Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Structure-Aware Framework for Learning Device Placements on Computation Graphs
Authors: Shukai Duan, Heng Ping, Nikos Kanakaris, Xiongye Xiao, Panagiotis Kyriakis, Nesreen K. Ahmed, Peiyu Zhang, Guixiang Ma, Mihai Capotă, Shahin Nazarian, Theodore Willke, Paul Bogdan
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate our approach we use the computation graphs created from three popular benchmarks: (1) Inception-V3, Res Net, and BERT. The effectiveness and robustness of the proposed approach are demonstrated through multiple experiments with different benchmark models and a detailed ablation study. |
| Researcher Affiliation | Collaboration | Shukai Duan Center for Complex Particle Systems University of Southern California Los Angeles, USA EMAIL; Panagiotis Kyriakis Meta EMAIL; Nesreen K. Ahmed Cisco Outshift EMAIL; Guixiang Ma Intel Labs EMAIL; Mihai Capot a Intel Labs EMAIL; Theodore L. Willke Intel Labs EMAIL |
| Pseudocode | Yes | Algorithm 1 Hierarchical Structure-Aware Device Assignment Graph (HSDAG); Algorithm 2 Graph Parsing Network |
| Open Source Code | Yes | A Code availability The source code is available at https://github.com/hping666/HSDAG. |
| Open Datasets | Yes | To evaluate our approach we use the computation graphs created from three popular benchmarks: (1) Inception-V3: The Inception-V3 architecture [25] is extensively employed for image recognition and visual feature extraction [12]. (2) Res Net: Res Net [10] is a widely-used model for image classification. (3) BERT: BERT [6] is a language model relying on the transformer architecture. |
| Dataset Splits | No | The paper focuses on optimizing device placement using reinforcement learning and does not specify traditional training/validation/test dataset splits for model training. |
| Hardware Specification | Yes | Devices. The available devices for our experiments are the following: (1) CPU: 12th Gen Intel(R) Core(TM) i9-12900K, (2) GPU.0: Intel(R) UHD Graphics 770 (i GPU) and (3) GPU.1: Intel(R) Data Center GPU Flex 170 (d GPU). Our server has 64GB of memory. |
| Software Dependencies | Yes | We run our experiments on real hardware using the Open VINO toolkit version 2023.3.0 |
| Experiment Setup | Yes | Table 6: Model Parameters provides specific values for num_devices, hidden_channel, layer_trans, layer_gnn, layer_parsingnet, gnn_model, dropout_network, dropout_parsing, link_ignore_self_loop, act_final, learning_rate, max_episodes, update_timestep, and K_epochs. |