A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO | Paper Citation Record

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This paper cites Carbon explorer: A holistic framework for designing carbon aware datacenters.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Carbon explorer: A holistic framework for designing carbon aware datacenters

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This paper cites Gar- net: A detailed on-chip network model inside a full-system simulator.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Gar- net: A detailed on-chip network model inside a full-system simulator

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This paper cites A survey of computer architecture simulation techniques and tools.Ieee Access, 7:78120–78145, 2019.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO A survey of computer architecture simulation techniques and tools.Ieee Access, 7:78120–78145, 2019

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Partir: Composing spmd partitioning strategies for machine learning

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation

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This paper cites Deepflow: A cross-stack pathfinding framework for distributed ai systems.ACM Transactions on Design Automation of Electronic Systems, 29(2):1–20, 2024.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Deepflow: A cross-stack pathfinding framework for distributed ai systems.ACM Transactions on Design Automation of Electronic Systems, 29(2):1–20, 2024

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This paper cites Efficient Large Scale Language Modeling with Mixtures of Experts.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Efficient Large Scale Language Modeling with Mixtures of Experts

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This paper cites Understanding the future of energy efficiency in multi-module gpus.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Understanding the future of energy efficiency in multi-module gpus

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This paper cites vtrain: A simulation framework for evaluating cost- effective and compute-optimal large language model training.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO vtrain: A simulation framework for evaluating cost- effective and compute-optimal large language model training

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This paper cites Dtco including sustainability: Power-performance-area-cost-environmental score (ppace) analysis for logic technologies.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Dtco including sustainability: Power-performance-area-cost-environmental score (ppace) analysis for logic technologies

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO The datacenter as a computer: Designing warehouse-scale machines

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This paper cites Stco: driving the more than moore era.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Stco: driving the more than moore era

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This paper cites A survey of cache simulators.ACM Computing Surveys (CSUR), 53(1):1–32, 2020.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO A survey of cache simulators.ACM Computing Surveys (CSUR), 53(1):1–32, 2020

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This paper cites Understanding gpu power: A survey of profiling, modeling, and simulation methods.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Understanding gpu power: A survey of profiling, modeling, and simulation methods

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This paper cites Wattch: A framework for architectural-level power analysis and optimizations.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Wattch: A framework for architectural-level power analysis and optimizations

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This paper cites TVM: An automated End-to-End optimizing compiler for deep learning.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO TVM: An automated End-to-End optimizing compiler for deep learning

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This paper cites cuDNN: Efficient Primitives for Deep Learning.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO cuDNN: Efficient Primitives for Deep Learning

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This paper cites LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale

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This paper cites CodeCarbon: v2.4.1.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO CodeCarbon: v2.4.1

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This paper cites Gloo100k: Collective communications library with various primitives for multi-machine training.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Gloo100k: Collective communications library with various primitives for multi-machine training

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This paper cites Stablehlo: A portable high-level operations set for machine learning models.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Stablehlo: A portable high-level operations set for machine learning models

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This paper cites Torchscript documentation.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Torchscript documentation

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This paper cites Available at https://docs.nvidia.com/cuda/ parallel-thread-execution/index.html.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Available at https://docs.nvidia.com/cuda/ parallel-thread-execution/index.html

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Nvidia dgx h200, 2024

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO The rising costs of training frontier AI models

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This paper cites Total cost of ownership model for data center technology evaluation.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Total cost of ownership model for data center technology evaluation

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This paper cites Total cost of ownership model for data center technology evaluation.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Total cost of ownership model for data center technology evaluation

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Logp: Towards a realistic model of parallel computation

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This paper cites Large scale distributed deep networks.Advances in neural information processing systems, 25, 2012.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Large scale distributed deep networks.Advances in neural information processing systems, 25, 2012

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Uptime institute global data center survey 2024

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Proteus: Simulating the performance of distributed dnn training.IEEE Transactions on Parallel and Distributed Systems, 2024

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Cordoba: Carbon-efficient optimization framework for computing systems

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Amant, Karthikeyan Sankaralingam, and Doug Burger

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Llmcarbon: Modeling the end-to-end carbon footprint of large language models, 2024

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Echo: Simulating Distributed Training At Scale

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Mpi: A message-passing interface standard, 1994

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Estimating gpu memory consumption of deep learning models

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO MIT Press, 2016

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Google data centers efficiency, 2024

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Catch: a cost analysis tool for co- optimization of chiplet-based heterogeneous systems, 2025

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Gpgpu power modeling for multi-domain voltage-frequency scaling

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Accelerating design space ex- ploration for LLM training systems with multi-experiment parallel simulation

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO A survey on performance modeling and prediction for distributed dnn training.IEEE Transac- tions on Parallel and Distributed Systems, 2024

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Act: Designing sustainable computer systems with an architectural carbon modeling tool

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Chasing car- bon: The elusive environmental footprint of computing

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Onnxim: A fast, cycle-level multi-core npu simulator, 2024

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO An analytical framework for estimating tco and exploring data center design space

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Ryckaert

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Towards the systematic reporting of the energy and carbon footprints of machine learning.Journal of Machine Learning Research, 21(248):1–43, 2020

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Towards the systematic reporting of the energy and carbon footprints of machine learning, 2022

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Group operation assembly language-a flexible way to express collective com- munication

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO An integrated gpu power and performance model

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A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Unresolved cited work

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Observation d3d924b6-120f-4f7e-af78-0438196405f4 · outbound

This paper cites imec.netzero public, 2024.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO imec.netzero public, 2024

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.336695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5405c30a-c7a0-4311-b747-e981ceea3a9b · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.340228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d016ba27-5c77-411a-bcc5-88595efefc90 · outbound

This paper cites Calculon: a methodology and tool for high-level co-design of systems and large language models.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Calculon: a methodology and tool for high-level co-design of systems and large language models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.343296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc697b19-932f-498a-bc1e-92cbcf7b64d1 · outbound

This paper cites DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.346347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3d6af5a-8e8b-4764-9672-6aa70861b738 · outbound

This paper cites A quantitative evaluation of contemporary gpu simulation methodology.Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2(2):1–28, 2018.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO A quantitative evaluation of contemporary gpu simulation methodology.Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2(2):1–28, 2018

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.350083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 857459fc-767c-471b-804f-01562e51e737 · outbound

This paper cites Scarif: Towards carbon modeling of cloud servers with accelerators.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Scarif: Towards carbon modeling of cloud servers with accelerators

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.353238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21b109d6-ab52-4c0e-8e5c-6694a30657a3 · outbound

This paper cites Beyond data and model parallelism for deep neural networks.Proceedings of Machine Learning and Systems, 1:1–13, 2019.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Beyond data and model parallelism for deep neural networks.Proceedings of Machine Learning and Systems, 1:1–13, 2019

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.356419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a44eb354-53e7-4716-b25b-20049c922c2b · outbound

This paper cites Tpu v4: An optically reconfigurable supercomputer for machine learning with hardware support for embeddings.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Tpu v4: An optically reconfigurable supercomputer for machine learning with hardware support for embeddings

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.359282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f2afaf8-5d0f-49f9-949e-8802fd50788c · outbound

This paper cites In-datacenter performance analysis of a tensor processing unit.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO In-datacenter performance analysis of a tensor processing unit

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.362258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dbdb77e-381e-4d5a-8839-6bf47e3a7540 · outbound

This paper cites Accelwattch: A power modeling framework for modern gpus.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Accelwattch: A power modeling framework for modern gpus

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.365870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6dea6d6-cfd9-47e5-a421-1fedca7475e1 · outbound

This paper cites MIOpen: An Open Source Library For Deep Learning Primitives.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO MIOpen: An Open Source Library For Deep Learning Primitives

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.368856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 071081d0-8493-42ef-a18b-0e99711d9fc9 · outbound

This paper cites Moonwalk: Nre optimization in asic clouds.SIGARCH Comput.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Moonwalk: Nre optimization in asic clouds.SIGARCH Comput

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.372221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73df6e8a-7fa8-4baf-910f-33d09488f446 · outbound

This paper cites LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.375078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91210b0a-f774-4a55-b34f-007207878237 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Adam: A Method for Stochastic Optimization

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.378380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce9ccecf-224d-429f-a0fe-3e658742cba0 · outbound

This paper cites Greenchip: A tool for evaluating holistic sustainability of modern computing systems.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Greenchip: A tool for evaluating holistic sustainability of modern computing systems

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.381307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e3da598-3bf7-4e8a-8b02-f52055e8c674 · outbound

This paper cites A simple model for determining true total cost of ownership for data centers.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO A simple model for determining true total cost of ownership for data centers

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.384400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d124fa95-9dee-40fc-bd50-ce60eb724ade · outbound

This paper cites Reducing activation recomputation in large transformer models.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Reducing activation recomputation in large transformer models

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.974769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 22180df2-748e-4d35-a4b6-11e739f0118c · outbound

This paper cites Performance Modeling and Workload Analysis of Distributed Large Language Model Training and Inference.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Performance Modeling and Workload Analysis of Distributed Large Language Model Training and Inference

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.390487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05e417fa-55ca-4ab1-b93a-0c09c8eb17cb · outbound

This paper cites Quantifying the Carbon Emissions of Machine Learning.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Quantifying the Carbon Emissions of Machine Learning

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.394060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab11d42e-9657-4393-92d2-1509425612b5 · outbound

This paper cites Llvm: A compilation framework for lifelong program analysis & transformation.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Llvm: A compilation framework for lifelong program analysis & transformation

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.965486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4fb36b0f-25af-430a-8770-e254cea950c8 · outbound

This paper cites Mlir: Scaling compiler infrastructure for domain specific computation.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Mlir: Scaling compiler infrastructure for domain specific computation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.956115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ded2ce9d-69bc-4240-ac79-fdaa2cb71152 · outbound

This paper cites Forecasting GPU Performance for Deep Learning Training and Inference.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Forecasting GPU Performance for Deep Learning Training and Inference

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:57:52.957704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4d6c5cd6-af4c-4b67-bbc9-92b69190c56a · outbound

This paper cites Mcpat: An integrated power, area, and timing modeling framework for multicore and manycore architectures.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Mcpat: An integrated power, area, and timing modeling framework for multicore and manycore architectures

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.946559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bc0a946d-801a-4667-8a9c-a7dd012d37f1 · outbound

This paper cites Towards universal performance modeling for machine learning training on multi-gpu platforms.IEEE Transactions on Parallel and Distributed Systems, 2024.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Towards universal performance modeling for machine learning training on multi-gpu platforms.IEEE Transactions on Parallel and Distributed Systems, 2024

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.937144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 25f384dd-3806-494b-bb72-5b1d86181914 · outbound

This paper cites The Llama 3 Herd of Models.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO The Llama 3 Herd of Models

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.413074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 454d5b3b-5a9e-4c67-8102-92490af8c6ce · outbound

This paper cites A survey of performance modeling and simulation techniques for accelerator-based computing.IEEE Transactions on Parallel and Distributed Systems, 26(1):272–281, 2014.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO A survey of performance modeling and simulation techniques for accelerator-based computing.IEEE Transactions on Parallel and Distributed Systems, 26(1):272–281, 2014

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.928476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 92a9f44b-e91c-4e62-8851-337837d78599 · outbound

This paper cites Distsim: A performance model of large-scale hybrid distributed dnn training.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Distsim: A performance model of large-scale hybrid distributed dnn training

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.919207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8ca5de48-b907-4c95-8482-0676e6786b65 · outbound

This paper cites Estimating the carbon footprint of bloom, a 176b parameter language model.Journal of Machine Learning Research, 24(253):1–15, 2023.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Estimating the carbon footprint of bloom, a 176b parameter language model.Journal of Machine Learning Research, 24(253):1–15, 2023

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.422480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ce70d94-22df-4e45-8077-77004c4301ae · outbound

This paper cites Review of asic accelerators for deep neural network.Microprocessors and Microsystems, 89:104441, 2022.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Review of asic accelerators for deep neural network.Microprocessors and Microsystems, 89:104441, 2022

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.904278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 90bcfcb3-e79e-4c0e-a164-0c63b1e6f64d · outbound

This paper cites Azure Total Cost of Ownership (TCO) Calcula- tor.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Azure Total Cost of Ownership (TCO) Calcula- tor

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.894761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fd20dfbf-0bd3-42ae-bf40-14b3316920f7 · outbound

This paper cites Tensor operator set architecture (tosa) dialect.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Tensor operator set architecture (tosa) dialect

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.885822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5fe95af3-1a4a-401f-8bad-08ef7cd95389 · outbound

This paper cites Amped: An analytical model for performance in distributed training of transformers.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Amped: An analytical model for performance in distributed training of transformers

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.876361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 63cf2743-9f62-4a71-a5e9-b92cc0dae7c0 · outbound

This paper cites Efficient large-scale language model training on gpu clusters using megatron-lm.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Efficient large-scale language model training on gpu clusters using megatron-lm

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.438443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aacfa65a-10d0-4669-8e34-66d0fbec3d95 · outbound

This paper cites Supply chain aware computer architecture.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Supply chain aware computer architecture

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.861782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 62cd2ab1-669c-4c24-8ede-49f87980ba00 · outbound

This paper cites Architectural simulators considered harmful.IEEE Micro, 35(6):4–12, 2015.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Architectural simulators considered harmful.IEEE Micro, 35(6):4–12, 2015

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.853278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2ae537e2-1c53-45bd-ae01-e1e487785f02 · outbound

This paper cites Available at https://developer.nvidia.com/nccl.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Available at https://developer.nvidia.com/nccl

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.844648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f42dd465-cb96-4440-8eb9-e210a72bc4bb · outbound

This paper cites Open neural network exchange (onnx).

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Open neural network exchange (onnx)

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.835430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 823d1f6a-0451-4db5-9907-999331e5e05e · outbound

This paper cites Xla architecture and high-level optimizer (hlo).

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Xla architecture and high-level optimizer (hlo)

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.826258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 536ecd84-208b-4335-a258-d340d672a571 · outbound

This paper cites Survey on network simulators.International Journal of Computer Applications, 182(21):23–30, 2018.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Survey on network simulators.International Journal of Computer Applications, 182(21):23–30, 2018

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.817389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d296986e-9feb-4094-9e74-08a3e9645664 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Carbon Emissions and Large Neural Network Training

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.460227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f463f9c-46d5-4b72-8016-0264e718bc60 · outbound

This paper cites Chiplet cloud: Building ai supercomputers for serving large generative language models, 2024.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Chiplet cloud: Building ai supercomputers for serving large generative language models, 2024

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.808681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 972fc42c-d806-448f-b670-27ddb58fbaa6 · outbound

This paper cites Paleo: A performance model for deep neural networks.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Paleo: A performance model for deep neural networks

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:53.799860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 67ccacd2-e5e7-48e9-9b58-50109f9d5f57 · outbound

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

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.469810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 870d4915-8c1a-4829-9dbb-a77af41af4d5 · outbound

This paper cites Zero: Memory optimizations toward training trillion parameter models.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Zero: Memory optimizations toward training trillion parameter models

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.472779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 244a8661-ca23-4a3a-948b-0f62a2ad878a · outbound

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

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:52.475695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a817209-a93d-4a42-9939-53b6a7acb138 · outbound

This paper cites an unresolved cited work.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Unresolved cited work

Reference 101

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:53.775425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers