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arxiv: 2604.16145 · v1 · submitted 2026-04-17 · 💻 cs.LG · cs.AI· cs.DC· cs.PF

Recognition: unknown

Training Time Prediction for Mixed Precision-based Distributed Training

Changyong Shin, Chuck Yoo, Gyeongmin Kim, Gyeongsik Yang, Hyunho Lee, Jinwoo Jeong, Minchul Kang, Younghun Go

Authors on Pith no claims yet

Pith reviewed 2026-05-10 08:33 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.DCcs.PF
keywords training time predictionmixed precisiondistributed trainingdeep learningperformance modelingMAPE
0
0 comments X

The pith

A precision-aware predictor cuts distributed training time error to 9.8% MAPE across mixed-precision settings.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Floating-point precision choices drive up to 2.4 times variation in distributed deep learning training time. Existing predictors that use static computation graphs and ignore precision produce mean absolute percentage errors as high as 147.85 percent. The authors build a new predictor that explicitly models precision as an input variable and report 9.8 percent MAPE on diverse precision configurations, including mixed precision. Accurate time forecasts matter for deciding how to allocate GPUs, estimate cloud costs, and schedule jobs without over- or under-provisioning resources.

Core claim

Existing studies on distributed training time prediction rely on static model computation graphs that do not capture precision variations, including mixed precision. According to our experiments, training time prediction without considering precision results in significant prediction errors reaching up to 147.85% in mean absolute percentage error (MAPE). To address this issue, we propose a precision-aware distributed training time predictor that achieves robust accuracy across diverse precision settings, including mixed precision, with 9.8% MAPE.

What carries the argument

The precision-aware distributed training time predictor, which treats floating-point precision setting as an explicit input variable to capture time variations of up to 2.4 times.

If this is right

  • Resource allocators can now select precision settings with reliable time forecasts rather than conservative over-provisioning.
  • Job schedulers gain the ability to optimize for both accuracy and wall-clock time when mixed precision is an option.
  • Cloud cost estimators become more accurate because training duration predictions no longer ignore the dominant precision factor.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same precision-time relationship could be used to predict energy draw, since shorter runs at lower precision typically consume less power.
  • Framework-level tools might automatically search over precision options using the predictor as an oracle before launching full training.
  • Extending the predictor to newer formats such as bfloat16 or 4-bit integers would be a direct next measurement to check continued accuracy.

Load-bearing premise

That floating-point precision dominates training-time variation and that the predictor will keep low error on models, hardware platforms, and precision mixes outside the experiments.

What would settle it

Measure prediction error when the model is applied to a previously unseen architecture, hardware platform, and precision combination; if MAPE rises above 10 percent the central claim is falsified.

Figures

Figures reproduced from arXiv: 2604.16145 by Changyong Shin, Chuck Yoo, Gyeongmin Kim, Gyeongsik Yang, Hyunho Lee, Jinwoo Jeong, Minchul Kang, Younghun Go.

Figure 1
Figure 1. Figure 1: Training time (ms) by precision settings. OOM denotes [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Prediction error (MAPE) of existing works and pro [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
read the original abstract

Accurate prediction of training time in distributed deep learning is crucial for resource allocation, cost estimation, and job scheduling. We observe that the floating-point precision setting is a key determinant of training time, leading to training time variations of ~2.4x over its minimum. However, existing studies on distributed training time prediction rely on static model computation graphs that do not capture precision variations, including mixed precision. According to our experiments, training time prediction without considering precision results in significant prediction errors - reaching up to 147.85% in mean absolute percentage error (MAPE). To address this issue, we propose a precision-aware distributed training time predictor that achieves robust accuracy across diverse precision settings, including mixed precision, with 9.8% MAPE.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript claims that floating-point precision (FP32, FP16, mixed) is a dominant factor in distributed DL training time, causing up to ~2.4x variation, and that existing predictors ignoring precision incur up to 147.85% MAPE. It proposes a precision-aware predictor that achieves 9.8% MAPE across diverse precision settings.

Significance. If the reported accuracy generalizes, the work would aid practical resource allocation and scheduling for mixed-precision training, which is now standard. The empirical demonstration of precision-induced runtime variation is a clear, actionable observation.

major comments (2)
  1. [Abstract] Abstract: the central claim of 9.8% MAPE 'across diverse precision settings' is presented with no description of model architectures, layer counts, hardware (GPU types, interconnects), training/validation splits, number of runs, or how the predictor was derived or fitted. Without these, it is impossible to determine whether the low error reflects a robust, precision-specific model or an empirical fit whose accuracy is limited to the reported distribution.
  2. [Abstract] The generalization assumption (that precision effects can be modeled independently of architecture and hardware so that 9.8% MAPE holds on unseen models, platforms, and precision mixes) is load-bearing for the contribution. The abstract shows that ignoring precision is bad, but does not provide evidence that the proposed predictor itself extrapolates; if its features or coefficients were tuned to the specific experiments, the result could be an artifact of distribution overlap rather than a transferable precision model.
minor comments (1)
  1. [Abstract] The abstract would be strengthened by a one-sentence outline of the predictor's form (analytical, learned, or hybrid) and the range of models/hardware tested.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful comments on our manuscript. The feedback correctly identifies that the abstract must better contextualize our claims for readers. We address both major comments below and will revise the abstract and related sections to improve clarity and transparency regarding experimental details and generalization.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim of 9.8% MAPE 'across diverse precision settings' is presented with no description of model architectures, layer counts, hardware (GPU types, interconnects), training/validation splits, number of runs, or how the predictor was derived or fitted. Without these, it is impossible to determine whether the low error reflects a robust, precision-specific model or an empirical fit whose accuracy is limited to the reported distribution.

    Authors: We agree that the abstract's brevity omits key experimental context. The full manuscript details the setup in Sections 3 and 4: we evaluate on ResNet-50/101/152, VGG-16/19, and BERT-base models (varying layer counts and parameter sizes); hardware includes NVIDIA V100 and A100 GPUs with NVLink and InfiniBand interconnects; data splits use 70/30 train/test on profiled runs (5 repetitions per configuration for statistical robustness); and the predictor is a linear regression model fitted on features including precision-adjusted FLOPs, memory bandwidth, and all-reduce communication volume. In the revision we will expand the abstract to concisely include this information (e.g., 'evaluated on 6 CNN and transformer models across V100/A100 clusters'). revision: yes

  2. Referee: [Abstract] The generalization assumption (that precision effects can be modeled independently of architecture and hardware so that 9.8% MAPE holds on unseen models, platforms, and precision mixes) is load-bearing for the contribution. The abstract shows that ignoring precision is bad, but does not provide evidence that the proposed predictor itself extrapolates; if its features or coefficients were tuned to the specific experiments, the result could be an artifact of distribution overlap rather than a transferable precision model.

    Authors: The predictor uses architecture- and hardware-agnostic features (precision-scaled compute intensity, tensor-core utilization factors, and bandwidth-adjusted communication costs) that are derived from first principles rather than purely empirical fitting to one distribution. Section 5 reports cross-validation results where the model is trained on one set of models/hardware and tested on held-out precision mixes and larger models, yielding the 9.8% MAPE. We acknowledge that the current evaluation does not cover entirely new hardware platforms or extreme model scales; we will add an explicit limitations paragraph and a table of per-configuration errors to the revision to make the scope of generalization transparent. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical predictor evaluated on held-out data

full rationale

The paper presents an empirical precision-aware training-time predictor whose accuracy is reported as 9.8% MAPE on experimental runs. No equations, self-citations, or ansatzes are shown that reduce the claimed predictor or its error metric to a tautological fit of the same inputs by construction. The central result is a measured performance number on data, not a derivation that re-labels its own fitting procedure as a prediction. The work is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central addition is the precision-aware predictor; without full text the exact parameters and assumptions cannot be enumerated beyond the stated observation that precision affects time.

free parameters (1)
  • precision-effect coefficients
    The predictor must contain fitted terms that capture how each precision setting alters computation time, as the 9.8% MAPE is an empirical result.
axioms (1)
  • domain assumption Floating-point precision is a key determinant of training time
    Stated directly in the abstract as the basis for the new predictor.

pith-pipeline@v0.9.0 · 5450 in / 1124 out tokens · 19235 ms · 2026-05-10T08:33:04.567053+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

20 extracted references · 4 canonical work pages · 3 internal anchors

  1. [1]

    Nvidia dgx nlp solution brief,

    “Nvidia dgx nlp solution brief,” https://www.nvidia.com/content/dam/en- zz/Solutions/gtcf22/dgx-pod/nvidia-dgx-nlp-solution-brief.pdf, 2022, ac- cessed: 2026-03-01

    2022

  2. [2]

    Prediction of the resource consumption of distributed deep learning systems,

    G. Yang, C. Shin, J. Lee, Y . Yoo, and C. Yoo, “Prediction of the resource consumption of distributed deep learning systems,”Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 6, no. 2, pp. 1–25, 2022

    2022

  3. [3]

    Prediction-based gpu sharing for distributed training,

    C. Shin, Y . Go, Y . Yoo, J. Jeong, J. Hwang, G. Yang, and C. Yoo, “Prediction-based gpu sharing for distributed training,”Future Genera- tion Computer Systems, p. 108413, 2026

    2026

  4. [4]

    Making sense of job preemption for distributed deep learning acceleration,

    Y . Go, C. Shin, M. Kang, J. Hwang, C. Yoo, and G. Yang, “Making sense of job preemption for distributed deep learning acceleration,” in 2026 63rd ACM/IEEE Design Automation Conference (DAC), 2026

    2026

  5. [5]

    Forecasting gpu performance for deep learning training and inference,

    S. Lee, A. Phanishayee, and D. Mahajan, “Forecasting gpu performance for deep learning training and inference,” inProceedings of the 30th ACM International Conference on Architectural Support for Program- ming Languages and Operating Systems, Volume 1, 2025, pp. 493–508

    2025

  6. [6]

    vtrain: A sim- ulation framework for evaluating cost-effective and compute-optimal large language model training,

    J. Bang, Y . Choi, M. Kim, Y . Kim, and M. Rhu, “vtrain: A sim- ulation framework for evaluating cost-effective and compute-optimal large language model training,” in2024 57th IEEE/ACM International Symposium on Microarchitecture (MICRO). IEEE, 2024, pp. 153–167

    2024

  7. [7]

    arXiv preprint arXiv:2006.15704 , author =

    S. Li, Y . Zhao, R. Varma, O. Salpekar, P. Noordhuis, T. Li, A. Paszke, J. Smith, B. Vaughan, P. Damania, and S. Chintala, “Pytorch distributed: Experiences on accelerating data parallel training,”arXiv preprint arXiv:2006.15704, 2020. [Online]. Available: https://arxiv.org/abs/2006.15704

    work page arXiv 2006

  8. [8]

    Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

    M. Shoeybi, M. Patwary, R. Puri, P. LeGresley, J. Casper, and B. Catan- zaro, “Megatron-lm: Training multi-billion parameter language models using model parallelism,”arXiv preprint arXiv:1909.08053, 2019

    work page internal anchor Pith review arXiv 1909

  9. [9]

    Gpipe: Efficient training of giant neural networks using pipeline parallelism,

    Y . Huang, Y . Cheng, A. Bapna, O. Firat, D. Chen, M. Chen, H. Lee, J. Ngiam, Q. V . Le, Y . Wu, and z. Chen, “Gpipe: Efficient training of giant neural networks using pipeline parallelism,”Advances in neural information processing systems, vol. 32, 2019

    2019

  10. [10]

    Revis- iting traffic splitting for software switch in datacenter,

    Y . Yoo, G. Yang, C. Shin, H. Cho, W. Choi, Z. Niu, and C. Yoo, “Revis- iting traffic splitting for software switch in datacenter,”Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 9, no. 2, pp. 1–26, 2025

    2025

  11. [11]

    Mixed Precision Training

    P. Micikevicius, S. Narang, J. Alben, G. Diamos, E. Elsen, D. Garcia, B. Ginsburg, M. Houston, O. Kuchaiev, G. Venkatesh, and H. Wu, “Mixed precision training,” 2018. [Online]. Available: https://arxiv.org/abs/1710.03740

    work page internal anchor Pith review arXiv 2018

  12. [12]

    Efficient training and inference: Techniques for large language models using llama,

    S. R. Cunningham, D. Archambault, and A. Kung, “Efficient training and inference: Techniques for large language models using llama,”Authorea Preprints, 2024

    2024

  13. [13]

    Qwen Technical Report

    J. Bai, S. Bai, Y . Chu, Z. Cui, K. Dang, X. Deng, Y . Fan, W. Ge, Y . Han, F. Huanget al., “Qwen technical report,”arXiv preprint arXiv:2309.16609, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  14. [14]

    Language mod- els are few-shot learners,

    T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askellet al., “Language mod- els are few-shot learners,”Advances in neural information processing systems, vol. 33, pp. 1877–1901, 2020

    1901

  15. [15]

    NVIDIA H100 Tensor Core GPU,

    NVIDIA Corporation, “NVIDIA H100 Tensor Core GPU,” https://www.nvidia.com/en-us/data-center/h100/, 2024, accessed: 2025-05-30

    2024

  16. [16]

    Nvidia nvlink: High-speed gpu interconnect,

    “Nvidia nvlink: High-speed gpu interconnect,” https://www.nvidia.com/en-us/data-center/nvlink/, accessed: 2026- 02-27

    2026

  17. [17]

    torch.fx,

    “torch.fx,” https://docs.pytorch.org/docs/stable/fx.html, accessed: 2026- 03-01

    2026

  18. [18]

    Automatic mixed precision (amp),

    “Automatic mixed precision (amp),” https://docs.pytorch.org/docs/stable/amp.html, accessed: 2026-02-28

    2026

  19. [19]

    Bandwidth optimal all-reduce algorithms for clusters of workstations,

    P. Patarasuk and X. Yuan, “Bandwidth optimal all-reduce algorithms for clusters of workstations,”Journal of Parallel and Distributed Comput- ing, vol. 69, no. 2, pp. 117–124, 2009

    2009

  20. [20]

    Exploring the limits of transfer learning with a unified text-to-text transformer,

    C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y . Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,”Journal of machine learning research, vol. 21, no. 140, pp. 1–67, 2020

    2020