Towards optimal placement and scheduling of DNN operations with Pesto-Reference-Cited by-同舟云学术

Towards optimal placement and scheduling of DNN operations with Pesto

Published:2021-10-02 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 22nd International Middleware Conference
language:
Short-container-title:

Author:

Hafeez Ubaid Ullah¹,Sun Xiao¹,Gandhi Anshul¹,Liu Zhenhua¹

Affiliation:

1. Stony Brook University

Funder

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3464298.3476132

Reference61 articles.

1. [n.d.]. Amazon EC2 Instance Types. https://aws.amazon.com/ec2/instance-types. Accessed: 2020-11-28. [n.d.]. Amazon EC2 Instance Types. https://aws.amazon.com/ec2/instance-types. Accessed: 2020-11-28.

2. [n.d.]. NVIDIA V100 TENSOR CORE GPUs. https://www.nvidia.com/en-us/data-center/v100/. Accessed: 2020-11-28. [n.d.]. NVIDIA V100 TENSOR CORE GPUs. https://www.nvidia.com/en-us/data-center/v100/. Accessed: 2020-11-28.

3. [n.d.]. NVLINK FABRIC: A FASTER MORE SCALABLE INTERCONNECT. https://www.nvidia.com/en-us/data-center/nvlink/. [n.d.]. NVLINK FABRIC: A FASTER MORE SCALABLE INTERCONNECT. https://www.nvidia.com/en-us/data-center/nvlink/.

4. [n.d.]. Pesto Source-code. https://github.com/PACELab/TF-Pesto. Accessed: 2020-11-28. [n.d.]. Pesto Source-code. https://github.com/PACELab/TF-Pesto. Accessed: 2020-11-28.

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Metrics for Sustainability in Data Centers;ACM SIGEnergy Energy Informatics Review;2023-10

2. Mercury: Fast and Optimal Device Placement for Large Deep Learning Models;Proceedings of the 52nd International Conference on Parallel Processing;2023-08-07

3. A Survey on Auto-Parallelism of Large-Scale Deep Learning Training;IEEE Transactions on Parallel and Distributed Systems;2023-08

4. Expediting Distributed DNN Training With Device Topology-Aware Graph Deployment;IEEE Transactions on Parallel and Distributed Systems;2023-04

5. MiniMalloc: A Lightweight Memory Allocator for Hardware-Accelerated Machine Learning;Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4;2023-03-25