Evaluating Performance Overheads of HPX/Kokkos Applications in Singularity Containers on Supercomputer Fugaku and a Hybrid CPU-GPU Cluster

Core Concepts

Containerization can improve reproducibility of HPC applications, but may introduce performance overheads. This study evaluates the performance impact of running the HPX/Kokkos-based astrophysics application Octo-Tiger in Singularity containers on a homogeneous CPU-based supercomputer (Fugaku) and a heterogeneous CPU-GPU cluster (DeepBayou).

Abstract

The paper investigates the use of containers, specifically Singularity, for running the HPX/Kokkos-based astrophysics application Octo-Tiger on HPC resources. It covers the following key points: Motivation for using containers in HPC: Improved reproducibility by packaging the application and dependencies Easier deployment on different HPC systems Software stack for Octo-Tiger: Octo-Tiger is built on top of the HPX asynchronous many-task runtime system and uses the Kokkos performance-portable programming model. It also relies on several other dependencies like HDF5, Boost, CUDA, etc. Workflow for building and running Octo-Tiger in containers: The authors use Spack to manage the dependencies and generate a Docker file. They then convert the Docker image to a Singularity container, which can be executed on HPC clusters without requiring root access. Challenges encountered include handling vendor-specific compilers and libraries on the Fugaku supercomputer. Performance evaluation: On the Fugaku supercomputer (homogeneous CPU-based), the regular (non-containerized) runs were about 50 seconds faster on average compared to the Singularity container runs. On the DeepBayou hybrid CPU-GPU cluster, the CPU-only runs showed comparable performance between the regular and containerized runs. However, the distributed GPU runs in the container crashed with CUDA errors. The paper concludes that containers offer benefits for reproducibility and portability, but building the containers can be more challenging than a native installation. The performance impact varies depending on the target HPC system, with the homogeneous CPU-based Fugaku showing more overhead compared to the hybrid CPU-GPU DeepBayou cluster.

Stats

The paper provides the following performance statistics: On Supercomputer Fugaku: Minimum time for regular runs: 214.1 seconds Median time for regular runs: 215.2 seconds Average time for regular runs: 221.1 seconds Maximum time for regular runs: 237.6 seconds Standard deviation for regular runs: 9.8 seconds Minimum time for Singularity runs: 267.5 seconds Median time for Singularity runs: 277.3 seconds Average time for Singularity runs: 277.1 seconds Maximum time for Singularity runs: 286.8 seconds Standard deviation for Singularity runs: 6.2 seconds

Quotes

None.

Key Insights Distilled From

HPX with Spack and Singularity Containers: Evaluating Overheads for HPX/Kokkos using an astrophysics application

by Patr... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00016.pdf

Deeper Inquiries

How would the performance impact of containerization differ on other HPC systems, especially those with more heterogeneous architectures (e.g., systems with a larger number of GPUs)

The performance impact of containerization on other HPC systems, especially those with more heterogeneous architectures like systems with a larger number of GPUs, can vary based on several factors. Resource Utilization: Systems with more GPUs may see a different performance impact as containers need to efficiently utilize these resources. Optimizing GPU utilization within containers can be crucial for maintaining performance. Communication Overheads: In systems with complex architectures, such as those with multiple GPUs, the communication overhead between different components can be significant. Containerization may introduce additional communication overhead, affecting performance. Hardware-Specific Optimizations: Heterogeneous systems often have specific optimizations tailored to their architecture. Containerization may impact these optimizations differently, leading to varying performance impacts. Data Movement: Systems with more GPUs may require efficient data movement between different components. Containerization strategies should consider optimizing data movement within the container to minimize performance overhead. Parallelism and Concurrency: Systems with a larger number of GPUs may require advanced parallelism and concurrency management. Containerization strategies should ensure that these aspects are well-handled to maintain performance levels. In summary, the performance impact of containerization on HPC systems with more heterogeneous architectures will depend on how well the containerization strategy aligns with the specific characteristics and requirements of the system.

What strategies could be employed to mitigate the performance overhead observed in the Singularity container runs, such as optimizing the container build process or exploring alternative container technologies

To mitigate the performance overhead observed in Singularity container runs, especially on systems like the Fugaku supercomputer, several strategies can be employed: Optimized Container Build Process: Streamlining the container build process by optimizing dependencies, reducing unnecessary layers, and ensuring efficient resource utilization can help improve performance. Compiler and Library Versions: Ensuring that the compiler and library versions used within the container are compatible with the target system can help minimize performance discrepancies. Hardware-Specific Tuning: Tailoring the container build process to leverage hardware-specific optimizations and configurations of the target system can enhance performance. Profiling and Benchmarking: Conducting thorough performance profiling and benchmarking within the containerized environment can help identify bottlenecks and areas for optimization. Alternative Container Technologies: Exploring alternative container technologies that may offer better performance characteristics for specific use cases, such as Podman or Charliecloud, could be beneficial. By implementing these strategies, users can potentially mitigate the performance overhead observed in Singularity container runs and optimize the performance of their applications on diverse HPC platforms.

Given the challenges encountered in building the containers, particularly on the Fugaku supercomputer, what best practices or guidelines could be developed to help HPC users successfully containerize their applications on diverse HPC platforms

To help HPC users successfully containerize their applications on diverse HPC platforms, especially considering the challenges faced on systems like the Fugaku supercomputer, the following best practices and guidelines could be developed: Compatibility Testing: Ensure that containerized applications are thoroughly tested for compatibility with the target HPC platform, including specific hardware configurations and software dependencies. Documentation: Provide detailed documentation on the container build process, including compiler versions, library dependencies, and any platform-specific considerations. Collaboration with System Administrators: Collaborate with system administrators to understand platform-specific requirements and optimize the container build process accordingly. Community Support: Establish a community forum or support system where users can share experiences, troubleshoot issues, and exchange best practices for containerizing applications on diverse HPC platforms. Continuous Optimization: Regularly review and optimize the container build process based on feedback, performance benchmarks, and advancements in containerization technologies. By following these best practices and guidelines, HPC users can navigate the challenges of containerizing applications on diverse platforms more effectively and ensure optimal performance and compatibility.