Optimizing OpenMC Performance and Energy Efficiency through Asynchronous Autotuning

Concepts de base

Integrating the ytopt autotuning framework with libEnsemble to accelerate the autotuning process and improve the accuracy of the surrogate model, enabling efficient exploration of the large parameter space of the ECP application OpenMC to optimize its performance, energy, and energy-delay product.

Résumé

The paper presents a new autotuning framework, ytopt-libe, that integrates the ytopt autotuning framework with the libEnsemble toolkit. ytopt-libe leverages the asynchronous and dynamic task management capabilities of libEnsemble to parallelize the evaluation of multiple parameter configurations simultaneously, accelerating the overall autotuning process. It also improves the accuracy of the random forest surrogate model used by ytopt by feeding more data points, leading to more efficient exploration of the large parameter space.
The authors apply the ytopt-libe framework to autotune the ECP application OpenMC, a community-developed Monte Carlo neutral transport code, on the OLCF Crusher system. OpenMC has a large parameter space with 7 tunable parameters, including the maximum number of particles in-flight, number of MPI ranks per GPU, and various queue management strategies.
The experimental results show that ytopt-libe can achieve up to 29.49% improvement in the figure of merit (particles/s) and up to 30.44% improvement in energy-delay product (EDP) compared to the default configuration of OpenMC. The authors also analyze the performance scaling of OpenMC as the number of GPUs is increased from 8 to 128, demonstrating the effectiveness of the autotuning framework in identifying optimal configurations across different hardware scales.
Furthermore, the paper explores the tradeoffs between application runtime, energy consumption, and EDP, showcasing the ability of the ytopt-libe framework to optimize OpenMC for these diverse performance metrics.

Stats

The maximum number of particles in-flight is in the range of 100,000 to 8 million, with the default of 1 million.
The number of MPI ranks per GPU can be 1 to 4.
The number of logarithmic hash grid bins can be 100 to 100,000, with the default of 4,000.
The minimum sorting threshold (for queued mode only) can be 0 (always sort) to infinity (never sort), with the default of 20,000.

Citations

"The experimental results show that we achieve improvement up to 29.49% in FoM and up to 30.44% in EDP."
"By using ytopt to identify the best configuration, we achieved up to 91.59% performance improvement, up to 21.2% energy savings, and up to 37.84% EDP improvement on up to 4,096 nodes."

Idées clés tirées de

Integrating ytopt and libEnsemble to Autotune OpenMC

by Xingfu Wu, J... à arxiv.org 09-18-2024

https://arxiv.org/pdf/2402.09222.pdf

Questions plus approfondies

How can the ytopt-libe framework be extended to handle other types of applications beyond OpenMC, such as those with different parallelization strategies or hardware requirements?

The ytopt-libe framework can be extended to accommodate a broader range of applications by implementing several key strategies. First, the framework's modular design allows for the integration of different simulation functions tailored to various applications. By defining new code molds and parameter spaces specific to other applications, users can leverage the existing Bayesian optimization and surrogate modeling capabilities of ytopt while adapting the evaluation process to fit the unique characteristics of each application.
Second, to support diverse parallelization strategies, the framework can incorporate additional generator and allocator functions that are optimized for different parallel computing paradigms, such as task-based parallelism or data parallelism. This flexibility would enable ytopt-libe to efficiently manage workloads across various architectures, including those utilizing MPI, OpenMP, or hybrid models.
Moreover, the framework can be enhanced to include support for heterogeneous hardware environments by integrating hardware-specific tuning parameters. This would involve characterizing the performance of different hardware accelerators (e.g., GPUs, TPUs, FPGAs) and their interaction with the application. By utilizing performance profiling tools and incorporating feedback mechanisms, ytopt-libe can dynamically adjust configurations based on the specific capabilities and limitations of the underlying hardware, ensuring optimal performance across diverse systems.

What are the potential limitations or challenges in applying the ytopt-libe framework to autotune applications on heterogeneous systems with diverse hardware accelerators?

Applying the ytopt-libe framework to autotune applications on heterogeneous systems presents several challenges. One significant limitation is the complexity of managing diverse hardware architectures, each with its own performance characteristics and tuning requirements. For instance, different accelerators may have varying memory bandwidth, compute capabilities, and communication latencies, which can complicate the parameter space definition and the evaluation process.
Another challenge is the potential for increased overhead due to the need for extensive profiling and benchmarking across multiple hardware platforms. This overhead can lead to longer initialization times and may negate some of the performance benefits gained from autotuning. Additionally, the asynchronous nature of the ytopt-libe framework may introduce synchronization issues when coordinating evaluations across heterogeneous resources, particularly if the performance metrics vary significantly between different hardware components.
Furthermore, the scalability of the ytopt-libe framework may be hindered by the limited availability of certain hardware resources. For example, if an application is designed to run on a specific type of GPU, the autotuning process may be constrained by the number of available GPUs, leading to suboptimal configurations being selected due to insufficient data points for training the surrogate model.

How can the autotuning process be further optimized to reduce the overall time and computational resources required, especially for applications with extremely large parameter spaces?

To optimize the autotuning process and reduce the time and computational resources required, especially for applications with large parameter spaces, several strategies can be employed. First, implementing a more sophisticated surrogate modeling approach, such as Gaussian processes or deep learning-based models, can enhance the accuracy of predictions while requiring fewer evaluations. These advanced models can better capture complex relationships within the parameter space, allowing for more efficient exploration and exploitation.
Second, adaptive sampling techniques can be introduced to prioritize evaluations in regions of the parameter space that are more likely to yield high-performance configurations. By dynamically adjusting the sampling strategy based on previous evaluations, the framework can focus computational resources on the most promising areas, thereby reducing the number of evaluations needed to identify optimal configurations.
Additionally, incorporating multi-fidelity optimization techniques can further streamline the autotuning process. By leveraging lower-fidelity models or approximations to quickly evaluate configurations before committing to more expensive high-fidelity evaluations, the framework can significantly cut down on computational costs while still converging on effective solutions.
Lastly, parallelizing the evaluation process more aggressively by utilizing a larger number of workers and optimizing the task allocation strategy can help maximize resource utilization. This approach not only speeds up the evaluation of configurations but also allows for a more comprehensive exploration of the parameter space within a given time frame, ultimately leading to better performance outcomes.