A Study of Pipeline Parallelism in Deep Neural Networks

The current popularity in the application of artificial intelligence to solve complex problems is growing. The appearance of chats based on artificial intelligence or natural language processing has generated the creation of increasingly large and sophisticated neural network models, which are the b...

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Autores:
Núñez, Gabriel
Romero Sandí, Hairol
Rojas, Elvis
Meneses, Esteban
Tipo de recurso:
Article of investigation
Fecha de publicación:
2024
Institución:
Universidad Autónoma de Bucaramanga - UNAB
Repositorio:
Repositorio UNAB
Idioma:
spa
OAI Identifier:
oai:repository.unab.edu.co:20.500.12749/26659
Acceso en línea:
http://hdl.handle.net/20.500.12749/26659
https://doi.org/10.29375/25392115.5056
Palabra clave:
Deep learning
Parallelism
Artificial neural networks
Distributed training
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License
http://purl.org/coar/access_right/c_abf2
id UNAB2_ee356aff759402c605137b5910bcc2b2
oai_identifier_str oai:repository.unab.edu.co:20.500.12749/26659
network_acronym_str UNAB2
network_name_str Repositorio UNAB
repository_id_str
dc.title.eng.fl_str_mv A Study of Pipeline Parallelism in Deep Neural Networks
title A Study of Pipeline Parallelism in Deep Neural Networks
spellingShingle A Study of Pipeline Parallelism in Deep Neural Networks
Deep learning
Parallelism
Artificial neural networks
Distributed training
title_short A Study of Pipeline Parallelism in Deep Neural Networks
title_full A Study of Pipeline Parallelism in Deep Neural Networks
title_fullStr A Study of Pipeline Parallelism in Deep Neural Networks
title_full_unstemmed A Study of Pipeline Parallelism in Deep Neural Networks
title_sort A Study of Pipeline Parallelism in Deep Neural Networks
dc.creator.fl_str_mv Núñez, Gabriel
Romero Sandí, Hairol
Rojas, Elvis
Meneses, Esteban
dc.contributor.author.none.fl_str_mv Núñez, Gabriel
Romero Sandí, Hairol
Rojas, Elvis
Meneses, Esteban
dc.contributor.orcid.spa.fl_str_mv Núñez, Gabriel [0000-0002-6907-533X]
Romero Sandí, Hairol [0000-0002-3199-1244]
Rojas, Elvis [0000-0002-4238-0908]
Meneses, Esteban [0000-0002-4307-6000]
dc.subject.keywords.eng.fl_str_mv Deep learning
Parallelism
Artificial neural networks
Distributed training
topic Deep learning
Parallelism
Artificial neural networks
Distributed training
description The current popularity in the application of artificial intelligence to solve complex problems is growing. The appearance of chats based on artificial intelligence or natural language processing has generated the creation of increasingly large and sophisticated neural network models, which are the basis of current developments in artificial intelligence. These neural networks can be composed of billions of parameters and their training is not feasible without the application of approaches based on parallelism. This paper focuses on studying pipeline parallelism, which is one of the most important types of parallelism used to train neural network models in deep learning. In this study we offer a look at the most important concepts related to the topic and we present a detailed analysis of 3 pipeline parallelism libraries: Torchgpipe, FairScale, and DeepSpeed. We analyze important aspects of these libraries such as their implementation and features. In addition, we evaluated them experimentally, carrying out parallel trainings and taking into account aspects such as the number of stages in the training pipeline and the type of balance.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-09-19T21:46:23Z
dc.date.available.none.fl_str_mv 2024-09-19T21:46:23Z
dc.date.issued.none.fl_str_mv 2024-06-18
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.type.local.spa.fl_str_mv Artículo
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dc.type.redcol.none.fl_str_mv http://purl.org/redcol/resource_type/ART
format http://purl.org/coar/resource_type/c_2df8fbb1
dc.identifier.issn.spa.fl_str_mv ISSN: 1657-2831
e-ISSN: 2539-2115
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12749/26659
dc.identifier.instname.spa.fl_str_mv instname:Universidad Autónoma de Bucaramanga UNAB
dc.identifier.repourl.spa.fl_str_mv repourl:https://repository.unab.edu.co
dc.identifier.doi.none.fl_str_mv https://doi.org/10.29375/25392115.5056
identifier_str_mv ISSN: 1657-2831
e-ISSN: 2539-2115
instname:Universidad Autónoma de Bucaramanga UNAB
repourl:https://repository.unab.edu.co
url http://hdl.handle.net/20.500.12749/26659
https://doi.org/10.29375/25392115.5056
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv https://revistas.unab.edu.co/index.php/rcc/article/view/5056/3969
dc.relation.uri.spa.fl_str_mv https://revistas.unab.edu.co/index.php/rcc/issue/view/297
dc.relation.references.none.fl_str_mv Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., . . . Zheng, X. (2016). TensorFlow: A System for Large-Scale Machine Learning. e Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16). November 2–4 (pp. 264-283). Savannah, GA, USA: USENIX Association. https://doi.org/10.48550/arXiv.1605.08695
Akintoye, S., Han, L., Zhang, X., Chen, H., & Zhang, D. (2022). A Hybrid Parallelization Approach for Distributed and Scalable Deep Learning. IEEE Access, 10, 77950-77961. https://doi.org/10.1109/ACCESS.2022.3193690
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Aminabadi, R. Y., Rajbhandari, S., Awan, A. A., Li, C., Li, D., Zheng, E., . . . He, Y. (2022). DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale. SC22: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1-15). Dallas, TX, USA: IEEE. https://doi.org/10.1109/SC41404.2022.00051
Chatelain, A., Djeghri, A., Hesslow, D., & Launay, J. (2022). Is the Number of Trainable Parameters All That Actually Matters? In M. F. Pradier, A. Schein, S. Hyland, F. J. Ruiz, & J. Z. Forde (Ed.), Proceedings on "I (Still) Can't Believe It's Not Better!" at NeurIPS 2021 Workshops. 163, pp. 27-32. PMLR. https://proceedings.mlr.press/v163/chatelain22a.html
Chen, M. (2023). Analysis of Data Parallelism Methods with Deep Neural Network. EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering, October 21 - 23 (pp. 1857 - 1861). Xiamen, China: Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3573428.3573755
Chen, Z., Xu, C., Qian, W., & Zhou, A. (2023). Elastic Averaging for Efficient Pipelined DNN Training. Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP´23 (pp. 380-391). Montreal, QC, Canada: Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3572848.3577484
Chilimbi, T., Suzue, Y., Apacible, J., & Kalyanaraman, K. (2014). Project Adam: Building an Efficient and Scalable Deep Learning Training System. Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI´14). October 6–8 (pp. 570-582). Broomfield, CO: USENIX Association. https://www.usenix.org/system/files/conference/osdi14/osdi14-paper-chilimbi.pdf
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Le, Q. V., . . . Ng, A. Y. (2012). Large Scale Distributed Deep Networks. In F. Pereira, C. J. Burges, L. Bottou, & K. Q. Weinberger (Ed.), Advances in Neural Information Processing Systems (NIPS 2012). 25, pp. 1223-1231. Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2012/file/6aca97005c68f1206823815f66102863-Paper.pdf
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Fan, S., Rong, Y., Meng, C., Cao, Z., Wang, S., Zheng, Z., . . . Lin, W. (2021). DAPPLE: a pipelined data parallel approach for training large models. Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 431-445). Virtual Event, Republic of Korea: Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3437801.3441593
Farkas, A., Kertész, G., & Lovas, R. (2020). Parallel and Distributed Training of Deep Neural Networks: A brief overview. 2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) (pp. 165-170). Reykjavík, Iceland: IEEE. https://doi.org/10.1109/INES49302.2020.9147123
Guan, L., Yin, W., Li, D., & Lu, X. (2020, November 9). XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training. arXiv:1911.04610v3 [cs.LG]. https://doi.org/10.48550/arXiv.1911.04610
Harlap, A., Narayanan, D., Phanishayee, A., Seshadri, V., Devanur, N., Ganger, G., & Gibbons, P. (2018, June 18). PipeDream: Fast and Efficient Pipeline Parallel DNN Training. arXiv:1806.03377v1 [cs.DC]. https://doi.org/10.48550/arXiv.1806.03377
Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, M. X., Chen, D., . . . Chen, Z. (2019, July 25). GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. arXiv:1811.06965v5 [cs.CV], 1-11. https://doi.org/10.48550/arXiv.1811.06965
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., . . . Darrell, T. (2014, June 20). Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv:1408.5093v1 [cs.CV], 1-4.
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Krizhevsky, A. (2014, April 26). One weird trick for parallelizing convolutional neural networks. arXiv:1404.5997v2 [cs.NE], 1-7. https://doi.org/10.48550/arXiv.1404.5997
Li, S., & Hoefler, T. (2021). Chimera: efficiently training large-scale neural networks with bidirectional pipelines. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. Article No. 27, pp. 1-14. St. Louis, Missouri, USA: Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3458817.3476145
Liang, G., & Alsmadi, I. (2022, February 12). Benchmark Assessment for DeepSpeed Optimization Library. arXiv:2202.12831v1 [cs.LG], 1-8. https://doi.org/10.48550/arXiv.2202.12831
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spelling Núñez, Gabriela45e771e-ff8c-4a47-8f30-619fc2bfb589Romero Sandí, Hairol41889347-9c69-46f3-a39e-cac8fb987a7cRojas, Elvisf3118ba3-c006-4846-ba9d-c1a41452acadMeneses, Esteban4a20e5ac-8885-4884-8394-47d2c557f95fNúñez, Gabriel [0000-0002-6907-533X]Romero Sandí, Hairol [0000-0002-3199-1244]Rojas, Elvis [0000-0002-4238-0908]Meneses, Esteban [0000-0002-4307-6000]2024-09-19T21:46:23Z2024-09-19T21:46:23Z2024-06-18ISSN: 1657-2831e-ISSN: 2539-2115http://hdl.handle.net/20.500.12749/26659instname:Universidad Autónoma de Bucaramanga UNABrepourl:https://repository.unab.edu.cohttps://doi.org/10.29375/25392115.5056application/pdfspaUniversidad Autónoma de Bucaramanga UNABhttps://revistas.unab.edu.co/index.php/rcc/article/view/5056/3969https://revistas.unab.edu.co/index.php/rcc/issue/view/297Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., . . . Zheng, X. (2016). TensorFlow: A System for Large-Scale Machine Learning. e Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16). November 2–4 (pp. 264-283). Savannah, GA, USA: USENIX Association. https://doi.org/10.48550/arXiv.1605.08695Akintoye, S., Han, L., Zhang, X., Chen, H., & Zhang, D. (2022). A Hybrid Parallelization Approach for Distributed and Scalable Deep Learning. IEEE Access, 10, 77950-77961. https://doi.org/10.1109/ACCESS.2022.3193690Alshamrani, R., & Ma, X. (2022). Deep Learning. In C. L. McNeely, & L. A. Schintler (Eds.), Encyclopedia of Big Data (pp. 373-377). Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-32010-6_5Aminabadi, R. Y., Rajbhandari, S., Awan, A. A., Li, C., Li, D., Zheng, E., . . . He, Y. (2022). DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale. SC22: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1-15). Dallas, TX, USA: IEEE. https://doi.org/10.1109/SC41404.2022.00051Chatelain, A., Djeghri, A., Hesslow, D., & Launay, J. (2022). Is the Number of Trainable Parameters All That Actually Matters? In M. F. Pradier, A. Schein, S. Hyland, F. J. Ruiz, & J. Z. Forde (Ed.), Proceedings on "I (Still) Can't Believe It's Not Better!" at NeurIPS 2021 Workshops. 163, pp. 27-32. PMLR. https://proceedings.mlr.press/v163/chatelain22a.htmlChen, M. (2023). Analysis of Data Parallelism Methods with Deep Neural Network. EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering, October 21 - 23 (pp. 1857 - 1861). Xiamen, China: Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3573428.3573755Chen, Z., Xu, C., Qian, W., & Zhou, A. (2023). Elastic Averaging for Efficient Pipelined DNN Training. Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP´23 (pp. 380-391). Montreal, QC, Canada: Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3572848.3577484Chilimbi, T., Suzue, Y., Apacible, J., & Kalyanaraman, K. (2014). Project Adam: Building an Efficient and Scalable Deep Learning Training System. Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI´14). October 6–8 (pp. 570-582). Broomfield, CO: USENIX Association. https://www.usenix.org/system/files/conference/osdi14/osdi14-paper-chilimbi.pdfDean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Le, Q. V., . . . Ng, A. Y. (2012). Large Scale Distributed Deep Networks. In F. Pereira, C. J. Burges, L. Bottou, & K. Q. Weinberger (Ed.), Advances in Neural Information Processing Systems (NIPS 2012). 25, pp. 1223-1231. Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2012/file/6aca97005c68f1206823815f66102863-Paper.pdfDeep Learning. (2020). In A. Tatnall (Ed.), Encyclopedia of Education and Information Technologies (First ed., p. 558). Springer Cham. https://doi.org/10.1007/978-3-030-10576-1_300164Deeplearning4j: Deeplearning4j Suite Overview. (2023, July). https://www.deepspeed.ai/DeepSpeed authors: Deepspeed (overview and features). (2023, July). (Microsoft) https://www.deepspeed.ai/FairScale authors. (2021). Fairscale: A general purpose modular pytorch library for high performance and large scale training. https://github.com/facebookresearch/fairscaleFan, S., Rong, Y., Meng, C., Cao, Z., Wang, S., Zheng, Z., . . . Lin, W. (2021). DAPPLE: a pipelined data parallel approach for training large models. Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 431-445). Virtual Event, Republic of Korea: Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3437801.3441593Farkas, A., Kertész, G., & Lovas, R. (2020). Parallel and Distributed Training of Deep Neural Networks: A brief overview. 2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) (pp. 165-170). Reykjavík, Iceland: IEEE. https://doi.org/10.1109/INES49302.2020.9147123Guan, L., Yin, W., Li, D., & Lu, X. (2020, November 9). XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training. arXiv:1911.04610v3 [cs.LG]. https://doi.org/10.48550/arXiv.1911.04610Harlap, A., Narayanan, D., Phanishayee, A., Seshadri, V., Devanur, N., Ganger, G., & Gibbons, P. (2018, June 18). PipeDream: Fast and Efficient Pipeline Parallel DNN Training. arXiv:1806.03377v1 [cs.DC]. https://doi.org/10.48550/arXiv.1806.03377Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, M. X., Chen, D., . . . Chen, Z. (2019, July 25). GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. arXiv:1811.06965v5 [cs.CV], 1-11. https://doi.org/10.48550/arXiv.1811.06965Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., . . . Darrell, T. (2014, June 20). Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv:1408.5093v1 [cs.CV], 1-4.Keras: Keras api references. (2023, July). https://keras.io/api/Kim, C., Lee, H., Jeong, M., Baek, W., Yoon, B., Kim, I., . . . Kim, S. (2020, April 21). torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models. arXiv:2004.09910v1 [cs.DC], 1-10. https://doi.org/10.48550/arXiv.2004.09910Krizhevsky, A. (2014, April 26). One weird trick for parallelizing convolutional neural networks. arXiv:1404.5997v2 [cs.NE], 1-7. https://doi.org/10.48550/arXiv.1404.5997Li, S., & Hoefler, T. (2021). Chimera: efficiently training large-scale neural networks with bidirectional pipelines. 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Future Generation Computer Systems, 147, 107-118. https://doi.org/10.1016/j.future.2023.04.033Vol. 25 Núm. 1 (2024): Revista Colombiana de Computación (Enero-Junio); 48-59A Study of Pipeline Parallelism in Deep Neural Networksinfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Deep learningParallelismArtificial neural networksDistributed trainingThe current popularity in the application of artificial intelligence to solve complex problems is growing. The appearance of chats based on artificial intelligence or natural language processing has generated the creation of increasingly large and sophisticated neural network models, which are the basis of current developments in artificial intelligence. These neural networks can be composed of billions of parameters and their training is not feasible without the application of approaches based on parallelism. This paper focuses on studying pipeline parallelism, which is one of the most important types of parallelism used to train neural network models in deep learning. In this study we offer a look at the most important concepts related to the topic and we present a detailed analysis of 3 pipeline parallelism libraries: Torchgpipe, FairScale, and DeepSpeed. We analyze important aspects of these libraries such as their implementation and features. In addition, we evaluated them experimentally, carrying out parallel trainings and taking into account aspects such as the number of stages in the training pipeline and the type of balance.http://purl.org/coar/access_right/c_abf2ORIGINALArtículo.pdfArtículo.pdfArtículoapplication/pdf708357https://repository.unab.edu.co/bitstream/20.500.12749/26659/1/Art%c3%adculo.pdf311600f7d85e89b47f78a563a27ac609MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8347https://repository.unab.edu.co/bitstream/20.500.12749/26659/2/license.txt855f7d18ea80f5df821f7004dff2f316MD52open accessTHUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg9841https://repository.unab.edu.co/bitstream/20.500.12749/26659/3/Art%c3%adculo.pdf.jpg7a4fd4d21dc3293f1bab2cffedaeaefeMD53open access20.500.12749/26659oai:repository.unab.edu.co:20.500.12749/266592024-09-19 22:01:17.572open accessRepositorio Institucional | Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.coTGEgUmV2aXN0YSBDb2xvbWJpYW5hIGRlIENvbXB1dGFjacOzbiBlcyBmaW5hbmNpYWRhIHBvciBsYSBVbml2ZXJzaWRhZCBBdXTDs25vbWEgZGUgQnVjYXJhbWFuZ2EuIEVzdGEgUmV2aXN0YSBubyBjb2JyYSB0YXNhIGRlIHN1bWlzacOzbiB5IHB1YmxpY2FjacOzbiBkZSBhcnTDrWN1bG9zLiBQcm92ZWUgYWNjZXNvIGxpYnJlIGlubWVkaWF0byBhIHN1IGNvbnRlbmlkbyBiYWpvIGVsIHByaW5jaXBpbyBkZSBxdWUgaGFjZXIgZGlzcG9uaWJsZSBncmF0dWl0YW1lbnRlIGludmVzdGlnYWNpw7NuIGFsIHDDumJsaWNvIGFwb3lhIGEgdW4gbWF5b3IgaW50ZXJjYW1iaW8gZGUgY29ub2NpbWllbnRvIGdsb2JhbC4=

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