Straggler Mitigation in Distributed Optimization Through Data Encoding
Authors: Can Karakus, Yifan Sun, Suhas Diggavi, Wotao Yin
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide experimental results demonstrating the advantage of the approach over uncoded and data replication strategies. |
| Researcher Affiliation | Collaboration | Can Karakus UCLA Los Angeles, CA karakus@ucla.edu Yifan Sun Technicolor Research Los Altos, CA Yifan.Sun@technicolor.com Suhas Diggavi UCLA Los Angeles, CA suhasdiggavi@ucla.edu Wotao Yin UCLA Los Angeles, CA wotaoyin@math.ucla.edu |
| Pseudocode | No | The paper describes algorithms textually but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper refers to an arXiv preprint but does not provide an explicit statement or link to the source code for the described methodology. |
| Open Datasets | Yes | Matrix factorization on Movielens 1-M dataset [18] for the movie recommendation task. |
| Dataset Splits | Yes | We withhold randomly 20% of these ratings to form an 80/20 train/test split. |
| Hardware Specification | Yes | We implement distributed L-BFGS as described in Section 3 on an Amazon EC2 cluster using the mpi4py Python package, over m = 32 m1.small worker node instances, and a single c3.8xlarge central server instance. The Movielens experiment is run on a single 32-core machine with 256 GB RAM. |
| Software Dependencies | No | The paper mentions 'mpi4py Python package' and 'using the built-in function numpy.linalg.solve' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | for regularization parameter λ = 0.05. We evaluate column-subsampled Hadamard matrix with redundancy β = 2 (encoded using FWHT for fast encoding)... which are aggregated over 20 trials. We choose µ = 3, p = 15, and λ = 10... |