NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining | Paper Citation Record

Observation 32e9ae4a-3a7d-4f5a-b7a7-ee8442ebe258 · outbound

This paper cites ISBN 9798400705052.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining ISBN 9798400705052

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.443795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 37360f58-a956-4eda-918b-d6588f4d4e42 · outbound

This paper cites MTGR: industrial-scale generative recommendation framework in meituan.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining MTGR: industrial-scale generative recommendation framework in meituan

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.440060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 73cf06f4-b2a1-4839-ac11-899799e45321 · outbound

This paper cites MTGR: industrial-scale generative recommendation framework in meituan.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining MTGR: industrial-scale generative recommendation framework in meituan

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.435871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 53a12e2a-f9c1-4d73-bbbc-6a9c0fbb5f8d · outbound

This paper cites Bending the scaling law curve in large-scale recommendation systems.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Bending the scaling law curve in large-scale recommendation systems

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:41:04.520085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ac21f146-f5c9-4172-9de8-e5f46ab93de6 · outbound

This paper cites Bending the scaling law curve in large-scale recommendation systems.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Bending the scaling law curve in large-scale recommendation systems

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.431562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2ea7c232-a9f5-4622-9cc8-711b76575ad1 · outbound

This paper cites The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversion.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversion

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.387781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b17401b1-c7f7-4896-b79a-b0c115685dff · outbound

This paper cites Adaembed: Adaptive embedding for large-scale recommendation models.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Adaembed: Adaptive embedding for large-scale recommendation models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:51:39.988106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d2132d71-7dc6-45fd-a27e-6ecf369e8154 · outbound

This paper cites Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-16T04:54:49.681471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8561a1d3-85a7-49bc-a172-e48067c9d1ec · outbound

This paper cites Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.384440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dc851432-b6d5-485f-beff-74f7dbb86999 · outbound

This paper cites Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems

Reference 10

Resolution: malformed identifier; arxiv_id, observed 2026-05-11T05:41:04.555772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 70569ae2-561c-4fc7-a84a-fff156e264a8 · outbound

This paper cites InProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2(Vancouver, BC, Canada)(ASPLOS 2023).

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining InProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2(Vancouver, BC, Canada)(ASPLOS 2023)

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.413836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3db5c204-92e6-44a8-ae8f-707cbf4edcc6 · outbound

This paper cites Distributed hierarchical GPU parameter server for massive scale deep learning ads systems.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Distributed hierarchical GPU parameter server for massive scale deep learning ads systems

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:51:39.980297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 11b8bcd3-41aa-4a7b-9c41-7db99f077bea · outbound

This paper cites Persia: An open, hybrid system scaling deep learning-based recommenders up to 100 trillion parameters.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Persia: An open, hybrid system scaling deep learning-based recommenders up to 100 trillion parameters

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.381294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 205f5784-34f2-4c08-876e-ac7bae80584a · outbound

This paper cites Efficient and scalable huge embedding model training via distributed cache management.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Efficient and scalable huge embedding model training via distributed cache management

Reference 14

Resolution: verified exact; doi, observed 2026-05-10T18:00:41.395864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 50c35153-bbd9-47d0-96f4-8a0f6727aaf5 · outbound

This paper cites GBA: A tuning-free approach to switch between synchronous and asynchronous training for recommen- dation models.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining GBA: A tuning-free approach to switch between synchronous and asynchronous training for recommen- dation models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:51:39.999260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6c8dfb2b-2400-42b1-b771-97f7da954c87 · outbound

This paper cites InProceedings of the 27th ACM SIGKDD Confer- ence on Knowledge Discovery & Data Mining(Virtual Event, Singapore)(KDD ’21).

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining InProceedings of the 27th ACM SIGKDD Confer- ence on Knowledge Discovery & Data Mining(Virtual Event, Singapore)(KDD ’21)

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.399425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cec6e6c2-bf3f-428e-9136-d8547067258c · outbound

This paper cites CAFE+: towards compact, adaptive, and fast embedding for large-scale online recommendation models.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining CAFE+: towards compact, adaptive, and fast embedding for large-scale online recommendation models

Reference 17

Resolution: verified exact; doi, observed 2026-05-10T18:00:41.393408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6bb02ff7-c1ae-4abe-b6f6-1c400d079f0f · outbound

This paper cites Hilfer fractional advection-diffusion equations with power-law initial condition; a Numerical study using variational iteration method.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Hilfer fractional advection-diffusion equations with power-law initial condition; a Numerical study using variational iteration method

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.402857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1f9f79cc-139e-47fc-a62b-4068702975fc · outbound

This paper cites Hilfer fractional advection-diffusion equations with power-law initial condition; a Numerical study using variational iteration method.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Hilfer fractional advection-diffusion equations with power-law initial condition; a Numerical study using variational iteration method

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.373954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a5ea9811-9bb9-4271-ad1d-5208e63b8357 · outbound

This paper cites Generative recommendation models: Progress and directions.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Generative recommendation models: Progress and directions

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.426985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a896ca77-f6f3-4d07-8399-4964945dcc18 · outbound

This paper cites Slimpipe: Memory-thrifty and efficient pipeline parallelism for long-context LLM training.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Slimpipe: Memory-thrifty and efficient pipeline parallelism for long-context LLM training

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:51:39.969619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d53bdb25-79f8-4dea-9278-5ee43375927b · outbound

This paper cites Available: https://doi.org/10.1145/3712285.3759855.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Available: https://doi.org/10.1145/3712285.3759855

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.410036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 93a7940f-7820-4216-ae64-40cf04e6a610 · outbound

This paper cites Pre-train and search: Efficient embedding table sharding with pre-trained neural cost models.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Pre-train and search: Efficient embedding table sharding with pre-trained neural cost models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:51:39.972820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 54015905-e09d-495d-b8d8-1a55a2576071 · outbound

This paper cites Embedding optimization for training large-scale deep learning recommendation systems with embark.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Embedding optimization for training large-scale deep learning recommendation systems with embark

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.417958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 375ab948-9de5-4b54-9841-8bfbe2ce42e8 · outbound

This paper cites Autoshard: Automated embedding table sharding for recommender systems.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Autoshard: Automated embedding table sharding for recommender systems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:51:39.966190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d55bc863-5101-421a-8d35-69ab8aea431a · outbound

This paper cites Available: https://doi.org/10.1145/3534678.3539034.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Available: https://doi.org/10.1145/3534678.3539034

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.391071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c11ab777-0664-47e3-8440-61cf6b7cfc24 · outbound

This paper cites OPER: optimality-guided embedding table parallelization for large-scale recommendation model.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining OPER: optimality-guided embedding table parallelization for large-scale recommendation model

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:51:39.976359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation af062372-9ed8-4494-b786-eb1e9f3f80c9 · outbound

This paper cites A., Gao, L., Ivchenko, D., Basant, A., Hu, Y., Yang, J., Ardestani, E.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining A., Gao, L., Ivchenko, D., Basant, A., Hu, Y., Yang, J., Ardestani, E

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.406247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e5300932-8ae4-4e31-9197-042782070cf4 · outbound

This paper cites Accelerating neural recommendation training with embedding schedul- ing.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Accelerating neural recommendation training with embedding schedul- ing

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:51:39.984583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4b108d88-37b5-47b1-8e43-6377b619cab3 · outbound

This paper cites Mixed-Precision Embeddings for Large-Scale Recommendation Models.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Mixed-Precision Embeddings for Large-Scale Recommendation Models

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.378012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a9286def-f096-46b6-9085-a05948e28b87 · outbound

This paper cites Fusedrec: Fused embedding communication for distributed recommendation training on gpus.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Fusedrec: Fused embedding communication for distributed recommendation training on gpus

Reference 31

Resolution: verified exact; doi, observed 2026-05-10T18:00:41.464838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7b623c7a-6a2b-4a9f-b556-f9731ac4ff9c · outbound

This paper cites DQRM: Deep Quantized Recommendation Models.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining DQRM: Deep Quantized Recommendation Models

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:41:04.570750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f2d78ac2-4c4a-4c07-9247-e00c30100a5b · outbound

This paper cites Neutrino Production via $e^-e^+$ Collision at $Z$-boson Peak.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Neutrino Production via $e^-e^+$ Collision at $Z$-boson Peak

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.500283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 706258a9-0da2-49b7-8cf0-2d9c4bb81ee3 · outbound

This paper cites Disaggregated multi-tower: Topology-aware modeling technique for efficient large scale recommendation.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Disaggregated multi-tower: Topology-aware modeling technique for efficient large scale recommendation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:51:39.992253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bf4613e0-f670-46ef-a935-fca85607eb54 · outbound

This paper cites Actions speak louder than words: Trillion- parameter sequential transducers for generative recommendations.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Actions speak louder than words: Trillion- parameter sequential transducers for generative recommendations

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:51:39.995993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 976c1a6e-7beb-4c51-a1cb-08140170ad52 · outbound

This paper cites Abel, Xu Guo, Jianbing Dong, Ji Shi, and Kunlun Li.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Abel, Xu Guo, Jianbing Dong, Ji Shi, and Kunlun Li

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.468853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e43652a1-1ccf-463e-b7b0-a7ebc0fc076d · outbound

This paper cites Recis: Sparse to dense, A unified training framework for recommendation models.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Recis: Sparse to dense, A unified training framework for recommendation models

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:41:04.506348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 56be5b4a-9413-4029-87ba-a123320bd5f1 · outbound

This paper cites Recis: Sparse to dense, A unified training framework for recommendation models.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Recis: Sparse to dense, A unified training framework for recommendation models

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.447929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2937ddb1-cdf7-4049-9d2c-45eb2e0702f0 · outbound

This paper cites Unified and near-optimal multi-gpu cache for embedding-based deep learning.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Unified and near-optimal multi-gpu cache for embedding-based deep learning

Reference 39

Resolution: verified exact; doi, observed 2026-05-10T18:00:41.454806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c22681ac-4991-4320-b40f-0cb47786f236 · outbound

This paper cites In38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, May 9-12.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining In38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, May 9-12

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.476915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 59b6e76c-0b94-4cb3-b3aa-206e53372a76 · outbound

This paper cites Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Anselm Lev- skaya, Jonathan Heek, Kefan Xiao, Shivani Agrawal, and Jeff Dean.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Anselm Lev- skaya, Jonathan Heek, Kefan Xiao, Shivani Agrawal, and Jeff Dean

Reference 41

Resolution: malformed identifier; arxiv_id, observed 2026-05-11T05:41:04.487011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7b0b9092-ab3f-49f7-b08f-9c8c2c2709fa · outbound

This paper cites Training personalized recommendation systems from (GPU) scratch: look forward not backwards.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Training personalized recommendation systems from (GPU) scratch: look forward not backwards

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.458756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9a9af451-6c53-47a3-ad4f-c471c7e4c8b8 · outbound

This paper cites Hypereca: Distributed het- erogeneous in-memory embedding database for training recommender models.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Hypereca: Distributed het- erogeneous in-memory embedding database for training recommender models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:51:39.958489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 79af8c3d-abde-49c1-88c1-bc4c41464c9d · outbound

This paper cites OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-07T02:15:57.736644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fe185d4f-4ae6-4478-a44e-39660ce9e918 · outbound

This paper cites URLhttps://doi.org/10.1145/3600006.3613145.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining URLhttps://doi.org/10.1145/3600006.3613145

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.504203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation be0ada0a-2700-4529-a187-43d3e416180d · outbound

This paper cites Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation

Reference 46

Resolution: metadata mismatch; arxiv_id, observed 2026-05-28T02:04:14.298512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e25dba7b-d7c1-44c6-a764-122172d1bb08 · outbound

This paper cites Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-05-28T02:04:14.298512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2e80651b-8755-4f3a-96cf-8e41a2cc22c8 · outbound

This paper cites Design of a hybrid MPI-CUDA benchmark suite for CPU-GPU clusters.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Design of a hybrid MPI-CUDA benchmark suite for CPU-GPU clusters

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.485157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 174bbd77-9faf-41ed-8296-882743781988 · outbound

This paper cites DCMA: accelerating parallel DMA transfers with a multi-port direct cached memory access in a massive-parallel vector processor.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining DCMA: accelerating parallel DMA transfers with a multi-port direct cached memory access in a massive-parallel vector processor

Reference 49

Resolution: verified exact; doi, observed 2026-05-10T18:00:41.461969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8ff95d63-791e-4f8e-a14a-5bdd12f12d2d · outbound

This paper cites Td-pipe: Temporally-disaggregated pipeline parallelism architecture for high- throughput LLM inference.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Td-pipe: Temporally-disaggregated pipeline parallelism architecture for high- throughput LLM inference

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.492938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 85dcdb35-9dae-4172-bb50-483565ed9672 · outbound

This paper cites Revisiting parameter server in LLM post-training.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Revisiting parameter server in LLM post-training

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:41:04.475223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2f46a396-bc0c-4365-80a7-5d8239c5ff28 · outbound

This paper cites Revisiting parameter server in LLM post-training.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Revisiting parameter server in LLM post-training

Reference 52

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T18:00:41.452179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cd4a9e0b-48e4-43bc-9bd9-5afc4982687f · outbound

This paper cites COMET: fine-grained computation-communication overlapping for mixture-of- experts.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining COMET: fine-grained computation-communication overlapping for mixture-of- experts

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:51:39.962956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f0c0e135-1cc0-468e-a25d-23fdcddee4b9 · outbound

This paper cites Kuairand: An unbiased sequential recommendation dataset with randomly exposed videos.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Kuairand: An unbiased sequential recommendation dataset with randomly exposed videos

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.508061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dd2abcad-0d69-4fe4-ac01-e78d35c0966a · outbound

This paper cites Fuxi-α: Scaling recommendation model with feature interaction enhanced transformer.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Fuxi-α: Scaling recommendation model with feature interaction enhanced transformer

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.472927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a236ebce-4f8a-496a-803f-199eecf706b8 · outbound

This paper cites Torchrec: a pytorch domain library for recommendation systems.

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining Torchrec: a pytorch domain library for recommendation systems

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:41.496397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers