Predicting Optimal Tuning Parameters for GPU Compute Kernels using Deep Sequence-to-Sequence Models

Główne pojęcia

A sequence-to-sequence deep learning model can accurately predict the optimal tuning parameters for GPU compute kernels by translating the input tensor descriptors to the corresponding kernel parameter configurations.

Streszczenie

The paper proposes a methodology that uses deep sequence-to-sequence models to predict the optimal tuning parameters for GPU compute kernels. The authors consider the prediction of kernel parameters as a sequence-to-sequence translation problem, borrowing models from the Natural Language Processing (NLP) domain. Parameters describing the input, output, and weight tensors are used as the input language to the model, which then emits the corresponding kernel parameters.
The key contributions of this work are:

Demonstrating that a sequence-to-sequence model can accurately learn the performance dynamics of a GPU compute kernel.
Proposing a novel network architecture that predicts the kernel tuning parameters for GPU kernels.
Incorporating a constrained beam search that incorporates the physical limits of the GPU hardware and other expert knowledge to reduce the search space.

The proposed algorithm can achieve over 90% accuracy on various convolutional kernels in MIOpen, the AMD machine learning primitives library. This technique can reduce the development time and compute resources required to tune unseen input configurations, resulting in shorter development cycles, reduced development costs, and better user experience.

Statystyki

The total number of possible kernel parameters is vast, making it a formidable task to predict these parameters accurately.
The selected kernels have a wide range of output parameters, with the total number of possible parameter combinations being extremely large.

Cytaty

"The core contributions of this work are: a) Proposing that a sequence to sequence model can accurately learn the performance dynamics of a GPU compute kernel b) A novel network architecture which predicts the kernel tuning parameters for GPU kernels, c) A constrained beam search which incorporates the physical limits of the GPU hardware as well as other expert knowledge reducing the search space."

Kluczowe wnioski z

Optimal Kernel Tuning Parameter Prediction using Deep Sequence Models

by Khawir Mahmo... o arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10162.pdf

Głębsze pytania

How can the proposed sequence-to-sequence model be extended to handle unseen kernel configurations or hardware architectures

To extend the proposed sequence-to-sequence model to handle unseen kernel configurations or hardware architectures, several strategies can be implemented. One approach is to incorporate transfer learning techniques, where the model is pre-trained on a large dataset of diverse kernel configurations and hardware architectures. By leveraging the knowledge gained from this pre-training phase, the model can adapt more effectively to new, unseen configurations during the fine-tuning process. Additionally, implementing a mechanism for continual learning can enable the model to incrementally update its knowledge as it encounters new data, ensuring adaptability to evolving hardware architectures and kernel configurations. Furthermore, integrating a mechanism for self-supervised learning can allow the model to learn from unlabeled data, further enhancing its ability to generalize to unseen scenarios. By combining these approaches, the sequence-to-sequence model can be extended to handle a wide range of kernel configurations and hardware architectures with improved accuracy and efficiency.

What are the potential drawbacks or limitations of using a constrained beam search approach, and how can they be addressed

While constrained beam search offers benefits in terms of improving prediction accuracy and reducing the search space, there are potential drawbacks and limitations that need to be considered. One limitation is the computational overhead associated with beam search, especially when using a large beam width. This can lead to increased inference time and resource consumption, impacting the model's efficiency. To address this, techniques such as beam pruning can be implemented to selectively retain the most promising candidate sequences, reducing the computational burden while maintaining prediction quality. Another drawback is the potential for the model to get stuck in local optima, especially when the search space is constrained. To mitigate this, incorporating mechanisms for exploration and diversification within the beam search algorithm can help the model escape local optima and discover more optimal solutions. By carefully balancing the trade-offs between computational complexity and exploration-exploitation dynamics, the limitations of constrained beam search can be effectively managed.

What other types of deep learning architectures or techniques could be explored to further improve the accuracy and efficiency of kernel parameter prediction

In addition to the proposed sequence-to-sequence model, exploring other deep learning architectures and techniques can further enhance the accuracy and efficiency of kernel parameter prediction. One promising approach is the use of transformer-based models, such as the Transformer architecture or its variants like BERT (Bidirectional Encoder Representations from Transformers). Transformers excel in capturing long-range dependencies and have shown significant success in various natural language processing tasks. By adapting transformer-based models to the task of kernel parameter prediction, the model can potentially learn more complex patterns and relationships within the data, leading to improved performance. Additionally, techniques like reinforcement learning can be explored to optimize the model's decision-making process during parameter prediction. By formulating the prediction task as a reinforcement learning problem and designing appropriate reward mechanisms, the model can learn to make more informed and strategic decisions, further enhancing its predictive capabilities. By integrating these advanced deep learning architectures and techniques, the accuracy and efficiency of kernel parameter prediction can be significantly improved.