# Application Performance Modeling via Tensor Completion

arXiv: https://arxiv.org/abs/2210.10184 · HTML: https://mlsanity.com/paper/2210.10184/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 76 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks](https://mlsanity.com/paper/1808.07412/) (2018)
- [Generalized Canonical Polyadic Tensor Decomposition](https://mlsanity.com/paper/1808.07452/) (2018)
- [A Deep Learning Based Cost Model for Automatic Code Optimization](https://mlsanity.com/paper/2104.04955/) (2021)
- [A better measure of relative prediction accuracy for model selection and model estimation](https://mlsanity.com/paper/2105.05249/) (2021)
- [Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors](https://mlsanity.com/paper/1404.4667/) (2014)
- [Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization](https://mlsanity.com/paper/2010.08040/) (2020)
- [GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation](https://mlsanity.com/paper/2210.03894/) (2022)
- [Non-smooth Bayesian Optimization in Tuning Problems](https://mlsanity.com/paper/2109.07563/) (2021)
- [Input-Aware Auto-Tuning of Compute-Bound HPC Kernels](https://mlsanity.com/paper/1802.05371/) (2018)
- [Distributed-Memory Tensor Completion for Generalized Loss Functions in Python using New Sparse Tensor Kernels](https://mlsanity.com/paper/1910.02371/) (2019)
- [Distributed Methods for High-dimensional and Large-scale Tensor Factorization](https://mlsanity.com/paper/1410.5209/) (2014)
- [Extracting Clean Performance Models from Tainted Programs](https://mlsanity.com/paper/2012.15592/) (2020)
- [Learning with Analytical Models](https://mlsanity.com/paper/1810.11772/) (2018)

## Built on by (most cited first, 3 of 3)

- [Learning Generalizable Program and Architecture Representations for Performance Modeling](https://mlsanity.com/paper/2310.16792/) (2023)
- [Data Understanding Survey: Pursuing Improved Dataset Characterization Via Tensor-based Methods](https://mlsanity.com/paper/2510.14161/) (2025)
- [Efficient Tensor Completion Algorithms for Highly Oscillatory Operators](https://mlsanity.com/paper/2510.17734/) (2025)

## Abstract (arXiv)

Performance tuning, software/hardware co-design, and job scheduling are among the many tasks that rely on models to predict application performance. We propose and evaluate low-rank tensor decomposition for modeling application performance. We discretize the input and configuration domains of an application using regular grids. Application execution times mapped within grid-cells are averaged and represented by tensor elements. We show that low-rank canonical-polyadic (CP) tensor decomposition is effective in approximating these tensors. We further show that this decomposition enables accurate extrapolation of unobserved regions of an application's parameter space. We then employ tensor completion to optimize a CP decomposition given a sparse set of observed execution times. We consider alternative piecewise/grid-based models and supervised learning models for six applications and demonstrate that CP decomposition optimized using tensor completion offers higher prediction accuracy and memory-efficiency for high-dimensional performance modeling.