MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations

• URL: http://arxiv.org/abs/2407.02238v1
• Date: Tue, 2 Jul 2024 13:00:19 GMT
• Title: MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations
• Authors: Akash Dutta, Ali Jannesari,
• Abstract summary: In this paper, we propose MIREncoder, a Multi-modal IR-based Auto-Encoder that can be pre-trained to generate a learned embedding space. A multi-modal approach enables us to better extract features from compilable programs. Our evaluations will show that our proposed approach can outperform the state of the art while reducing overhead.
• Score: 6.919817502555546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the primary areas of interest in High Performance Computing is the improvement of performance of parallel workloads. Nowadays, compilable source code-based optimization tasks that employ deep learning often exploit LLVM Intermediate Representations (IRs) for extracting features from source code. Most such works target specific tasks, or are designed with a pre-defined set of heuristics. So far, pre-trained models are rare in this domain, but the possibilities have been widely discussed. Especially approaches mimicking large-language models (LLMs) have been proposed. But these have prohibitively large training costs. In this paper, we propose MIREncoder, a M}ulti-modal IR-based Auto-Encoder that can be pre-trained to generate a learned embedding space to be used for downstream tasks by machine learning-based approaches. A multi-modal approach enables us to better extract features from compilable programs. It allows us to better model code syntax, semantics and structure. For code-based performance optimizations, these features are very important while making optimization decisions. A pre-trained model/embedding implicitly enables the usage of transfer learning, and helps move away from task-specific trained models. Additionally, a pre-trained model used for downstream performance optimization should itself have reduced overhead, and be easily usable. These considerations have led us to propose a modeling approach that i) understands code semantics and structure, ii) enables use of transfer learning, and iii) is small and simple enough to be easily re-purposed or reused even with low resource availability. Our evaluations will show that our proposed approach can outperform the state of the art while reducing overhead.

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