AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments | Paper Citation Record

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments As shown in Fig

Reference 1

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This paper cites As shown in Fig.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments As shown in Fig

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This paper cites As shown in Fig.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments As shown in Fig

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This paper cites As shown in Fig.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments As shown in Fig

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This paper cites NeutronOrch: rethinking sample-based GNN training under CPU- GPU heterogeneous environments.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments NeutronOrch: rethinking sample-based GNN training under CPU- GPU heterogeneous environments

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Graph attention networks for neural social recommendation

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments NeutronStar: distributed GNN training with hybrid dependency management

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This paper cites Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments XGCN: a library for large- scale graph neural network recommendations

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Semi-supervised classification with graph convolutional networks

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This paper cites TurboGNN: improving the end-to- end performance for sampling-based GNN training on GPUs.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments TurboGNN: improving the end-to- end performance for sampling-based GNN training on GPUs

Reference 11

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This paper cites Sampling meth- ods for efficient training of graph convolutional networks: a survey.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Sampling meth- ods for efficient training of graph convolutional networks: a survey

Reference 12

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments A comprehen- sive survey on graph neural networks

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This paper cites A comprehensive survey on graph neural network accelerators.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments A comprehensive survey on graph neural network accelerators

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments A survey of dynamic graph neural net- works

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments SAN- CUS: staleness-aware communication-avoiding full-graph decentral- ized training in large-scale graph neural networks

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This paper cites ASA-GNN: adaptive sampling and aggregation-based graph neural network for transaction fraud de- tection.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments ASA-GNN: adaptive sampling and aggregation-based graph neural network for transaction fraud de- tection

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This paper cites Tissue specific tumor-gene link prediction through sampling based GNN using a heterogeneous network.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Tissue specific tumor-gene link prediction through sampling based GNN using a heterogeneous network

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This paper cites PGSampler: accelerating GPU- based graph sampling in GNN systems via workload fusion.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments PGSampler: accelerating GPU- based graph sampling in GNN systems via workload fusion

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Observation a0c75a33-b6c5-4dd9-9f29-810cb95dea99 · outbound

This paper cites Scalable graph neural network training: the case for sampling.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Scalable graph neural network training: the case for sampling

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This paper cites Efficient data loader for fast sampling-based GNN training on large graphs.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Efficient data loader for fast sampling-based GNN training on large graphs

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This paper cites FastGL: a GPU- efficient framework for accelerating sampling-based GNN training at large scale.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments FastGL: a GPU- efficient framework for accelerating sampling-based GNN training at large scale

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This paper cites A Local Graph Limits Perspective on Sampling-Based GNNs.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments A Local Graph Limits Perspective on Sampling-Based GNNs

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments GNNLab: a factored system for sample-based GNN training over GPUs

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Graph neural network training and data tiering

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments DUCATI: a Dual-Cache training system for graph neural networks on giant graphs with the GPU

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Large graph convolutional network training with GPU-oriented data communication architecture

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments FastGCN: fast learning with graph con- volutional networks via importance sampling

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This paper cites Efficient neighbor-sampling-based GNN training on CPU-FPGA heterogeneous platform.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Efficient neighbor-sampling-based GNN training on CPU-FPGA heterogeneous platform

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments FreshGNN: reducing memory access via stable historical embeddings for graph neural network training

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments GNNAutoScale: scalable and expressive graph neural networks via historical embed- dings

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Marius: learning massive graph embeddings on a single machine

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments WholeGraph: a fast graph neural network training framework with multi-GPU distributed shared mem- ory architecture

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments SaGNN: a sample- based GNN training and inference hardware accelerator

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments An efficient sampling- based SpMM kernel for balancing accuracy and speed in GNN infer- ence

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments SCGraph: accelerating sample-based GNN training by staged caching of features on GPUs

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Ascend: a scalable and unified architecture for ubiquitous deep neural network computing : industry track paper

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Performance evaluation of MindSpore and PyTorch based on Ascend NPU

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Habana labs purpose-built AI inference and training processor architectures: Scaling AI training systems using standard ethernet with gaudi processor

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments AIbench: a tool for benchmarking Huawei Ascend AI processors

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Ascend-CC: Confidential Computing on Heterogeneous NPU for Emerging Generative AI Workloads

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput

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This paper cites Tackling the Dynamicity in a Production LLM Serving System with SOTA Optimizations via Hybrid Prefill/Decode/Verify Scheduling on Efficient Meta-kernels.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Tackling the Dynamicity in a Production LLM Serving System with SOTA Optimizations via Hybrid Prefill/Decode/Verify Scheduling on Efficient Meta-kernels

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This paper cites Analysis of performance and optimization in MindSpore on Ascend NPUs.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Analysis of performance and optimization in MindSpore on Ascend NPUs

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Machine learning-enabled performance model for DNN applications and AI accelerator

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This paper cites Unlocking high performance with low-bit NPUs and CPUs for highly optimized HPL- MxP on Cloud Brain II.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Unlocking high performance with low-bit NPUs and CPUs for highly optimized HPL- MxP on Cloud Brain II

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Inductive representation learning on large graphs

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Defining and evaluating network communi- ties based on ground-truth

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This paper cites Predicting positive and negative links in online social networks.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Predicting positive and negative links in online social networks

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments when to sample

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AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters

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This paper cites Cube-fx: mapping Taylor ex- pansion onto matrix multiplier-accumulators of Huawei Ascend AI processors.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Cube-fx: mapping Taylor ex- pansion onto matrix multiplier-accumulators of Huawei Ascend AI processors

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This paper cites High- utilization GPGPU design for accelerating GEMM workloads: an incremental approach.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments High- utilization GPGPU design for accelerating GEMM workloads: an incremental approach

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This paper cites HBM-based hardware accelerator for GNN sampling and aggregation.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments HBM-based hardware accelerator for GNN sampling and aggregation

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Observation 99c6cf8c-626d-4d7c-8b24-850e874a7495 · outbound

This paper cites HongTu: Scalable Full-Graph GNN Training on Multiple GPUs (via communication-optimized CPU data offloading).

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments HongTu: Scalable Full-Graph GNN Training on Multiple GPUs (via communication-optimized CPU data offloading)

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:36:15.532929Z

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Observation e625ad1a-b8d7-4aa0-9baf-d699fae15130 · outbound

This paper cites Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:36:15.535634Z

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Observation 8f78c375-7647-496b-a243-1a1726c79f68 · outbound

This paper cites Principal component analysis in the local differ- ential privacy model.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Principal component analysis in the local differ- ential privacy model

Reference 58

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Observation 2c524f60-50d0-4d52-9c88-77a1b2cc6d2f · outbound

This paper cites Accelerating graph sampling for graph machine learning using GPUs.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Accelerating graph sampling for graph machine learning using GPUs

Reference 59

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Observation 9c864bbc-2d5d-4fe4-bce4-6dd1ce0ab5e2 · outbound

This paper cites PaGraph: scaling GNN training on large graphs via computation-aware caching.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments PaGraph: scaling GNN training on large graphs via computation-aware caching

Reference 60

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Observation 231fd87e-2cb5-4499-ad74-0213796dc1c7 · outbound

This paper cites Neutronascend: Optimizing gnn training with ascend ai processors.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Neutronascend: Optimizing gnn training with ascend ai processors

Reference 61

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Observation 5ad1c623-8b2d-47e3-8583-3cc95baf8bb0 · outbound

This paper cites Paper of Distinction.

AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Paper of Distinction

Reference 62

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