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Introduction

E4S includes a variety of build tools that simplify the process of configuring, compiling, and installing scientific software across diverse high-performance computing (HPC) environments. Build tools are a critical part of the software lifecycle, ensuring reproducible builds, compatibility with system architectures, and integration with package managers such as Spack.

Selecting the right build tool depends on factors such as portability needs, dependency complexity, supported platforms, and project maturity. Some tools focus on package-level builds (like CMake or Autotools), while others manage complex dependency graphs (like Spack or EasyBuild). E4S supports and tests a wide range of build tools across architectures including x86, ARM, POWER, and GPUs from NVIDIA, AMD, and Intel.

Example: I am building a scientific simulation code that will run on both local workstations and leadership-class HPC systems using AMD and NVIDIA GPUs.
I need reproducible builds that integrate with Spack and support cross-compilation for GPU targets.
Ease of use and automated dependency management are important, but I also need fine control over compiler flags for performance tuning.
My code uses CMake for configuration, but I want to explore whether another tool might simplify dependency management and multi-architecture builds.
Please recommend a build tool or toolchain configuration based on these attributes:

  • Portability: High
  • Dependency Management: Automated
  • Integration with Package Managers: Required
  • Reproducibility: High
  • Hardware Tuning: Required for GPU optimization
  • Parallel Build Support: Yes
  • Debugging Support: Helpful for research prototyping

The tables below provide attributes that can help a newcomer or an automated assistant select an appropriate build tool for a given environment or development scenario.

Broadly Meaningful Attributes

Attribute Description
Portability The ability of the build tool to operate across multiple architectures and operating systems.
Ease of Use How straightforward it is to configure, build, and install software with minimal manual intervention.
Dependency Management How well the tool manages software dependencies and ensures version compatibility.
Integration with Package Managers The degree to which the build tool integrates with higher-level systems such as Spack or system package managers.
Reproducibility The ability to recreate the same build environment and outputs consistently.
Build Customization The flexibility in specifying build options, compiler flags, and target architectures.
Parallel Build Support The extent to which the tool supports multi-threaded or distributed builds.
Documentation and Community Availability of documentation, tutorials, and an active community or ecosystem.
Licensing and Openness Whether the tool is open source and compliant with E4S’s open-science principles.
Interoperability Compatibility with multiple compilers (GNU, LLVM, Intel, Cray, etc.) and runtime environments.

Situation-Specific Attributes

For Portable Software Development

Attribute Description
Cross-Compilation Support Ability to build for different target architectures from a single development environment.
Toolchain Integration Compatibility with major compiler toolchains (e.g., GCC, LLVM, oneAPI, ROCm).
Environment Modules Support Ease of integration with environment modules used on HPC systems.
Platform Autodetection Ability to detect available hardware and adjust build configuration automatically.

For Multi-Package or Ecosystem Builds

Attribute Description
Dependency Graph Handling Ability to manage complex dependency trees automatically.
Version Control Integration Support for Git, GitHub, or GitLab workflows in building and testing multiple packages.
Continuous Integration Compatibility Integration with CI systems such as GitHub Actions, GitLab CI, or Frank CI.
Binary Cache Management Ability to store and reuse build artifacts to reduce rebuild times.

For Performance Optimization and Hardware-Specific Builds

Attribute Description
Hardware Tuning Support for architecture-specific optimizations (e.g., vectorization, GPU acceleration).
Compiler-Specific Optimization Ability to leverage vendor compilers for improved performance.
Benchmark Integration Capability to build with or for performance benchmarking frameworks (e.g., HPCG, ReFrame).
SDK Awareness Integration with vendor SDKs (e.g., CUDA, ROCm, oneAPI).

For Development and Research Prototyping

Attribute Description
Incremental Build Support Ability to rebuild only changed components during development.
Debugging Support Integration with debuggers and profilers.
Build Logging and Diagnostics Quality of output logs and error reporting to aid troubleshooting.
Scriptability Ability to automate builds via Python or shell scripting interfaces.

For Large-Scale or Production Deployment

Attribute Description
Scalability Ability to handle large-scale builds on distributed systems.
Security and Verification Availability of checksums, cryptographic signing, or provenance metadata.
Policy Compliance Support for site-specific build policies and reproducibility requirements.
Lifecycle Management Tools or hooks for maintaining, upgrading, and deprecating software builds.