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ROOT is a large code base with a complex set of build-time dependencies; there is a significant difference in compilation time between the core of ROOT and the full-fledged deployment. We present results on a delayed build for internal ROOT packages and external packages. This gives the ability to offer a lightweight core of ROOT, later extended by building additional modules to extend the functionality of ROOT. As a part of this work, we have improved the separation of ROOT code into distinct modules and packages with minimal dependencies. This approach gives users better flexibility and the possibility to combine various build features without rebuilding from scratch. Dependency hell is a common problem found in software and particularly in HEP software ecosystem. We would like to discuss an improvement of artifact management (lazy-install) system as a solution to the dependency hell problem. HEP software stack usually consists of multiple sub-projects with dependencies. The development model is often distributed, independent and non-coherent among the sub-projects. We believe that software should be designed to take advantage of other software components that are already available, or have already been designed and implemented for use elsewhere rather than reinventing the wheel. In our contribution, we will present our approach to artifact management system of ROOT together with a set of examples and use cases.
With the ever-increasing adoption of machine learning for data analytics, maintaining a machine learning pipeline is becoming more complex as both the datasets and trained models evolve with time. In a collaborative environment, the changes and updates due to pipeline evolution often cause cumbersome coordination and maintenance work, raising the costs and making it hard to use. Existing solutions, unfortunately, do not address the version evolution problem, especially in a collaborative environment where non-linear version control semantics are necessary to isolate operations made by different user roles. The lack of version control semantics also incurs unnecessary storage consumption and lowers efficiency due to data duplication and repeated data pre-processing, which are avoidable. In this paper, we identify two main challenges that arise during the deployment of machine learning pipelines, and address them with the design of versioning for an end-to-end analytics system MLCask. The system supports multiple user roles with the ability to perform Git-like branching and merging operations in the context of the machine learning pipelines. We define and accelerate the metric-driven merge operation by pruning the pipeline search tree using reusable history records and pipeline compatibility information. Further, we design and implement the prioritized pipeline search, which gives preference to the pipelines that probably yield better performance. The effectiveness of MLCask is evaluated through an extensive study over several real-world deployment cases. The performance evaluation shows that the proposed merge operation is up to 7.8x faster and saves up to 11.9x storage space than the baseline method that does not utilize history records.
The ROOT software framework is foundational for the HEP ecosystem, providing capabilities such as IO, a C++ interpreter, GUI, and math libraries. It uses object-oriented concepts and build-time components to layer between them. We believe additional layering formalisms will benefit ROOT and its users. We present the modularization strategy for ROOT which aims to formalize the description of existing source components, making available the dependencies and other metadata externally from the build system, and allow post-install additions of functionality in the runtime environment. components can then be grouped into packages, installable from external repositories to deliver post-install step of missing packages. This provides a mechanism for the wider software ecosystem to interact with a minimalistic install. Reducing intra-component dependencies improves maintainability and code hygiene. We believe helping maintain the smallest base install possible will help embedding use cases. The modularization effort draws inspiration from the Java, Python, and Swift ecosystems. Keeping aligned with the modern C++, this strategy relies on forthcoming features such as C++ modules. We hope formalizing the component layer will provide simpler ROOT installs, improve extensibility, and decrease the complexity of embedding in other ecosystems
Foundational software libraries such as ROOT are under intense pressure to avoid software regression, including performance regressions. Continuous performance benchmarking, as a part of continuous integration and other code quality testing, is an industry best-practice to understand how the performance of a software product evolves over time. We present a framework, built from industry best practices and tools, to help to understand ROOT code performance and monitor the efficiency of the code for a several processor architectures. It additionally allows historical performance measurements for ROOT I/O, vectorization and parallelization sub-systems.
Inter-package conflicts require the presence of two or more packages in a particular configuration, and thus tend to be harder to detect and localize than conventional (intra-package) defects. Hundreds of such inter-package conflicts go undetected by the normal testing and distribution process until they are later reported by a user. The reason for this is that current meta-data is not fine-grained and accurate enough to cover all common types of conflicts. A case study of inter-package conflicts in Debian has shown that with more detailed package meta-data, at least one third of all package conflicts could be prevented relatively easily, while another one third could be found by targeted testing of packages that share common resources or characteristics. This paper reports the case study and proposes ideas to detect inter-package conflicts in the future.
In this research, we provide a comprehensive empirical summary of the Python Package Repository, PyPI, including both package metadata and source code covering 178,592 packages, 1,745,744 releases, 76,997 contributors, and 156,816,750 import statements. We provide counts and trends for packages, releases, dependencies, category classifications, licenses, and package imports, as well as authors, maintainers, and organizations. As one of the largest and oldest software repositories as of publication, PyPI provides insight not just into the Python ecosystem today, but also trends in software development and licensing more broadly over time. Within PyPI, we find that the growth of the repository has been robust under all measures, with a compound annual growth rate of 47% for active packages, 39% for new authors, and 61% for new import statements over the last 15 years. As with many similar social systems, we find a number of highly right-skewed distributions, including the distribution of releases per package, packages and releases per author, imports per package, and size per package and release. However, we also find that most packages are contributed by single individuals, not multiple individuals or organizations. The data, methods, and calculations herein provide an anchor for public discourse on PyPI and serve as a foundation for future research on the Python software ecosystem.