# Phantora: Maximizing Code Reuse in Simulation-based Machine Learning System Performance Estimation

arXiv: https://arxiv.org/abs/2505.01616 · HTML: https://mlsanity.com/paper/2505.01616/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 52 references that are in the map.
- First posted on arXiv 2 May 2025.

## Builds on (most cited first, 27 of 27)

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- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
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- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
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- [ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale](https://mlsanity.com/paper/2303.14006/) (2023)
- [vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training](https://mlsanity.com/paper/2312.12391/) (2023)
- [Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training](https://mlsanity.com/paper/2006.03318/) (2020)
- [Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces](https://mlsanity.com/paper/2305.14516/) (2023)
- [dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training](https://mlsanity.com/paper/2205.02473/) (2022)
- [DistSim: A performance model of large-scale hybrid distributed DNN training](https://mlsanity.com/paper/2306.08423/) (2023)
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## Abstract (arXiv)

Modern machine learning (ML) training workloads place substantial demands on both computational and communication resources. Consequently, accurate performance estimation has become increasingly critical for guiding system design decisions, such as the selection of parallelization strategies, cluster configurations, and hardware provisioning. Existing simulation-based performance estimation requires reimplementing the ML framework in a simulator, which demands significant manual effort and is hard to maintain as ML frameworks evolve rapidly. This paper introduces Phantora, a hybrid GPU cluster simulator designed for performance estimation of ML training workloads. Phantora executes unmodified ML frameworks as is within a distributed, containerized environment. Each container emulates the behavior of a GPU server in a large-scale cluster, while Phantora intercepts and simulates GPU- and communication-related operations to provide high-fidelity performance estimation. We call this approach hybrid simulation of ML systems, in contrast to traditional methods that simulate static workloads. The primary advantage of hybrid simulation is that it allows direct reuse of ML framework source code in simulation, avoiding the need for reimplementation. Our evaluation shows that Phantora provides accuracy comparable to static workload simulation while supporting three state-of-the-art LLM training frameworks out-of-the-box. In addition, Phantora operates on a single GPU, eliminating the need for the resource-intensive trace collection and workload extraction steps required by traditional trace-based simulators. Phantora is open-sourced at https://github.com/QDelta/Phantora.