# A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels

arXiv: https://arxiv.org/abs/2001.07104 · HTML: https://mlsanity.com/paper/2001.07104/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2020.

## Built on by (most cited first, 7 of 7)

- [Tao: Re-Thinking DL-based Microarchitecture Simulation](https://mlsanity.com/paper/2404.10921/) (2024)
- [Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach](https://mlsanity.com/paper/2411.16954/) (2024)
- [Maya: Optimizing Deep Learning Training Workloads using GPU Runtime Emulation](https://mlsanity.com/paper/2503.20191/) (2025)
- [FIKIT: Priority-Based Real-time GPU Multi-tasking Scheduling with Kernel Identification](https://mlsanity.com/paper/2311.10359/) (2023)
- [LLMPerf: GPU Performance Modeling meets Large Language Models](https://mlsanity.com/paper/2503.11244/) (2025)
- [GCL-Sampler: Discovering Kernel Similarity for Sampled GPU Simulation via Graph Contrastive Learning](https://mlsanity.com/paper/2603.00551/) (2026)
- [A Tabular Schedule Abstraction for Communication-Aware Evaluation of Pipeline-Parallel LLM Training](https://mlsanity.com/paper/2605.24006/) (2026)

## Abstract (arXiv)

Characterizing compute kernel execution behavior on GPUs for efficient task scheduling is a non-trivial task. We address this with a simple model enabling portable and fast predictions among different GPUs using only hardware-independent features. This model is built based on random forests using 189 individual compute kernels from benchmarks such as Parboil, Rodinia, Polybench-GPU and SHOC. Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 8.86-52.00% and 1.84-2.94%, for time respectively power prediction across five different GPUs, while latency for a single prediction varies between 15 and 108 milliseconds.