Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs | Paper Citation Record

Observation d8aaa988-1371-4436-b58b-afe3287819e2 · outbound

This paper cites Flux: Fine-grained computation-communication overlap- ping gpu kernel library.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Flux: Fine-grained computation-communication overlap- ping gpu kernel library

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.486836Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 2d78b25b-6b62-4aac-b645-42a7f19b1333 · outbound

This paper cites Cen- tauri: Enabling efficient scheduling for communication-computation overlap in large model training via communication partitioning.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Cen- tauri: Enabling efficient scheduling for communication-computation overlap in large model training via communication partitioning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.490470Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 1219b650-6eea-4c59-b70d-30fba8d79280 · outbound

This paper cites P4COM: In-Network Computation with Programmable Switches.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs P4COM: In-Network Computation with Programmable Switches

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:36:17.329800Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation c2448228-6e64-4cc1-9e45-9bbb18f76857 · outbound

This paper cites Programmable Switch as a Parallel Computing Device.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Programmable Switch as a Parallel Computing Device

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-04T22:35:33.046063Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 7aad3c3d-430b-47c8-88fa-267ede186a41 · outbound

This paper cites Flare: Flexible in-network allreduce.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Flare: Flexible in-network allreduce

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.645687Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 652c75e9-5387-4ba6-9ba3-4aad24381d35 · outbound

This paper cites DeepSeek-V3 Technical Report.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs DeepSeek-V3 Technical Report

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-11T21:36:17.351298Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 3ac7a5fe-f986-485f-9047-1278aeb4f6d4 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-11T21:36:17.357146Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation ff4d0357-76fe-43df-b2b8-bee0f05e6fa3 · outbound

This paper cites In-network aggregation for shared machine learning clus- ters.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs In-network aggregation for shared machine learning clus- ters

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.609462Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation a3442206-eff5-412b-b2ba-6a77c426cabe · outbound

This paper cites Scal- able hierarchical aggregation protocol (sharp): A hardware architecture for efficient data reduction.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Scal- able hierarchical aggregation protocol (sharp): A hardware architecture for efficient data reduction

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.639031Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 68ab322c-553b-492f-8853-1e76ba855b60 · outbound

This paper cites Faster- moe: modeling and optimizing training of large-scale dynamic pre- trained models.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Faster- moe: modeling and optimizing training of large-scale dynamic pre- trained models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.612378Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 66d6dfbe-ae19-45a6-b19d-348e99adf106 · outbound

This paper cites Traci: Network acceleration of input-dynamic communication for large- scale deep learning recommendation model.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Traci: Network acceleration of input-dynamic communication for large- scale deep learning recommendation model

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.590829Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 0189ad2e-628c-411a-946e-69132cb3b7e6 · outbound

This paper cites Tutel: Adaptive mixture-of-experts at scale.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Tutel: Adaptive mixture-of-experts at scale

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.600135Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation ed8b7a6d-3469-46bd-9f3c-844ed7ce306f · outbound

This paper cites Nvswitch and dgx-2.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Nvswitch and dgx-2

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.570062Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation f889ec92-4173-4a2b-89e3-96320938da26 · outbound

This paper cites The nvlink-network switch: Nvidia’s switch chip for high communication-bandwidth superpods.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs The nvlink-network switch: Nvidia’s switch chip for high communication-bandwidth superpods

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.576256Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 9ef579c3-0949-4e8e-9c2c-e24f23a3b168 · outbound

This paper cites Breaking the com- putation and communication abstraction barrier in distributed machine learning workloads.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Breaking the com- putation and communication abstraction barrier in distributed machine learning workloads

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.579137Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 0517f699-f6a9-434f-96fb-6ce5aa0187d7 · outbound

This paper cites A detailed and flexible cycle-accurate network-on-chip simulator.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs A detailed and flexible cycle-accurate network-on-chip simulator

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.603575Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 6aa3d965-b3db-4516-bb78-5bcb7cc6b6a1 · outbound

This paper cites Scaling Laws for Neural Language Models.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Scaling Laws for Neural Language Models

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-11T21:36:17.344156Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 4bab2dad-00e5-4980-bc60-8a55f972637d · outbound

This paper cites Accel-sim: An extensible simulation framework for validated gpu modeling.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Accel-sim: An extensible simulation framework for validated gpu modeling

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.635732Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation d02461ce-b458-4c51-a25f-0629768d9705 · outbound

This paper cites An in-network architecture for accelerating shared-memory multiprocessor collectives.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs An in-network architecture for accelerating shared-memory multiprocessor collectives

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.564395Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 5793d8d8-63ce-4261-b19c-519b93ded90f · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-11T21:36:17.284055Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 4d47f29a-04a9-477e-a165-91526162fc4b · outbound

This paper cites The case for network accelerated query processing.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs The case for network accelerated query processing

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.567354Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 118159d9-3fb7-4efd-81f2-08ac939d6528 · outbound

This paper cites Accelerating distributed {MoE}training and inference with lina.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Accelerating distributed {MoE}training and inference with lina

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.558134Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 2abf725b-d20c-43d1-88cc-38191b9b044a · outbound

This paper cites Accel- erating distributed reinforcement learning with in-switch computing.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Accel- erating distributed reinforcement learning with in-switch computing

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.593694Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation db243b18-bb27-49d2-926a-fade00adcaf1 · outbound

This paper cites In-network aggregation with transport transparency for distributed training.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs In-network aggregation with transport transparency for distributed training

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.539780Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation c5d66def-df20-4646-9aa5-749a456c50a3 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Swin transformer: Hierarchical vision transformer using shifted windows

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.631385Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation b5957924-ea6d-4e2c-ac34-1a69694ede2b · outbound

This paper cites Rammer: Enabling holistic deep learning compiler optimizations with{rTasks}.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Rammer: Enabling holistic deep learning compiler optimizations with{rTasks}

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.606497Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 1739d27d-79a5-43d1-96d3-d582803fe721 · outbound

This paper cites The llama 4 herd: The beginning of a new era of natively mul- timodal ai innovation.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs The llama 4 herd: The beginning of a new era of natively mul- timodal ai innovation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.582111Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation ba401268-cc32-40d5-b5a1-55e89d5230e9 · outbound

This paper cites Finepack: Transparently improving the efficiency of fine-grained trans- fers in multi-gpu systems.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Finepack: Transparently improving the efficiency of fine-grained trans- fers in multi-gpu systems

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.573203Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation dd5fa225-7254-41be-acb6-3c24b9cf0e3d · outbound

This paper cites HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:36:17.246414Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 2c465efd-8f55-4ada-afd9-1a1d9fd376cf · outbound

This paper cites Doubling all2all performance with nvidia collective com- munication library 2.12.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Doubling all2all performance with nvidia collective com- munication library 2.12

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.584984Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation bdc0f279-8b34-432a-8a98-b208d76bd550 · outbound

This paper cites Nvidia h100 tensor core gpu.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Nvidia h100 tensor core gpu

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.597459Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation b842fef6-4895-45a5-aeb5-49a66f4a4a92 · outbound

This paper cites Nvidia h200 tensor core gpu.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Nvidia h200 tensor core gpu

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.587927Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 43f2ee47-ab11-44e2-af9f-b38941702130 · outbound

This paper cites One giant superchip for llms, recommenders, and gnns: Introducing nvidia gh200 nvl32.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs One giant superchip for llms, recommenders, and gnns: Introducing nvidia gh200 nvl32

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.648922Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation b0638a45-82c5-4e7c-8956-b65115e87c15 · outbound

This paper cites Introduction to nvidia dgx h100/h200 systems.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Introduction to nvidia dgx h100/h200 systems

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.554831Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 2a569fb8-08df-428d-b5de-c66ec6cb2bca · outbound

This paper cites Nvidia blackwell architecture technical brief.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Nvidia blackwell architecture technical brief

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.561061Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation b878c445-a05d-437e-964d-08a6e7ff91cc · outbound

This paper cites Nvidia gb200 nvl72.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Nvidia gb200 nvl72

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.536760Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation df34deb4-f14e-4f49-8c5d-95ae300e039b · outbound

This paper cites Improving network performance of hpc systems using nvidia magnum io nvshmem and gpudirect async.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Improving network performance of hpc systems using nvidia magnum io nvshmem and gpudirect async

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.530730Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation f926e173-bb68-43cd-9faa-dcea09dd657c · outbound

This paper cites The nvidia quantum infiniband platform.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs The nvidia quantum infiniband platform

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.545512Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 54e38bca-d120-48c0-bde1-679b3cf2b603 · outbound

This paper cites Inside the nvidia rubin platform: Six new chips, one ai su- percomputer.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Inside the nvidia rubin platform: Six new chips, one ai su- percomputer

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.519059Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 572bfeb3-2102-49c2-8240-756e4b3ba58d · outbound

This paper cites Gpt-oss.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Gpt-oss

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.522122Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 29af7b48-0b80-45bf-94c6-615d1bd9303f · outbound

This paper cites Introducing gpt-5.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Introducing gpt-5

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.527840Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 6df6264e-4e90-4e1d-87e1-7325b532b00b · outbound

This paper cites T3: Transparent tracking & triggering for fine-grained overlap of compute & collectives.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs T3: Transparent tracking & triggering for fine-grained overlap of compute & collectives

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.511683Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 77bec684-dab2-403a-865e-d114cfbe462b · outbound

This paper cites Deepspeed-moe: Advancing mixture- of-experts inference and training to power next-generation ai scale.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Deepspeed-moe: Advancing mixture- of-experts inference and training to power next-generation ai scale

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.551934Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 558d1fd1-d428-4962-96b1-a54b2913fb07 · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.548517Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 308c34a3-89c6-4bac-bfa1-d697cfe44f49 · outbound

This paper cites Scaling distributed machine learning with{In-Network}aggregation.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Scaling distributed machine learning with{In-Network}aggregation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.504310Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation cfa6e07a-a2c9-416f-a07b-b3d504595984 · outbound

This paper cites Se-moe: A scalable and efficient mixture- of-experts distributed training and inference system.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Se-moe: A scalable and efficient mixture- of-experts distributed training and inference system

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.507570Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 532d8edc-981b-413f-9861-6767b870fde2 · outbound

This paper cites Unveiling super experts in mixture-of-experts large language models.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Unveiling super experts in mixture-of-experts large language models

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:36:17.268469Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation bf97066d-09fb-483f-9b9a-fadea72da56e · outbound

This paper cites Design compiler® rtl synthesis.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Design compiler® rtl synthesis

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.500714Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation d5261ea6-e128-4abd-9d2c-0c1430b02128 · outbound

This paper cites Pangu ultra moe: How to train your big moe on ascend npus.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Pangu ultra moe: How to train your big moe on ascend npus

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:36:17.238231Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation e80ec39f-2406-4a05-8622-e86597b97e47 · outbound

This paper cites Cheetah: Accelerating database queries with switch pruning.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Cheetah: Accelerating database queries with switch pruning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.515451Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 615536d0-2cec-4bfc-a1a7-548b971b70f0 · outbound

This paper cites Tsmc 16nm and 12nm process technologies.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Tsmc 16nm and 12nm process technologies

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.525037Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 0fb33a54-e1f3-4896-9d6c-798d44111a91 · outbound

This paper cites Attention is all you need.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Attention is all you need

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.533967Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 1a885280-4baf-4807-9d54-3275b81594ff · outbound

This paper cites Harnessing inter-gpu shared memory for seamless moe communication-computation fusion.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Harnessing inter-gpu shared memory for seamless moe communication-computation fusion

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.542477Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 3ae2f79f-7430-481e-8f41-ef75802b76fd · outbound

This paper cites Overlap communication with dependent computation via decomposition in large deep learning models.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Overlap communication with dependent computation via decomposition in large deep learning models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.642453Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 6ea5de0b-b536-4367-896d-98167c7d00ab · outbound

This paper cites Qwen3 Technical Report.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Qwen3 Technical Report

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-11T21:36:17.225486Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 131bf0de-c655-4c01-821e-881e3cfa9486 · outbound

This paper cites Towards compute-aware in-switch computing for llms tensor-parallelism on multi-gpu systems.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Towards compute-aware in-switch computing for llms tensor-parallelism on multi-gpu systems

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.493832Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 59426a67-6d3a-4c0a-80ad-b47819ad080a · outbound

This paper cites Comet: Fine-grained computation-communication overlapping for mixture-of-experts.arXiv preprint arXiv:2502.19811.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Comet: Fine-grained computation-communication overlapping for mixture-of-experts.arXiv preprint arXiv:2502.19811

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:36:17.317668Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation b49c3e5e-848c-451a-bfd4-a36dbd45edef · outbound

This paper cites Insights into deepseek-v3: Scaling challenges and reflections on hardware for ai architectures.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Insights into deepseek-v3: Scaling challenges and reflections on hardware for ai architectures

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.497322Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 7d3fc9e6-1de1-45d1-925c-290c4fc2dab9 · outbound

This paper cites Deepep: an efficient expert-parallel communication library.

Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs Deepep: an efficient expert-parallel communication library

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T20:38:01.483846Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers