Exponentially increasing dataset sizes have driven Machine Learning experts to explore using parallel and distributed computing for their research. Furthermore, cloud computing resources such as Amazon EC2 have become increasingly available, providing cheap and scalable platforms for large scale computation. However, due to the complexities involved in distributed design, it can be difficult for ML researchers to take full advantage of cloud resources. 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 compactly expresses asynchronous iterative algorithms with sparse computational dependencies common in ML, 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 for a variety of ML tasks, including learning graphical models with approximate inference, Gibbs sampling, tensor factorization, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large-scale real-world problems and demonstrate their scalability on Amazon EC2, using up to 256 processors.