidity. As a result our approach can readily be used to develop and deploy safer contracts. [ABSTRACT FROM AUTHOR]

Field Programmable Gate Arrays (FPGAs) have garnered significant attention in the development and enhancement of target identification algorithms that employ YOLOv2 models and FPGAs, owing to their adaptability and userfriendliness. The Simulink HDL compiler was utilized to design, simulate, and implement our proposed design. In an effort to rectify this, this paper presents a comprehensive programming and design proposal. The implementation of the YOLOv2 algorithm for real-time vehicle detection on the Xilinx® Zynq-7000 System-on-a-chip is proposed in this work. Real-time testing of the synthesised hardware revealed that it can process Full HD video at a rate of 16 frames per second. On the Xilinx Zynq-7000 SOC, the estimated dynamic power consumption is less than 90 mW. When comparing the results of the proposed work to those of other simulations, it is observed that resource utilization is enhanced by around 204 k (75%) LUT, 305 (12%) DSP, and 224 k (41%) flip-flops at 200 MHz. [ABSTRACT FROM AUTHOR]

Image style transfer is a major area of study in image processing and has applications in creative production, special effects for film and television, and other areas. Image style transfer is the process of using style transfer technology to change a common image into one with a different style without changing the content. Image style transfer methods are mainly divided into traditional image style transfer methods and deep learning image style transfer methods. The two primary classifications of picture style transfer techniques are deep learning technologies and conventional methods. Traditional image style transfer methods have poor results and are difficult to apply in people's lives. With the quick advancements in machine learning, digital image processing, and computer vision, deep learning image style transfer methods have received widespread attention from researchers. Most of these methods use convolutional neural networks to achieve image style transfer on the premise of paired data sets, but obtaining paired data sets is difficult and costly. Accordingly, it is of great significance to study unpaired images to implement style transfer algorithms. The primary focus of this study is the CycleGAN network-based picture style transfer technique, and improves this algorithm in content compiler, style compiler. It is applied to the generation of night road conditions during autonomous driving training. [ABSTRACT FROM AUTHOR]

The music-notation software Lilypond generates high-quality music typography from a plain-text input format; almost every aspect of the program can be customized and programmed, and the system lends itself well to automation and batch processing. Lilypond offers a minimal computing alternative to bloated, costly, and hegemonic graphical programs. Like many free software tools, however, Lilypond still exacts a cost in time and training needed to overcome an unwieldly interface and adapt the tool for scholarly purposes. The author developed a system called lirio that enabled the production of two critical editions and a monograph in Lilypond (integrated with LaTeX). The system provides a new semantic-markup interface to Lilypond that enables scholars to think about typography separately from musical content; a range of expanded Lilypond functionality including incipit staves, mensural coloration brackets, and editorial annotations; and a stricter input format that makes Lilypond files easier to maintain. The author also developed the prototype ly2mei compiler to demonstrate how Lilypond files can be converted to MEI-XML, overcoming a major limitation in Lilypond's export abilities. The article argues that scholars will be best served by a simple, consistent, meaningful interface in a format that can be shared and converted. An extension of Lilypond like lirio demonstrates the considerable potential of this tool for enterprising and patient scholars whose needs are not met by other tools. Lilypond provides a case study for how to make open-source, free-license tools work for our own needs as digital humanists. Lilypond music-notation software as a case study: how to make open-source tools work for us. [ABSTRACT FROM AUTHOR]

The object of this research is the process of compiler optimization, as it is essential in modern software development, particularly in functional programming languages like Lambda Calculus. Optimization strategies directly impact interpreter and compiler performance, influencing resource efficiency and program execution. While functional programming compilers have garnered less attention regarding optimization efforts than their object-oriented counterparts, Lambda Calculus’s complexity poses unique challenges. Bridging this gap requires innovative approaches like leveraging machine learning techniques to enhance optimization strategies. This study focuses on leveraging machine learning to bridge the optimization gap in functional programming, particularly within the context of Lambda Calculus. This study delves into the extraction features from Lambda terms related to reduction strategies by applying machine learning. Previous research has explored various approaches, including analyzing reduction step complexities and using sequence analysis Artificial Neural Networks (ANNs) with simplified term representation. This research aims to develop a methodology for extracting comprehensive term data and providing insights into optimal reduction priorities by employing Large Language Models (LLMs). Tasks were set to generate embeddings from Lambda terms using LLMs, train ANN models to predict reduction steps, and compare results with simplified term representations. This study employs a sophisticated blend of machine learning algorithms and deep learning models as a method of analyzing and predicting optimal reduction paths in Lambda Calculus terms. The result of this study is a method that showed improvement in determining the number of reduction steps by using embeddings. Conclusions: The findings of this research offer significant implications for further advancements in compiler and interpreter optimization. This study paves the way for future research to enhance compiler efficiency by demonstrating the efficacy of employing LLMs to prioritize normalization strategies. Using machine learning in functional programming optimization opens avenues for dynamic optimization strategies and comprehensive analysis of program features. [ABSTRACT FROM AUTHOR]

Real-Time rendering is the technique which allows us to have graphical applications in our everyday life, whether it is a 3D game or a tool with graphical user interface. Nowadays graphics rendering is handled by the GPU (Graphics Processing Unit) in our device. There are many layers of abstraction above the programming of GPUs through libraries and graphics engines, though the most low-level way of accessing a GPU in user-mode applications is using a Graphics API. Due to the need of high performance and low-level capabilities usually these APIs are used from C or C++, but we realized the need to utilize these APIs in higher level languages as well. In our approach we're using the.NET C# language for developing multi-platform real-time graphical applications instead of the C or C++ languages. Using the modern.NET environment, we're able to use Graphics APIs for rendering onto common.NET UI Frameworks while consuming all our previously implemented C# libraries and.NET technologies in the same application. To maintain compatibility with multiple platforms we're developing a library system allowing the use different Graphics APIs from the same C# source-code. The library system contains a Graphics API abstraction layer with multiple Graphics API implementations of this layer in C# and a C# to shader language compiler for cross-API shader development in C#. In this paper, we're proposing our considerations for implementing a library to be able to use the Vulkan and OpenGL APIs through a single C# codebase. We provide solutions for multi-platform rendering and dealing with the low-level challenges of using the two deeply different APIs, while maintaining performance capable to do real-time rendering. [ABSTRACT FROM AUTHOR]

Choreographies are coordination plans for concurrent and distributed systems, which define the roles of the involved participants and how they are supposed to work together. In the paradigm of choreographic programming, choreographies are programs that can be compiled into executable implementations. In this article, we present Choral, the first choreographic programming language based on mainstream abstractions. The key idea in Choral is a new notion of data type, which allows for expressing that data is distributed over different roles. We use this idea to reconstruct the paradigm of choreographic programming through object-oriented abstractions. Choreographies are classes, and instances of choreographies are objects with states and behaviours implemented collaboratively by roles. Choral comes with a compiler that, given a choreography, generates an implementation for each of its roles. These implementations are libraries in pure Java, whose types are under the control of the Choral programmer. Developers can then modularly compose these libraries in their programs, to participate correctly in choreographies. Choral is the first incarnation of choreographic programming offering such modularity, which finally connects more than a decade of research on the paradigm to practical software development. The integration of choreographic and object-oriented programming yields other powerful advantages, where the features of one paradigm benefit the other in ways that go beyond the sum of the parts. On the one hand, the high-level abstractions and static checks from the world of choreographies can be used to write concurrent and distributed object-oriented software more concisely and correctly. On the other hand, we obtain a much more expressive choreographic language from object-oriented abstractions than in previous work. This expressivity allows for writing more reusable and flexible choreographies. For example, object passing makes Choral the first higher-order choreographic programming language, whereby choreographies can be parameterised over other choreographies without any need for central coordination. We also extend method overloading to a new dimension: specialisation based on data location. Together with subtyping and generics, this allows Choral to elegantly support user-defined communication mechanisms and middleware. [ABSTRACT FROM AUTHOR]

Local-first software manages and processes private data locally while still enabling collaboration between multiple parties connected via partially unreliable networks. Such software typically involves interactions with users and the execution environment (the outside world). The unpredictability of such interactions paired with their decentralized nature make reasoning about the correctness of local-first software a challenging endeavor. Yet, existing solutions to develop local-first software do not provide support for automated safety guarantees and instead expect developers to reason about concurrent interactions in an environment with unreliable network conditions. We propose LoRe, a programming model and compiler that automatically verifies developer-supplied safety properties for local-first applications. LoRe combines the declarative data flow of reactive programming with static analysis and verification techniques to precisely determine concurrent interactions that violate safety invariants and to selectively employ strong consistency through coordination where required. We propose a formalized proof principle and demonstrate how to automate the process in a prototype implementation that outputs verified executable code. Our evaluation shows that LoRe simplifies the development of safe local-first software when compared to state-of-the-art approaches and that verification times are acceptable. [ABSTRACT FROM AUTHOR]

Due to the absence of communication and coordination with external spacecraft, non-cooperative spacecraft present challenges for the servicing spacecraft in acquiring information about their pose and location. The accurate segmentation of non-cooperative spacecraft components in images is a crucial step in autonomously sensing the pose of non-cooperative spacecraft. This paper presents a novel overlay accelerator of DeepLab Convolutional Neural Networks (CNNs) for spacecraft image segmentation on a FPGA. First, several software–hardware co-design aspects are investigated: (1) A CNNs-domain COD instruction set (Control, Operation, Data Transfer) is presented based on a Load–Store architecture to enable the implementation of accelerator overlays. (2) An RTL-based prototype accelerator is developed for the COD instruction set. The accelerator incorporates dedicated units for instruction decoding and dispatch, scheduling, memory management, and operation execution. (3) A compiler is designed that leverages tiling and operation fusion techniques to optimize the execution of CNNs, generating binary instructions for the optimized operations. Our accelerator is implemented on a Xilinx Virtex-7 XC7VX690T FPGA at 200 MHz. Experiments demonstrate that with INT16 quantization our accelerator achieves an accuracy (mIoU) of 77.84%, experiencing only a 0.2% degradation compared to that of the original fully precision model, in accelerating the segmentation model of DeepLabv3+ ResNet18 on the spacecraft component images (SCIs) dataset. The accelerator boasts a performance of 184.19 GOPS/s and a computational efficiency (Runtime Throughput/Theoretical Roof Throughput) of 88.72%. Compared to previous work, our accelerator improves performance by 1.5× and computational efficiency by 43.93%, all while consuming similar hardware resources. Additionally, in terms of instruction encoding, our instructions reduce the size by 1.5× to 49× when compiling the same model compared to previous work. [ABSTRACT FROM AUTHOR]

This paper introduces the Julia programming language as a dynamic, cost-effective, and efficient framework for implementing structural analysis packages. To achieve this, the finite element method was implemented for plane frames addressing the elastic instability problem through the Finite Element Method (FEM). Julia is a language open source, multiplatform, high-level and high-performance for technical and scientific computing, its compiler allows you to achieve speeds comparable to languages such as C and FORTRAN, but with more productive development dynamics due to its programming flexibility. Benchmarks between Julia and MATLAB are employed to discuss the processing costs, the programming techniques and paradigms used for computational performance. The results demonstrate that Julia performed the same analysis as the language used for comparison in 88.40% of the time, in addition to the fact that in loops comparisons case it reached 41.7% of the time for iteration, confirming its significant potential as a development tool of computational packages for structural analysis and scientific computing in general. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the Zuker RNA folding algorithm, a bioinformatics task with non-serial polyadic dynamic programming and non-uniform loop dependencies. The intricate dependence pattern is represented using affine formulas, enabling the automatic application of tiling strategies via the polyhedral method. Three source-to-source compilers—PLUTO, TRACO, and DAPT—are employed, utilizing techniques such as affine transformations, the transitive closure of dependence relation graphs, and space–time tiling to generate cache-efficient codes, respectively. A dedicated transpose code technique for non-serial polyadic dynamic programming codes is also examined. The study evaluates the performance of these optimized codes for speed-up and scalability on multi-core machines and explores energy efficiency using RAPL. The paper provides insights into related approaches and outlines future research directions within the context of bioinformatics algorithm optimization. [ABSTRACT FROM AUTHOR]