Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks | Paper Citation Record

Observation 82b32f4b-5527-479e-a682-4de7aeee6919 · outbound

This paper cites deepspeed-2071: [BUG] FP16 used for all reduce even if BFLOAT16 is enabled.https://github.com/deepspeedai/DeepSpeed/ issues/2071.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks deepspeed-2071: [BUG] FP16 used for all reduce even if BFLOAT16 is enabled.https://github.com/deepspeedai/DeepSpeed/ issues/2071

Reference 1

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Observation 1f9a4f5e-7b82-4759-abfa-73315b3759ed · outbound

This paper cites deepspeed-5618: [BUG] ZeRO optimizer with MoE Expert Parallelism.https://github.com/deepspeedai/DeepSpeed/issues/5618.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks deepspeed-5618: [BUG] ZeRO optimizer with MoE Expert Parallelism.https://github.com/deepspeedai/DeepSpeed/issues/5618

Reference 2

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Observation e14b7054-03b3-4e36-bf3d-86c734cbada2 · outbound

This paper cites deepspeed-5808: [BUG] sequence parallel alltoall with batch size > 1.https://github.com/deepspeedai/DeepSpeed/issues/5808.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks deepspeed-5808: [BUG] sequence parallel alltoall with batch size > 1.https://github.com/deepspeedai/DeepSpeed/issues/5808

Reference 3

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Observation 7a78f877-6dad-4cf9-b4b3-efe3c29fb472 · outbound

This paper cites deepspeed-6714: [BUG] pipeline parallelism+fp16+moe isn’t working.https://github.com/deepspeedai/DeepSpeed/issues/6714.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks deepspeed-6714: [BUG] pipeline parallelism+fp16+moe isn’t working.https://github.com/deepspeedai/DeepSpeed/issues/6714

Reference 4

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Observation 5cd92a81-f486-4160-9125-a9b9423ef893 · outbound

This paper cites deepspeed-7188: [BUG]when use overlap_comm:True with contiguous_gradients:True, grad_norm is NaN.https://github.com/ deepspeedai/DeepSpeed/issues/7188.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks deepspeed-7188: [BUG]when use overlap_comm:True with contiguous_gradients:True, grad_norm is NaN.https://github.com/ deepspeedai/DeepSpeed/issues/7188

Reference 5

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Observation f9d9c130-24de-475a-9694-fc520943d05c · outbound

This paper cites megatronlm-1611: [BUG] MLA is computed in a different ordercomparedtotheogimpl.https://github.com/NVIDIA/Megatron- LM/issues/1611.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks megatronlm-1611: [BUG] MLA is computed in a different ordercomparedtotheogimpl.https://github.com/NVIDIA/Megatron- LM/issues/1611

Reference 6

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Observation 75a3b4c7-8efa-458b-a1f9-a2f23fb08d7c · outbound

This paper cites megatronlm-1620: [BUG] Problem with calculation of self.q_layernorm in qkv_up_proj_and_rope_apply.https://github.com/ NVIDIA/Megatron-LM/issues/1620.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks megatronlm-1620: [BUG] Problem with calculation of self.q_layernorm in qkv_up_proj_and_rope_apply.https://github.com/ NVIDIA/Megatron-LM/issues/1620

Reference 7

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Observation b7b1bf75-c7a0-4b9c-939d-35d39c846c92 · outbound

This paper cites megatronlm-1699: [BUG] [MLA] k_pos_emb in MLA is not re- ducedacrossTPranksiflinear_kv_down_projisColumnParallelLinear.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks megatronlm-1699: [BUG] [MLA] k_pos_emb in MLA is not re- ducedacrossTPranksiflinear_kv_down_projisColumnParallelLinear

Reference 8

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Observation 238f837c-6307-404b-bd2a-e65a7533229b · outbound

This paper cites an unresolved cited work.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 9

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Observation 1ff74675-6e16-4b68-98e5-3ad6ad24a24f · outbound

This paper cites megatronlm-599: [BUG] SwitchMLP router weights are not synchronized within the tensor parallelism group.https://github.com/ NVIDIA/Megatron-LM/issues/599.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks megatronlm-599: [BUG] SwitchMLP router weights are not synchronized within the tensor parallelism group.https://github.com/ NVIDIA/Megatron-LM/issues/599

Reference 10

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Observation 00309e20-cd56-41c2-9496-4a4fc99d7aba · outbound

This paper cites megatronlm-5fffdfc: LinearWithFrozenWeight backward fix when TP > 1.https://github.com/NVIDIA/Megatron-LM/commit/ 5fffdfc737f14297bc3781dfc9e273199d1df52e.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks megatronlm-5fffdfc: LinearWithFrozenWeight backward fix when TP > 1.https://github.com/NVIDIA/Megatron-LM/commit/ 5fffdfc737f14297bc3781dfc9e273199d1df52e

Reference 11

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Observation 22fd3966-7a34-4a2b-b39a-aeb126466abb · outbound

This paper cites nemo-5564: Fix all gather while writing to a file during T5 finetuning.https://github.com/NVIDIA/NeMo/pull/5564.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks nemo-5564: Fix all gather while writing to a file during T5 finetuning.https://github.com/NVIDIA/NeMo/pull/5564

Reference 12

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Observation b88c8e59-76fb-4e10-9655-bc89e61de7e9 · outbound

This paper cites nemo-8487: LoRA training with FSDP has spike in train loss.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks nemo-8487: LoRA training with FSDP has spike in train loss

Reference 13

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Observation c947f74a-d8fd-4315-826c-6d9625604b15 · outbound

This paper cites nemo-9344:Skipsequence_parallelallreducewhenusingMcore DistOpt.https://github.com/NVIDIA/NeMo/pull/9344.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks nemo-9344:Skipsequence_parallelallreducewhenusingMcore DistOpt.https://github.com/NVIDIA/NeMo/pull/9344

Reference 14

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Observation 9414ee20-8800-4562-b3d1-fc793ba01478 · outbound

This paper cites transformerengine-3: Add checks for tensor parallel use case to ensure all-reduce is called only when necessary.https://github.com/ NVIDIA/TransformerEngine/pull/3.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks transformerengine-3: Add checks for tensor parallel use case to ensure all-reduce is called only when necessary.https://github.com/ NVIDIA/TransformerEngine/pull/3

Reference 15

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Observation ce49a227-be4d-45c8-831c-383cb6804f14 · outbound

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 16

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Observation 329a2d01-7fc6-4509-97b5-5300cd08fc33 · outbound

This paper cites transformersneuronx-0c646b0 : [GPT2]Fix layout changes.https://github.com/aws-neuron/transformers-neuronx/ commit/0c646b02ad36a82bcc13633523de09eb6ed92072.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks transformersneuronx-0c646b0 : [GPT2]Fix layout changes.https://github.com/aws-neuron/transformers-neuronx/ commit/0c646b02ad36a82bcc13633523de09eb6ed92072

Reference 17

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Observation 5306ae0d-e37a-4b34-9349-d727937fea38 · outbound

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 18

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Observation f2ed9a0e-c84c-4b2d-9ae5-7bba7f36ccdf · outbound

This paper cites transformersneuronx-e2f5241:slicethekvcachecorrectlywhen using sos.https://github.com/aws-neuron/transformers-neuronx/ commit/e2f524114f9536541fb886f5509ff7cda2a36ce5.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks transformersneuronx-e2f5241:slicethekvcachecorrectlywhen using sos.https://github.com/aws-neuron/transformers-neuronx/ commit/e2f524114f9536541fb886f5509ff7cda2a36ce5

Reference 19

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Observation d982ca07-c131-4ff4-8294-24b907023960 · outbound

This paper cites Manual Optimization does not synchronize gradients in DDP.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Manual Optimization does not synchronize gradients in DDP

Reference 20

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Observation 22626630-0b2b-4603-97b1-da6eed6c48fb · outbound

This paper cites [Bug] Gradients not synchronized.https://github.com/kohya- ss/sd-scripts/issues/924.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks [Bug] Gradients not synchronized.https://github.com/kohya- ss/sd-scripts/issues/924

Reference 21

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Observation 717704af-4450-4c3b-aee5-6c52cb871766 · outbound

This paper cites DDP: moving model to CPU and back to GPU breaks gradient synchronization.https://github.com/pytorch/pytorch/issues/104336.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks DDP: moving model to CPU and back to GPU breaks gradient synchronization.https://github.com/pytorch/pytorch/issues/104336

Reference 22

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Observation 809bbbf4-1921-4b10-9ebd-e573611eeefa · outbound

This paper cites CUDA Toolkit.https://developer.nvidia.com/cuda- toolkit.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks CUDA Toolkit.https://developer.nvidia.com/cuda- toolkit

Reference 23

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Observation 40fbb23d-9a9b-4528-be89-0ccc375ea9b1 · outbound

This paper cites NeuronX Distributed (NxD).https://awsdocs- neuron.readthedocs-hosted.com/en/latest/libraries/neuronx- distributed/index.html.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks NeuronX Distributed (NxD).https://awsdocs- neuron.readthedocs-hosted.com/en/latest/libraries/neuronx- distributed/index.html

Reference 24

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Observation 34262b4a-f050-45d4-9ac3-7a7f9c3486d5 · outbound

This paper cites PyTorch.https://pytorch.org/.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks PyTorch.https://pytorch.org/

Reference 25

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Observation 1c8108f7-d2eb-4d11-b3f6-b5fdc9e950ac · outbound

This paper cites TensorFlow: An end-to-end platform for machine learning.https://www.tensorflow.org/.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks TensorFlow: An end-to-end platform for machine learning.https://www.tensorflow.org/

Reference 26

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Observation 5c6e4137-3075-4cdf-aa7e-eb1994878a22 · outbound

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Reference 27

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Observation 0106bb95-78d3-447a-a10e-95f198a94e0f · outbound

This paper cites XLA (Accelerated Linear Algebra).https://openxla.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks XLA (Accelerated Linear Algebra).https://openxla

Reference 28

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Observation 2c1b7657-3302-465c-aecc-a64b96a4a245 · outbound

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 29

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 30

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 31

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 32

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 33

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Reference 34

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Observation 6efe0eb3-7f93-45c0-9977-5dbce17bff93 · outbound

This paper cites Lim, and Santosh Nagarakatte.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Lim, and Santosh Nagarakatte

Reference 35

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 36

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 37

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Observation 3c7804fa-e0ce-451e-97fa-d7a2722bfa12 · outbound

This paper cites On Neural Network Equivalence Checking using SMT Solvers.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks On Neural Network Equivalence Checking using SMT Solvers

Reference 38

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Observation b11dbd31-baaa-4f89-a3d1-dec2a6347dc4 · outbound

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 39

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 40

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Observation 12aa93b7-cabb-4980-b37d-cebf191ceefa · outbound

This paper cites Gunawi, Mingzhe Hao, Riza O.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Gunawi, Mingzhe Hao, Riza O

Reference 41

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Observation ee22e970-c1a2-420e-9a12-b26ced843dfc · outbound

This paper cites InProceedings of the 45th International Conference on Software Engineering(Melbourne, Victoria, Australia) (ICSE ’23).

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks InProceedings of the 45th International Conference on Software Engineering(Melbourne, Victoria, Australia) (ICSE ’23)

Reference 42

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 43

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 44

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Observation 6ed21138-85b2-45e2-8118-6ba0fe0d19c9 · outbound

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 45

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Observation 88bca262-0eca-4513-8ac1-ad7d35c176ab · outbound

This paper cites Pollard, Nadesh Ramanathan, and John Wickerson.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Pollard, Nadesh Ramanathan, and John Wickerson

Reference 46

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 47

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Observation a40876f2-17ca-4d94-8353-47d5cf638705 · outbound

This paper cites GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Reference 48

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Observation 608b3070-7c5e-4781-a735-3e8f9ffe69e1 · outbound

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 49

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Observation 667935bb-bfb9-40e9-a787-2a8b566a054b · outbound

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

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Reference 51

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks TrainingwithConfidence:CatchingSilentErrorsinDeep Learning Training with Automated Proactive Checks

Reference 52

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Reference 53

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Transformersinvision: A survey.ACM computing surveys (CSUR)54, 10s (2022), 1–41

Reference 54

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 55

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks InProceedings of the 19th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’25)

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This paper cites TrainVerify: Equivalence-Based Verification for Distributed LLM Training.

Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks TrainVerify: Equivalence-Based Verification for Distributed LLM Training

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Reference 59

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Reference 60

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Reference 66

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 70

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow

Reference 71

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks DeepTest: automated testing of deep-neural-network-driven autonomous cars

Reference 75

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Equality saturation: a new approach to optimization(POPL ’09)

Reference 76

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Reference 77

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 78

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Reference 79

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Reference 80

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks In16th USENIX Symposium on Operating Systems Design and Implemen- tation (OSDI 22)

Reference 81

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Reference 82

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 83

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Reference 84

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Reference 85

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 86

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Alpa:Automat- ing Inter- and Intra-Operator Parallelism for Distributed Deep Learn- ing

Reference 87

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Observation acdad7b1-1313-4825-8a51-92f93bcebb50 · outbound

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Reference 88

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Mirage: A Multi-Level Superoptimizer for Tensor Programs

Reference 90

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Reference 91

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Observation 5254f86c-a014-4780-8894-37ffecdff82a · outbound

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks Unresolved cited work

Reference 92

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks ACM Program

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Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks InProceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles(Austin, TX, USA)(SOSP ’24)

Reference 2024

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Outbound references

Pith citing papers