# Machine Learning Enabled Scalable Performance Prediction of Scientific Codes

arXiv: https://arxiv.org/abs/2010.04212 · HTML: https://mlsanity.com/paper/2010.04212/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Regression Language Models for Code](https://mlsanity.com/paper/2509.26476/) (2025)
- [BB-ML: Basic Block Performance Prediction using Machine Learning Techniques](https://mlsanity.com/paper/2202.07798/) (2022)

## Abstract (arXiv)

We present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input, predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks, finally present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of scientific application (radiation transport), SNAP. We analyze the application of multi-variate regression models that accurately predict the reuse profiles and the basic block counts. The predicted runtimes of SNAP when compared to that of actual times are accurate.