# GraphLab: A New Framework For Parallel Machine Learning

arXiv: https://arxiv.org/abs/1408.2041 · HTML: https://mlsanity.com/paper/1408.2041/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2014.

## Built on by (most cited first, 8 of 8)

- [Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads](https://mlsanity.com/paper/2105.11118/) (2021)
- [The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey](https://mlsanity.com/paper/2305.13854/) (2023)
- [GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding](https://mlsanity.com/paper/2110.06196/) (2021)
- [Guided parallelized stochastic gradient descent for delay compensation](https://mlsanity.com/paper/2101.07259/) (2021)
- [Phases, Modalities, Temporal and Spatial Locality: Domain Specific ML Prefetcher for Accelerating Graph Analytics](https://mlsanity.com/paper/2212.05250/) (2022)
- [Unsupervised MKL in Multi-layer Kernel Machines](https://mlsanity.com/paper/2111.13769/) (2021)
- [WawPart: Workload-Aware Partitioning of Knowledge Graphs](https://mlsanity.com/paper/2203.14888/) (2022)
- [gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs](https://mlsanity.com/paper/2210.11601/) (2022)

## Abstract (arXiv)

Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.