arxiv: 2509.24859 · v2 · submitted 2025-09-29 · 💻 cs.DC

HARP: Orchestrating Automated Parallel Training on Heterogeneous GPU Clusters

Antian Liang , Zhigang Zhao , Kai Zhang , Xuri Shi , Chuantao Li , Chunxiao Wang , Zhenying He , Yinan Jing

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X. Sean Wang

This is my paper

Pith reviewed 2026-05-18 12:18 UTC · model grok-4.3

classification 💻 cs.DC

keywords heterogeneous GPU clustersautomated parallel traininginter-operator parallelismheterogeneity-aware scheduling1F1B schedulerdistributed model trainingresource utilization

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The pith

Harp automates parallel training for heterogeneous GPU clusters to deliver 1.3x-1.6x higher performance than current frameworks.

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

Harp targets the problem of resource underutilization in distributed model training on clusters with diverse GPU types and network connections. Current frameworks built for identical hardware often leave some accelerators idle or cause excessive communication delays. The system uses a fine-grained planner to find effective ways to split operations across different devices while keeping loads balanced. It also features a scheduler that adjusts when microbatches run based on actual network speeds to hide communication behind computation. If these components work as intended, training jobs can complete faster by making full use of all available hardware without major extra costs.

Core claim

Harp introduces a fine-grained planner that efficiently searches a wide space for the inter-operator parallel strategy, enabling it to alleviate communication overheads while maintaining balanced loads across heterogeneous accelerators. In addition, Harp implements a heterogeneity-aware 1F1B scheduler that adaptively adjusts the execution timing and ordering of microbatches based on network characteristics, maximizing computation-communication overlap under cross-cluster interconnects while incurring only minimal memory overhead.

What carries the argument

The fine-grained planner searching inter-operator parallel strategies combined with the heterogeneity-aware 1F1B scheduler for adaptive microbatch timing.

If this is right

Training performance improves by 1.3x to 1.6x on heterogeneous setups compared to state-of-the-art frameworks.
Communication overheads are reduced through better parallel strategy selection.
Loads are balanced across accelerators of different capabilities.
Computation and communication overlap is maximized with low memory cost.

Where Pith is reading between the lines

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

Similar planning and scheduling ideas could apply to other heterogeneous computing environments like those mixing CPUs and GPUs.
The scheduler's adaptability suggests benefits in dynamic environments where network conditions change during training.
Future systems might use the same search technique to handle additional forms of parallelism automatically.

Load-bearing premise

The fine-grained planner can efficiently search the inter-operator parallel strategy space and the heterogeneity-aware scheduler can adapt execution timing without incurring prohibitive search or memory overhead on realistic heterogeneous clusters.

What would settle it

A benchmark on a real heterogeneous GPU cluster where Harp shows no speedup or incurs high planning and memory costs compared to baseline frameworks would challenge the central claims.

Figures

Figures reproduced from arXiv: 2509.24859 by Antian Liang, Chuantao Li, Chunxiao Wang, Kai Zhang, X. Sean Wang, Xuri Shi, Yinan Jing, Zhenying He, Zhigang Zhao.

**Figure 1.** Figure 1: Example heterogeneous cluster composed of multiple homogeneous subclusters, with fast interconnects within subclusters but slower interconnects across them. which model developers typically address by deploying large homogeneous clusters composed of identical accelerators. However, hardware vendors such as NVIDIA now release new accelerator architectures on an annual cycle[15, 16]. Due to budget constrain… view at source ↗

**Figure 2.** Figure 2: Classic 1F1B pipeline scheduler. different combinations of stage–mesh pairs and selects the optimal one as the inter-op parallel strategy. For pipeline scheduling, it accelerates inter-op parallelism by splitting a batch into multiple microbatches that are executed in a pipelined manner [6, 12, 13]. Specifically, each microbatch completes its forward and backward passes by traversing all pipeline stages. … view at source ↗

**Figure 3.** Figure 3: Pipeline timeline of case studies. Another approach confines intra-op parallelism to homogeneous meshes and introduces heterogeneity only at the inter-op level [24, 34]. This avoids cross-cluster collective communication over slow links, but restricts the search space of candidate meshes. At the same time, inter-op strategy planning is usually performed at a coarse layer granularity to avoid high profili… view at source ↗

**Figure 4.** Figure 4: Overview of Hapt workflow. opportunities to hide communication latency. However, it applies a fixed scheduling strategy, where each stage launches a static number of extra forward microbatches, which is less effective under heterogeneous networks. Specifically: (i) even when inter-stage communication latency is small and requires no hiding, the scheduler still launches two additional forward microbatches… view at source ↗

**Figure 5.** Figure 5: (a,b), although both cases incur the same compute and communication cost per stage, launching four forward microbatches creates a larger gap than three, thereby enabling more inter-stage communication to be overlapped. Thus, larger communication costs are compensated by proportionally larger launch counts. Let 𝑁𝑖 denote the number of forward microbatches launched by stage 𝑖 during warm-up. When deployed … view at source ↗

**Figure 6.** Figure 6: (a) Behaviour of layer construction in Alpa. (b) Behaviour of layer construction in Zero-Redundant Profiler. structural information, which limits opportunities for pruning during profiling. Consequently, planners are restricted to coarse-layer granularity to keep profiling time practical. To address this limitation, we propose the Zero-Redundant Profiler. First, it constructs a fine-grained layer sequence… view at source ↗

**Figure 7.** Figure 7: End-to-end training latency of Hapt compared with baselines under different heterogeneous configurations [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗

**Figure 9.** Figure 9: Comparison of end-to-end training latency across homogeneous and heterogeneous GPU clusters. Overlap Analysis. Beyond load balancing, differences in communication–computation overlap also significantly impact performance. As shown in [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗

**Figure 10.** Figure 10: End-to-end training latency of different systems under varying cross-cluster bandwidths (3–10 Gbps). 6.4 Ablation Study of Layer Granularity We examine the effectiveness of applying heterogeneous inter-op parallel strategy planning at different layer granularities. Using the heterogeneous configuration in [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗

**Figure 11.** Figure 11: (a) Effect of fine layer granularity, (b) Effect of joint optimization of planning and scheduling. 1248 seconds and finishes the DP search in just 133 seconds, reducing the total overhead to about 23 minutes. We further evaluate the scalability of our planner under heterogeneous configurations, as shown in [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗

read the original abstract

With the rapid evolution of GPU architectures, the heterogeneity of model training infrastructures is steadily increasing. In such environments, effectively utilizing all available heterogeneous accelerators becomes critical for distributed model training. However, existing frameworks, which are primarily designed for homogeneous clusters, often exhibit significant resource underutilization when deployed on heterogeneous accelerators and networks. In this paper, we present Harp, an automated parallel training framework designed specifically for heterogeneous clusters. Harp introduces a fine-grained planner that efficiently searches a wide space for the inter-operator parallel strategy, enabling Harp to alleviate communication overheads while maintaining balanced loads across heterogeneous accelerators. In addition, Harp implements a heterogeneity-aware 1F1B scheduler that adaptively adjusts the execution timing and ordering of microbatches based on network characteristics, maximizing computation-communication overlap under cross-cluster interconnects while incurring only minimal memory overhead. Our evaluation results show that Harp can deliver 1.3x-1.6x higher performance on heterogeneous clusters than state-of-the-art training frameworks.

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.

Desk Editor's Note private letter to a colleague

Harp proposes a planner and scheduler for parallel training on heterogeneous GPUs with claimed speedups, but the evaluation details are insufficient to fully assess the gains.

read the letter

Harp's main pitch is that its fine-grained inter-operator planner and heterogeneity-aware 1F1B scheduler can get 1.3x-1.6x better throughput on mixed GPU clusters than current frameworks. The new part is adapting the planning and scheduling specifically for varying accelerators and cross-cluster networks instead of assuming everything is the same. It does a reasonable job laying out why homogeneous designs fall short and how these components address load balance and compute-communication overlap with low memory cost. The weak part is the evaluation. There are no specifics on the hardware configurations used, what the state-of-the-art baselines actually were, or any stats on variability. The stress-test point about planner search time and scheduler overhead is a real concern here; without numbers showing those stay negligible, the net speedup could be smaller than advertised on bigger or more varied clusters. If the full paper has the missing details and shows the overheads are indeed small, the contribution would be more convincing. This is the kind of paper for engineers and researchers focused on scaling training systems to whatever hardware is available. It would interest readers who deal with production clusters that aren't all identical GPUs. The core idea is solid enough that it should go to a serious referee for a proper look at the code and experiments. I recommend sending it out for peer review.

Referee Report

2 major / 2 minor

Summary. The manuscript presents HARP, an automated parallel training framework for heterogeneous GPU clusters. It proposes a fine-grained planner for searching inter-operator parallel strategies to alleviate communication overheads and balance loads across heterogeneous accelerators, along with a heterogeneity-aware 1F1B scheduler that adaptively adjusts microbatch execution timing and ordering to maximize computation-communication overlap with minimal memory overhead. The central empirical claim is that HARP delivers 1.3x-1.6x higher performance than state-of-the-art frameworks on heterogeneous clusters.

Significance. If the performance claims hold under rigorous evaluation, this work would be significant for the field of distributed deep learning systems. As GPU clusters become more heterogeneous due to evolving architectures, frameworks that efficiently utilize mixed hardware can lead to substantial improvements in training efficiency and resource utilization. The focus on automated planning and adaptive scheduling addresses a practical pain point, and if the overheads are indeed minimal, it could influence the design of future training systems.

major comments (2)

[Evaluation] Evaluation section: The reported 1.3x-1.6x speedups are presented without details on cluster configurations (GPU types/counts, heterogeneity degree, network topologies), baseline implementations, statistical significance, or results across varying model sizes. These omissions are load-bearing for the central performance claim, as the speedups cannot be assessed for robustness or generalizability without them.
[Planner and Scheduler] Planner and scheduler sections: The fine-grained planner is asserted to efficiently enumerate inter-operator strategies and the 1F1B scheduler to adapt without prohibitive overhead, but no concrete bounds or measurements are supplied (e.g., planner runtime vs. operator count or heterogeneity degree; peak memory delta vs. standard 1F1B). If search costs scale combinatorially or adaptation inflates memory, the net speedup disappears, directly undermining the headline result.

minor comments (2)

[Abstract] Abstract: 'State-of-the-art training frameworks' is not named; explicitly listing baselines (e.g., Megatron-LM, DeepSpeed) would improve clarity.
[Introduction] Notation: Inter-operator parallel strategy terms could be defined earlier with a small example to aid readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive review of our manuscript. We address each of the major comments below and outline the revisions we plan to make to address the concerns raised.

read point-by-point responses

Referee: [Evaluation] Evaluation section: The reported 1.3x-1.6x speedups are presented without details on cluster configurations (GPU types/counts, heterogeneity degree, network topologies), baseline implementations, statistical significance, or results across varying model sizes. These omissions are load-bearing for the central performance claim, as the speedups cannot be assessed for robustness or generalizability without them.

Authors: We agree that providing these details is essential for validating the performance claims. Accordingly, we will revise the Evaluation section to include comprehensive information on the cluster setups, including specific GPU models and quantities used in our heterogeneous testbeds, the degree of heterogeneity, and network configurations. We will also detail the baseline implementations, report results with statistical measures such as means and standard deviations from repeated experiments, and present performance data for a broader range of model sizes. These changes will be incorporated in the next version of the manuscript. revision: yes
Referee: [Planner and Scheduler] Planner and scheduler sections: The fine-grained planner is asserted to efficiently enumerate inter-operator strategies and the 1F1B scheduler to adapt without prohibitive overhead, but no concrete bounds or measurements are supplied (e.g., planner runtime vs. operator count or heterogeneity degree; peak memory delta vs. standard 1F1B). If search costs scale combinatorially or adaptation inflates memory, the net speedup disappears, directly undermining the headline result.

Authors: We recognize the need for explicit measurements to demonstrate that the overheads remain low. In the revised manuscript, we will add quantitative results on the planner's runtime scaling with respect to the number of operators and the level of heterogeneity. Additionally, we will include comparisons of peak memory consumption between our heterogeneity-aware 1F1B scheduler and the standard 1F1B approach. These measurements will show that the overheads are minimal and do not offset the achieved speedups. We will also provide any relevant theoretical analysis on the search efficiency. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical system claims rest on direct benchmarking

full rationale

The paper presents an engineering framework (Harp) whose central claims are performance speedups measured on heterogeneous GPU clusters. These results are obtained by running the implemented planner and scheduler against baselines on concrete workloads, not by any closed mathematical derivation, fitted parameter renamed as prediction, or self-citation chain. No equations appear that would reduce the reported 1.3x-1.6x gains to quantities defined from the same runs; the planner's search and the 1F1B scheduler's adaptation are algorithmic descriptions whose overheads are asserted to be low and then validated empirically. The derivation chain is therefore self-contained against external benchmarks and contains none of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no explicit free parameters, axioms, or invented entities; the system appears to rest on standard assumptions about GPU compute and network latency that are common in the distributed-training literature.

pith-pipeline@v0.9.0 · 5726 in / 1160 out tokens · 27956 ms · 2026-05-18T12:18:15.840256+00:00 · methodology

discussion (0)

Reference graph

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