Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads

Minyu Cui; Miquel Pericas

arxiv: 2606.09200 · v1 · pith:DEHI2M7Inew · submitted 2026-06-08 · 💻 cs.DC · cs.AI

Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads

Minyu Cui , Miquel Pericas This is my paper

Pith reviewed 2026-06-27 14:58 UTC · model grok-4.3

classification 💻 cs.DC cs.AI

keywords computation-communication overlapmulti-GPU trainingshared memory allocationkernel prioritydistributed machine learningoccupancy controlcollective communication

0 comments

The pith

Shared-memory allocation and priority settings let computation and communication overlap in multi-GPU ML training.

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

The paper examines the bottleneck created when computation and collective communication run one after the other in distributed ML training on multiple GPUs. It tests two runtime controls that do not require changes to vendor libraries or kernels: per-block shared-memory amounts that limit how many compute threads can run at once, and higher scheduling priority for communication streams. The goal is to free on-chip resources so communication can advance while computation continues. If the controls succeed, training runs finish faster because the two phases no longer block each other. Tests across several NVIDIA and AMD GPUs show the approach can cut total execution time by as much as 25.5 percent.

Core claim

Regulating computation-kernel residency through per-block shared-memory allocation leaves sufficient on-chip resources for communication kernels to make progress; assigning elevated priority to communication streams then ensures steady communication once resources become available, enabling concurrent execution without library or kernel modifications.

What carries the argument

shared-memory-driven occupancy shaping for computation kernels paired with elevated scheduling priority for communication kernels; the allocation limits compute occupancy so communication can use remaining resources steadily.

If this is right

Multi-GPU training workloads can complete with lower total time when computation and communication phases overlap.
The overlap is achieved without any changes to vendor-supplied libraries or kernel code.
The same controls produce measurable gains on both NVIDIA and AMD GPU hardware.
Communication no longer needs to wait for full completion of preceding computation blocks.

Where Pith is reading between the lines

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

The same resource-shaping idea could be applied to other collective operations or to inference serving workloads that mix compute and network traffic.
Hardware vendors might expose more direct controls over occupancy and priority if the technique proves reliable across more models and scales.
Workloads with very different compute-to-communication ratios may require workload-specific tuning of the shared-memory parameter.

Load-bearing premise

That per-block shared-memory allocation can be tuned to leave enough on-chip resources for communication kernels to make steady progress on the tested GPU architectures without introducing new performance regressions or correctness issues.

What would settle it

Running the same workloads on the same GPUs with the tuned shared-memory values and priority settings produces no reduction, or an increase, in measured total execution time.

Figures

Figures reproduced from arXiv: 2606.09200 by Minyu Cui, Miquel Pericas.

**Figure 2.** Figure 2: TimeRatio of baseline overlap for cb-ar. overlap across the four evaluated platforms. To quantify overlap effectiveness, we define: T imeRatio = toverlap tsequential , where tsequential is the execution time when computation and communication are executed sequentially, and toverlap is the execution time when overlap is enabled. A lower ratio indicates more effective overlap. Across all platforms, the basel… view at source ↗

**Figure 3.** Figure 3: Norm. time (optimized overlap normalized to the baseline) for [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: The overlap rate. improves overlap efficiency, although the degree of improvement depends on both GPU architecture and workload characteristics. On NVIDIA GPUs, the optimized strategy produces notable reductions in total execution time, particularly when the baseline already enables partial concurrency. Taking cb-ar for example, the optimized overlap allows communication kernels to make progress earlier, … view at source ↗

**Figure 5.** Figure 5: Norm. time (tile configuration opt2 normalized to opt1). 2 4 8 16 32 64 128 256 512 1024 0.00 0.25 0.50 0.75 1.00 Norm. time (a) cb-a2a on A40 2 4 8 16 32 64 128 256 512 1024 (b) mb-a2a on A40 2 4 8 16 32 64 128 256 512 1024 (c) cb-a2a on A100 2 4 8 16 32 64 128 256 512 1024 (d) mb-a2a on A100 tile opt1 tile opt2 [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

read the original abstract

The rapid growth of large-scale machine learning (ML) has made distributed training across multiple GPUs a fundamental component of modern ML systems. As model sizes and computational throughput continue to increase, communication overhead has become a dominant bottleneck in multi-GPU training, particularly when computation and communication are executed sequentially. This work explores concurrent execution of computation and collective communication using two portable runtime controls: shared-memory-driven occupancy shaping for computation kernels and elevated scheduling priority for communication kernels. Our approach regulates computation-kernel residency through per-block shared-memory allocation, leaving sufficient on-chip resources for communication kernels to make progress. In addition, assigning higher priority to communication streams ensures steady communication progress once resources become available. Experiments on NVIDIA A40, A100, H100, and AMD MI250X GPUs demonstrate that the proposed method enables effective computation-communication overlap and reduces total execution time by up to 25.5 percent, without modifying vendor libraries or kernel implementations.

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

The paper offers a simple runtime overlap method for multi-GPU training via shared-memory occupancy shaping plus comm stream priority, with cross-vendor experiments, but the portability rests on tuning whose details and robustness are not shown.

read the letter

The main thing to know is that this paper describes a runtime technique to overlap computation and collective communication in distributed ML training. It uses per-block shared memory allocation to limit compute kernel occupancy and leaves room for communication kernels, plus elevated priority on communication streams. Experiments on A40, A100, H100, and MI250X GPUs report up to 25.5% reduction in total time without touching vendor libraries or kernels.

The work does a reasonable job of targeting a real bottleneck and trying to stay portable across NVIDIA and AMD hardware. That cross-vendor coverage is better than many overlap papers. The combination of occupancy control through shared memory and stream priority is presented as the core idea, and it is straightforward enough that practitioners could try it.

The soft spot is the tuning. The method depends on choosing the right shared memory size per block so communication kernels can progress. The stress-test note is on point here: this choice is architecture-specific and tied to register and shared memory limits, yet the paper supplies no sensitivity analysis, no explicit allocation values per GPU, and no tests showing what happens when kernels or model sizes change. Without those, the claim of portable runtime control is weaker than stated. The abstract also gives a headline number but no baselines, error bars, or controls, so the evidence strength is difficult to judge.

This paper is for systems people working on distributed training who want lightweight overlap fixes. A reader focused on practical runtime techniques could extract value from the method and the multi-GPU results.

It deserves peer review. The problem matters, the approach is concrete and implementable, and referee input on the tuning details and experimental rigor would help.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes two portable runtime controls—per-block shared-memory allocation to shape occupancy of computation kernels and elevated scheduling priority for communication kernels—to enable concurrent execution of computation and collective communication in multi-GPU ML training. Experiments on NVIDIA A40, A100, H100, and AMD MI250X GPUs are reported to achieve effective overlap and reduce total execution time by up to 25.5% without modifying vendor libraries or kernels.

Significance. If the results hold, the work provides a non-intrusive approach to overlap that could improve efficiency of distributed training across frameworks and hardware. The emphasis on runtime controls rather than kernel changes is a practical strength that distinguishes it from prior techniques requiring code modifications.

major comments (2)

[Abstract] Abstract: The headline claim of up to 25.5% reduction is presented without any description of the experimental setup, workloads, baselines, number of trials, or error bars, preventing verification of the result.
[the description of the runtime controls and experimental evaluation] The central performance claim depends on the shared-memory allocation leaving sufficient on-chip resources for communication kernels to progress. The manuscript supplies no sensitivity analysis, explicit allocation sizes per GPU, or tests of robustness when kernel characteristics or model sizes change, undermining the portability assertion.

minor comments (1)

[Abstract] The abstract would benefit from naming the specific ML workloads or collective operations used in the experiments.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and will revise the manuscript to strengthen clarity and evaluation details.

read point-by-point responses

Referee: [Abstract] Abstract: The headline claim of up to 25.5% reduction is presented without any description of the experimental setup, workloads, baselines, number of trials, or error bars, preventing verification of the result.

Authors: We agree that the abstract would benefit from additional context. In the revision we will expand it to briefly note the GPUs tested (NVIDIA A40/A100/H100 and AMD MI250X), the ML workloads evaluated, the sequential execution baseline, and that the 25.5% figure is the maximum observed improvement across multiple runs. revision: yes
Referee: [the description of the runtime controls and experimental evaluation] The central performance claim depends on the shared-memory allocation leaving sufficient on-chip resources for communication kernels to progress. The manuscript supplies no sensitivity analysis, explicit allocation sizes per GPU, or tests of robustness when kernel characteristics or model sizes change, undermining the portability assertion.

Authors: The existing experiments already span four GPU architectures and multiple workloads, providing initial evidence of portability. We will nevertheless add explicit per-GPU shared-memory allocation sizes and a dedicated sensitivity/robustness subsection (including variation in allocation and model scale) to the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity; purely empirical technique with no derivation chain

full rationale

The paper presents a runtime method for overlapping computation and communication via per-block shared-memory allocation and stream priority, then validates it with direct experiments on A40/A100/H100/MI250X GPUs showing up to 25.5% speedup. No equations, fitted parameters, predictions, or self-citations appear in the load-bearing claims; the performance numbers are measured outcomes rather than outputs derived from the inputs by construction. The approach is therefore self-contained as an engineering technique.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities are described or can be extracted.

pith-pipeline@v0.9.1-grok · 5687 in / 996 out tokens · 15353 ms · 2026-06-27T14:58:26.773558+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

21 extracted references · 10 canonical work pages · 1 internal anchor

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