Source code accompanying the paper:Klinik, M., Jansen, J.M. & Plasmeijer, R. (2017). The Sky is the Limit: Analysing Resource Consumption Over Time Using Skylines. In N. Wu (Ed.), IFL 2017: Proceedings of the 29th Symposium on the Implementation and Application of Functional Programming Languages, Bristol, United Kingdom — August 30 - September 01, 2017 (pp. 8-1-8-12). New York: ACMCONTENTS- *.dcl/.icl: the Clean modules of the analyzer- *Spec.icl/.dcl: test cases for the corresponding Clean module- *.prj.default: original project files for the main program and the testcases. Must be renamed to .prj to compile. This is because the clean compilermodifies those files, but we don't want the modification under versioncontrol.- bash_completion.d: source this file in your .bashrc to get simplecommand-line completion for the mtasks command.- test: source code of the TestFramework, needed to run the unit tests- boxes: some brainstorming how skylines are appended and stacked- programs: example programs to demonstrate the analyzer- More information on how to compile and run this program can be found in README.txt- We ran into some problems when trying to organize the source code in subdirectories, because compile errors of the Clean compiler does not reflect module structure well enough for integration with external tools. That is why all source code is in one directory.SHORT SUMMARYIn this paper we present a static analysis for costs of higher-order workflows, where costs are maps from resource types to simple functions over time. We present a type and effect system together with an algorithm that yields safe approximations for the cost functions of programs.

Research data for the Section "4. Impact" of the article "Clava: C/C++ source-to-source compilation using LARA"

It contains the required files and instructions to obtain the results presented in sections 4.1 and 4.3.

4.1. Stress Test

Automatically instruments several large C programs so that they produce a call graph when the program executes.

To run the test use the command: clava -c stress_test.clava

Some examples (e.g., gcc.c) will only parse sucessfully on a Linux machine.

4.3. OpsCounter

Automatically instruments the NAS benchmark set so that it counts the number of source code operations executed by the kernels.

To run the test use the command: clava -c ops_counter.clava

The Python compiler market is experiencing robust growth, driven by the increasing popularity of Python for various applications and the expanding need for optimized code execution. The market's size in 2025 is estimated at $2.5 billion, reflecting a Compound Annual Growth Rate (CAGR) of 15% from 2019. This growth is fueled by several key factors. Firstly, the rise of data science, machine learning, and artificial intelligence (AI) heavily relies on Python's extensive libraries and frameworks, making compiler optimization crucial for performance improvements. Secondly, the growing adoption of cloud-based solutions is driving demand for efficient and scalable Python compilers. Businesses increasingly utilize cloud platforms for development and deployment, boosting the need for optimized code execution in these environments. Thirdly, the versatility of Python across different sectors—from web development and scripting to embedded systems—contributes to a broader user base and wider application of Python compilers. The market is segmented by application (individual, commercial) and type (cloud-based, on-premise), with cloud-based solutions gaining significant traction due to their scalability and cost-effectiveness. Major players like JetBrains, Eclipse, and Red Hat are continuously innovating to enhance compiler performance, features, and integration with various development environments. Growth is expected to continue through 2033, propelled by ongoing technological advancements and the expanding scope of Python applications. Despite the positive outlook, the market faces certain restraints. The complexity of optimizing Python, a dynamically-typed language, presents technical challenges for compiler developers. Furthermore, the availability of efficient interpreted Python environments sometimes reduces the immediate need for compiled code, especially in less performance-critical applications. However, as the scale and complexity of Python applications increase, the demand for optimized compilation is bound to grow, ultimately outweighing these limitations. Competition among established players and emerging startups is expected to remain intense, driving innovation and making the market highly dynamic and competitive. Regional growth will vary, with North America and Europe expected to maintain significant market share due to strong technological infrastructure and adoption rates, but the Asia-Pacific region is poised for rapid expansion driven by increasing technological investments and adoption in burgeoning economies like India and China.

The Python compiler market is experiencing robust growth, driven by the increasing popularity of Python in various sectors like data science, machine learning, web development, and automation. The market's expansion is fueled by the language's readability, extensive libraries (like NumPy and Pandas), and a large, supportive community. While precise figures for market size and CAGR are unavailable, considering the widespread adoption of Python and the continuous development of related tools, a reasonable estimate would place the 2025 market size at approximately $500 million. A conservative CAGR of 15% for the forecast period (2025-2033) seems plausible, reflecting the sustained demand and continuous improvements in Python compiler technology. This growth is further supported by the diverse range of companies contributing to the ecosystem, including prominent players like JetBrains, Eclipse, and Red Hat, alongside specialized firms offering enhanced IDEs and compilers. Factors limiting market growth include the relatively mature nature of the Python language itself, and the existing ecosystem of well-established interpreters. However, the ongoing demand for improved performance and specialized compiler optimization (particularly for specific application domains like embedded systems via MicroPython) will continue to stimulate market expansion. Segmentation within the market is likely driven by compiler type (e.g., just-in-time vs. ahead-of-time compilation), target platform (desktop, mobile, embedded), and licensing model (open-source vs. commercial). Future growth will be shaped by innovations in compiler technology, such as enhanced static analysis capabilities, improved code optimization techniques, and support for emerging hardware architectures. The market will likely see further consolidation as companies focus on offering comprehensive development solutions, integrating compilers with other vital developer tools.

Source code accompanying the paperMarkus Klinik, Jurriaan Hage, Jan Martin Jansen, and Rinus Plasmeijer. Predicting resource consumption of higher-order workflows. In Proceedings of PEPM 2017, Paris, France, January 18-20, 2017, pages 99–110. ACM, 2017a. ISBN 978-1-4503-4721-1.CONTENTS- *.dcl/.icl: the Clean modules of the analyzer- *.prj: project files for the main program and the test- test: test cases for the corresponding Clean module, includes source code forthe TestFramework needed to run the tests- bash_completion.d: source this file in your .bashrc to get simplecommand-line completion for the mtasks command.- programs: example programs to demonstrate the analyzer- More information on how to compile and run this program can be found in README.txtSHORT SUMMARYWe present a type and effect system for the static analysis of programs written in a simplified version of iTasks.iTasks is a workflow specification language embedded in Clean, a general-purpose functional programming language.Given costs for basic tasks, our analysis calculates an upper bound of the total cost of a workflow.The analysis has to deal with the domain-specific features of iTasks, in particular parallel and sequential composition of tasks, as well as the general-purpose features of Clean, in particular let-polymorphism, higher-order functions, recursion and lazy evaluation.Costs are vectors of natural numbers where every element represents some resource, either consumable or reusable.

Python Compiler Market Outlook

The Python Compiler market size was valued at USD 341.2 million in 2023, and it is projected to grow at a robust compound annual growth rate (CAGR) of 10.8% to reach USD 842.6 million by 2032. The market growth is primarily driven by the increasing adoption of Python as a programming language across various industries, due to its simplicity and versatility.

The growth of the Python Compiler market can be attributed to several key factors. Firstly, the rising prominence of Python in data science, machine learning, and artificial intelligence domains is a significant driver. Python’s extensive libraries and frameworks make it an ideal choice for data processing and algorithm development, leading to increased demand for efficient Python compilers. This widespread application is spurring investments and advancements in compiler technologies to support increasingly complex computational tasks. Additionally, the open-source nature of Python encourages innovation and customization, further fueling market expansion.

Secondly, the educational sector's growing emphasis on coding and computer science education is another pivotal growth factor. Python is often chosen as the introductory programming language in educational institutions due to its readability and straightforward syntax. This trend is creating a steady demand for Python compilers that are user-friendly and suitable for educational purposes. As more schools and universities integrate Python into their curriculums, the market for Python compilers is expected to grow correspondingly, supporting a new generation of programmers and developers.

Furthermore, the increasing adoption of Python by small and medium enterprises (SMEs) is propelling the market forward. SMEs are leveraging Python for various applications, including web development, automation, and data analysis, due to its cost-effectiveness and ease of use. Python’s versatility allows businesses to streamline their operations and develop robust solutions without significant financial investment. This has led to a burgeoning demand for both on-premises and cloud-based Python compilers that can cater to the diverse needs of SMEs across different sectors.

Regionally, the Python Compiler market is witnessing notable growth in North America and the Asia Pacific. North America remains a key market due to the early adoption of advanced technologies and a strong presence of tech giants and startups alike. In contrast, the Asia Pacific region is experiencing rapid growth thanks to its expanding technological infrastructure and burgeoning IT industry. Countries like India and China are emerging as significant players due to their large pool of skilled developers and increasing investment in tech education and innovation.

Component Analysis

In the Python Compiler market, the component segment is divided into software and services. The software segment encompasses the actual compiler tools and integrated development environments (IDEs) that developers use to write and optimize Python code. This segment is crucial as it directly impacts the efficiency and performance of Python applications. The demand for advanced compiler software is on the rise due to the need for high-performance computing in areas like machine learning, artificial intelligence, and big data analytics. Enhanced features such as real-time error detection, optimization techniques, and seamless integration with other development tools are driving the adoption of sophisticated Python compiler software.

The services segment includes support, maintenance, consulting, and training services associated with Python compilers. As organizations increasingly adopt Python for critical applications, the need for professional services to ensure optimal performance and scalability is growing. Consulting services help businesses customize and optimize their Python environments to meet specific needs, while training services are essential for upskilling employees and staying competitive in the tech-driven market. Additionally, support and maintenance services ensure that the compilers continue to operate efficiently and securely, minimizing downtime and enhancing productivity.

Within the software sub-segment, integrated development environments (IDEs) like PyCharm, Spyder, and Jupyter Notebooks are gaining traction. These IDEs not only provide robust compiling capabilities but also offer features like debugging, syntax highlighting, and version control, which streamline the development process. The increasing complexity of software develo

This dataset does not contain data, only code.

The oraqle compiler lets you generate arithmetic circuits from high-level Python code. It also lets you generate code using HElib.

This repository uses a fork of fhegen as a dependency and adapts some of the code from [fhegen](https://github.com/Crypto-TII/fhegen), which was written by Johannes Mono, Chiara Marcolla, Georg Land, Tim Güneysu, and Najwa Aaraj.

Setting up

The best way to get things up and running is using a virtual environment:

- Set up a virtualenv using `python3 -m venv venv` in the directory.

- Enter the virtual environment using `source venv/bin/activate`.

- Install the requirements using `pip install requirements.txt`.

- *To overcome import problems*, run `pip install -e .`, which will create links to your files (so you do not need to re-install after every change).

Market Analysis for Python Compiler The global Python compiler market size was valued at USD XXX million in 2025 and is projected to grow at a CAGR of XX% from 2025 to 2033. The increasing adoption of Python in various industries, such as data science, machine learning, and web development, is driving market growth. Additionally, the cloud-based deployment model is gaining traction due to its scalability and cost-effectiveness. The presence of established players like JetBrains, Eclipse, and MicroPython strengthens the market's competitive landscape. Key trends shaping the Python compiler market include the rise of low-code and no-code development platforms, the integration of artificial intelligence and machine learning capabilities, and the growing demand for cloud-native applications. However, the market faces certain restraints, such as the availability of alternative compilers and the security concerns associated with cloud-based solutions. North America is the largest region in the market, followed by Europe and Asia Pacific. The increasing adoption of Python in emerging economies is expected to drive growth in these regions.

This dataset contains 493k LLVM-IRs taken from a wide range of projects and source programming languages, and includes labels for several compiler data analyses. We also include the logs for the machine learning jobs which produced our published experimental results.

The uncompressed dataset uses the following layout:

labels/

Directory containing machine learning features and labels for programs for compiler data flow analyses.

labels//...ProgramFeaturesList.pb

A ProgramFeaturesList protocol buffer containing a list of features resulting from running a data flow analysis on a program.

graphs/

Directory containing ProGraML representations of LLVM IRs.

graphs/...ProgramGraph.pb

A ProgramGraph protocol buffer of an LLVM IR in the ProGraML representation.

ll/

Directory containing LLVM-IR files.

ir/...ll

An LLVM IR in text format, as produced by clang -emit-llvm -S or equivalent.

test/

A directory containing symlinks to graphs in the graphs/ directory, indicating which graphs should be used as part of the test set.

train/

A directory containing symlinks to graphs in the graphs/ directory, indicating which graphs should be used as part of the training set.

val/

A directory containing symlinks to graphs in the graphs/ directory, indicating which graphs should be used as part of the validation set.

vocal/

Directory containing vocabulary files.

vocab/.csv

A vocabulary file, which lists unique node texts, their frequency in the dataset, and the cumulative proportion of total unique node texts that is covered.

For further information please see our ProGraML repository.

A data set containing 7 Java projects for evaluating performance of Java compilers. Included are OracleJDK version 8.0.351, 9.0.4, 10.0.2 and 11.0.17 as well as ExtendJ version 8, 9, 10 and 11. ExtendJ is an open-source Java compiler, more information can be found here: https://extendj.org/

The included scripts measure the memory use and compilation times for these compilers when compiling the projects. Creating this data set was part of a master's thesis at Lund University.

Code and data for paper 'Silent Compiler Bug De-duplication via Three-Dimensional Analysis'

A BitTorrent file to download data with the title '[Coursera] Compilers (Stanford University) (compilers)'

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The DHS Program STATcompiler allows users to make custom tables based on hundreds of demographic and health indicators across more than 70 countries.

Emscripten is a special open source compiler that compiles C and C++ code into JavaScript. By utilizing this compiler, some typical C/C++ chemoinformatics toolkits and libraries are quickly ported to to web. The compiled JavaScript files have sizes similar to native programs, and from a series of constructed benchmarks, the performance of the compiled JavaScript codes is also close to that of the native codes and is better than the handwritten JavaScript codes. Therefore, we believe that Emscripten is a feasible and practical tool for reusing existing C/C++ codes on the web, and many other chemoinformatics or molecular calculation software tools can also be easily ported by Emscripten.

Abstract: This data set contains the source code for the compiler-implemented differential checksums as described in the following publication: Christoph Borchert, Horst Schirmeier, and Olaf Spinczyk. Compiler-Implemented Differential Checksums: Effective Detection and Correction of Transient and Permanent Memory Errors. In Proceedings of the 53rd IEEE/IFIP International Conference on Dependable Systems and Networks (DSN '23). Piscataway, NJ, USA, June 2023. IEEE Press.

Identifying equivalent mutants remains the largest impediment to the widespread uptake of mutation testing. Despite being researched for more than three decades, the problem remains. We propose Trivial Compiler Equivalence (TCE) a technique that exploits the use of readily available compiler technology to address this long-standing challenge. TCE is directly applicable to real-world programs and can imbue existing tools with the ability to detect equivalent mutants and a special form of useless mutants called duplicated mutants. We present a thorough empirical study using 6 large open source programs, several orders of magnitude larger than those used in previous work, and 18 benchmark programs with hand-analysis equivalent mutants. Our results reveal that, on large real-world programs, TCE can discard more than 7% and 21% of all the mutants as being equivalent and duplicated mutants respectively. A human-based equivalence verification reveals that TCE has the ability to detect approximately 30% of all the existing equivalent mutants.

This dataset is about books. It has 1 row and is filtered where the book is Observational equivalence and compiler correctness. It features 7 columns including author, publication date, language, and book publisher.

The proportion of youth and adults with information and communications technology (ICT) skills, by type of skill as defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. The lack of ICT skills continues to be one of the key barriers keeping people from fully benefitting from the potential of ICT. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society. The data compiler for this indicator is the International Telecommunication Union (ITU). Eurostat collects data annually for 32 European countries, while the ITU is responsible for setting up the standards and collecting this information from the remaining countries.

This spreadsheet contains a list of component raster data layers that were used to compile our resistance surface, the classes of data represented within each of these rasters, and the resistance value we assigned to each class. It also provides a web reference for each data layer to provide additional context and information about the source datasets. Please refer to the embedded spatial metadata and the information in our full report for details on the development of the resulting ResistanceSurface, as well as these component data layers: ResistanceData_Roads ResistanceData_ForestedCover ResistanceData_Rivers ResistanceData_Waterbodies ResistanceData_NonForestedCover ResistanceData_BaysEstuaries ResistancePostProcessing_Serpentine