Cross-platform seismic imaging benchmarking

Optimization and benchmarking of seismic imaging workloads

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We have launched a groundbreaking framework to benchmark seismic imaging workloads across different platforms. This initiative brings together key stakeholders from Devito, hardware vendors, cloud providers, and the broader industry, setting a stage for standardization, reproducibility, and elevated performance in seismic imaging workloads.

Key Highlights:

  1. Standardization and Reproducibility:
    • Advocates for standardized comparisons and robust performance data, aiding organizations in insightful hardware or cloud system acquisitions.
  2. Efficient Resource Utilization:
    • Promotes code/data reuse and minimizes redundant efforts, leading to efficient resource and human capital utilization.
  3. Extendable and Automated Workflow:
    • The flexible architecture allows for extensibility and employs automation for a streamlined benchmarking process, catering to evolving needs.
  4. Community Engagement:
    • The initiative welcomes community engagement and dialogue, laying the groundwork for future collaborations, workshops, and benchmarking expansions.
  5. Transparency and Validation:
    • Even in its alpha phase, the emphasis on transparency and validation of benchmark data ensures responsible use of preliminary data. However, because of the commercial sensitivity of the data, the benchmarking data is only available under NDA.

This framework signifies a substantial stride towards nurturing a collaborative ecosystem aimed at advancing standardization and optimization of seismic imaging workloads across diverse computing architectures. The collaborative ethos facilitated by this platform is geared towards driving notable advancements in seismic imaging performance, contributing to the overarching goal of efficient resource utilization and heightened computational capabilities.


The framework is build on GitHub-based having being heavily influenced by our existing CI/CD framework. The platform includes a development cluster of servers with various computer architectures, configured as GitHub self-hosted runners, and utilizes GitHub Actions to automate workflows.

One of the advantages of this approach is that it naturally supports automation, standardized and reproducible comparisons of different methods, hardware, and skills. It also enables collaboration and code/data reuse, ultimately leading to better performance of the software and efficient use of human capital. The platform is also easily extendable by configuring self-hosted runners, adding different benchmarks to the GitHub Actions workflow, and more servers (either on-prem or Cloud-based).

Benchmarking as a platform

The key idea behind the seismic imaging benchmarking platform is to bring together stakeholders in the industry, such as energy companies, service companies, processor manufacturers, and academic researchers, to standardize benchmarking of seismic imaging kernels.

The objective is to enable accurate and reproducible benchmark experiments, facilitate collaboration and code/data reuse, reduce the duplication of effort and improve the overall performance of seismic imaging software. Additionally, robust performance data will help organizations make informed purchasing decisions for on-premise or cloud computing systems.

Overall, the proposed platform aims to address the common issues in benchmarking seismic imaging kernels, such as differences in the PDEs, discretization, algorithmic optimizations, and runtime choices, and provide a more standardized and reproducible approach for comparing different methods, hardware, and skill.

Anatomy of a standard benchmark

graph TB subgraph Standard: Benchmark setup/input A(Problem specification: PDEs, BCs, grid size/shape, ...) end subgraph Concrete implementation A-->B1(OSS Devito) A-->B2(DevitoPRO) A-->B3(Hardware vendor implementation) A-->B4(Other ISV, research, proprietary implementations) end subgraph Execution environment B2-->C1(Docker container) B2-->C2(Singularity container) end subgraph Target architecture C1-->D1(Bare metal) C2-->D2(Cloud) D1-->E1(CPU) D1-->E2(GPU) D2-->E3(CPU) D2-->E4(GPU) end subgraph Standard: benchmark output F(JSON: performance metrics, solution norms, status, implementation specific metadata) E1-->F E2-->F E3-->F E4-->F end;

The software infrastructure

We have created a GitHub-based extensible framework for benchmarking seismic imaging kernels. The Seismic Benchmark Platform e-infrastructure comprises GitHub Actions for automating workflows and a development cluster of servers with various computer architectures, each configured as a GitHub self-hosted runner.

GitHub Actions is a feature that allows users to automate software development workflows. It allows users to create custom workflows, called actions, triggered by specific events such as a code push, pull request, or the creation of an issue. These workflows include building and testing code, deploying software, and integrating with other tools. With GitHub Actions, users can automate repetitive tasks, reduce manual errors, and improve the overall efficiency of their development process. In our case, GitHub Actions are used to

Workflow of benchmark automation with GitHub Actions

graph TB subgraph GitHub Action: manual event trigger A(Benchmark matrix of jobs: benchmarks x architectures) B(GitHub actions schedules individual jobs to self-hosted runners) A-->B end subgraph Foreach benchmark job C(Job allocated to self-hosted runner) D(Setup execution environment) E(Run benchmark) F(Push benchmark output to data repo) B-->C C-->D D-->E E-->F end subgraph GitHub Action: triggered by data push G(Process data) H(Publish results to gh-pages) F-->G G-->H end;

GitHub Actions can run on either GitHub-hosted runners or self-hosted runners. Self-hosted runners are used to execute a workflow on machines the users have direct access to, rather than on GitHub-managed infrastructure. Self-hosted runners allow users more control over the environment in which their workflows run, including access to specific software, libraries, or hardware resources. Users can also use self-hosted runners to run workflows on-premises, in a virtual private cloud, or in a hybrid environment. Self-hosted runners are a flexible solution for organizations with specific requirements for their development environments and need more control over their workflow execution.

For the work described here, we have configured the following self-hosted runners:

The advantages of this design based on GitHub Actions are

While the vision is to advance standardization in our industry and grow a community around this platform, it is also straightforward to fork our codebase and create a private instance with proprietary benchmarks.

Another fundamental aspect of our software infrastructure is the use of virtual containers, in particular Docker. This makes it straightforward to configure new machines and reproduce performance results. In our experience, virtual containers are the only realistic way of maintaining and extending a software and hardware infrastructure like the one we envision in this project.

Currently, three benchmarks are configured:

Isotropic acoustic

Fletcher and Fowler TTI

Skew-adjoint TTI

Results preview

We have included a snapshot of results below. FLOPS (Floating Point Operations Per Second) is a well-recognized measure in High-Performance Computing (HPC), but it may not always be the most revealing. Its value can be inflated by employing inefficient numerical methods. In seismic imaging, GPts/s (Giga-Points Per Second) — also termed giga-cells-per-second — is often favored. This metric directly measures work throughput, offering a clearer gauge of performance. In essence, GPts/s helps in accurately estimating the time or cost required to solve a specific problem.

Disclaimer: Although we have exerted maximum effort to guarantee precise, equitable, and replicable results, it is crucial to understand that the benchmarking framework is still in the alpha development phase. Consequently, the benchmarks provided here are preliminary and subject to change. The benchmark data should not be considered comprehensive or final, and are not suited for making any financial decisions.

No warranty, express or implied, is provided with the data. The information is supplied on an “as is” basis. We expressly disclaim, to the maximum extent permitted by law, any liability for any damages or losses, direct or consequential, resulting from the use of these benchmarks. Please utilize this information responsibly, keeping in mind its tentative nature.

3D Isotropic acoustic

Processor GPts/s
NVIDIA A100-80GB 62.7
NVIDIA A100-PCIE-40GB 54.2
AMD Instinct™ MI250 54
NVIDIA Tesla PG503-216 (V100) 31
AMD Instinct™ MI210 29.2
Intel(R) Xeon(R) Gold 5218R CPU 7.97
AMD EPYC 7413 24-Core Processor 1.49

3D Fletcher Du Fowler TTI

Processor GPts/s
AMD Instinct™ MI250 16.1
NVIDIA A100-80GB 12.4
NVIDIA A100-PCIE-40GB 12.2
AMD Instinct™ MI210 9.83
NVIDIA Tesla PG503-216 (V100) 9.34
Intel(R) Xeon(R) Gold 5218R CPU 1.72
AMD EPYC 7413 24-Core Processor 0.797

Future work


Many thanks to Chevron for the funding and feedback to kickstart this initiative. We would also like to thank AMD, AWS, Dell, Nvidia and Supermicro for providing hardware and cloud resources.