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Seer: Predictive Runtime Kernel Selection for Irregular Problems

Core Concepts
Seer proposes a machine learning-based predictor, enabling runtime kernel selection for irregular workloads with significant performance improvements.
The content discusses the challenges GPUs face with irregular data processing and introduces Seer, a predictive runtime framework. It covers the design, training, and inference process of Seer using Sparse Matrix Vector Multiplication as a case study. The methodology, model accuracy, and performance evaluation for single and multiple iterations are detailed. Key insights include the importance of feature collection cost consideration and the effectiveness of the classifier selection model in predicting kernel performance. I. Introduction GPUs designed for regular problems struggle with load imbalance in irregular data processing. Seer offers a decision tree selector model for runtime kernel selection in irregular workloads. A case study on Sparse Matrix Vector Multiplication (SpMV) showcases Seer's effectiveness. II. Background and Related Works Previous works highlight the importance of load balancing techniques and compressed sparse formats. Comparison of various load balancing strategies and their impact on kernel performance is discussed. III. Abstraction and Framework Seer's two-level abstraction focuses on training models based on known and dynamically computed features. Decision tree classifiers are used to predict the fastest kernel based on input features at runtime. IV. Case Study Evaluation of Seer using SpMV demonstrates 2× better performance over individual kernels. Analysis includes single iteration and multiple iteration scenarios to assess predictor accuracy. V. Conclusion Seer provides an efficient solution for selecting optimal kernels at runtime in irregular workloads. Future research directions include exploring additional feature collection strategies and expanding application areas.
Seer predicts the best strategy for SpMV with a 2× improvement over individual kernels across datasets.
"Many libraries implement load balancing techniques without considering other potentially better strategies." "Seer's decision tree model provides an explainable approach to kernel selection."

Key Insights Distilled From

by Ryan Swann,M... at 03-27-2024

Deeper Inquiries

How can Seer's framework be extended to other irregular problem domains?

Seer's framework can be extended to other irregular problem domains by following a similar approach of training models on representative datasets and dynamically computed features. The key lies in identifying the relevant characteristics or statistics that are indicative of performance differences among different implementations for a given problem domain. By collecting these features and training decision tree classifiers, Seer can predict the best kernel or strategy for mapping irregular data to hardware at runtime. This methodology is adaptable across various domains as long as there are identifiable metrics that influence performance.

What are potential drawbacks or limitations of relying on machine learning predictors like Seer?

While machine learning predictors like Seer offer significant advantages in automating kernel selection for irregular problems, they also come with certain drawbacks and limitations: Overhead: Collecting dynamically computed features may introduce additional overhead, especially if the feature collection process is complex or time-consuming. Generalization: Machine learning models may not always generalize well to new datasets or scenarios outside their training scope, leading to inaccurate predictions. Interpretability: Complex machine learning models might lack interpretability, making it challenging to understand why a specific prediction was made. Training Data Bias: Biases present in the training dataset could lead to biased predictions in real-world applications. Scalability: Scaling up machine learning predictors like Seer to handle large datasets or complex problems may pose challenges in terms of computational resources and model complexity.

How might feature collection costs impact real-world applications beyond GPU computing?

Feature collection costs can have significant implications for real-world applications beyond GPU computing: Resource Utilization: In resource-constrained environments such as edge devices or IoT systems, high feature collection costs could strain limited resources. Latency: Feature collection overheads could increase latency in time-sensitive applications where quick decisions are crucial. Energy Consumption: Intensive feature extraction processes may consume more energy, impacting battery-powered devices' longevity. Cost-Effectiveness: Balancing the benefits gained from accurate predictions against the cost of feature collection is essential for ensuring cost-effective solutions. 5Privacy Concerns: Depending on the nature of collected features (e.g., sensitive data), there could be privacy concerns regarding information exposure during feature extraction processes. In conclusion, understanding and managing feature collection costs are vital considerations when deploying machine learning predictors like Seer in diverse real-world applications beyond GPU computing contexts."