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..
Coded Sequential Matrix Multiplication For Straggler Mitigation
Authors: Nikhil Krishnan Muralee Krishnan, Seyederfan Hosseini, Ashish Khisti
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | These are further validated by experiments, where we implement our schemes to train neural networks. |
| Researcher Affiliation | Academia | M. Nikhil Krishnan University of Toronto EMAIL Erfan Hosseini University of Toronto EMAIL Ashish Khisti University of Toronto EMAIL |
| Pseudocode | Yes | Algorithm 1: Algorithm used by master to assign mini-tasks in the DIP coding scheme |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of its methodology's code. |
| Open Datasets | Yes | Training is performed for 250 iterations over MNIST dataset with a batch size of 1024 using SGD. |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide details about validation dataset splits. |
| Hardware Specification | Yes | We use four virtual machines with 8GB of RAM and 4 v CPUs as workers and one more machine with 16GB of RAM and 8 v CPUs as the master. |
| Software Dependencies | No | The paper mentions 'mpi4py [19] and Num Py' but does not specify their version numbers. |
| Experiment Setup | Yes | Each NN model is fully connected with two hidden layers of size 3000 followed by a Re LU activation. Training is performed for 250 iterations over MNIST dataset with a batch size of 1024 using SGD. |