1. GraphLab A New Parallel Framework for Machine Learning Carnegie Mellon Based on Slides by Joseph Gonzalez Mosharaf Chowdhury

2. Belief Propagation SVM Kernel Methods Deep Belief Networks Neural Networks Tensor Factorization PageRank Lasso The Need for a New Abstraction 2 Data-Parallel Graph-Parallel Cross Validation Feature Extraction Map Reduce Computing Sufficient Statistics Pregel (Giraph)

3. GraphLab wants to support 1.Sparse Computational Dependencies 2.Asynchronous Iterative Computation 3.Sequential Consistency 4.Prioritized Ordering 5.Rapid Development

4. The GraphLab Framework Scheduler Consistency Model Graph Based Data Representation Update Functions User Computation 4

5. Data Graph 5 A graph with arbitrary data (C++ Objects) associated with each vertex and edge. Vertex Data: User profile text Current interests estimates Edge Data: Similarity weights Graph: Social Network

6. label_prop(i, scope){ // Get Neighborhood data (Likes[i], W ij, Likes[j])  scope; // Update the vertex data // Reschedule Neighbors if needed if Likes[i] changes then reschedule_neighbors_of(i); } Update Functions 6 An update function is a user defined program which when applied to a vertex transforms the data in the scope of the vertex

7. The Scheduler 7 CPU 1 CPU 2 The scheduler determines the order that vertices are updated. e e f f g g k k j j i i h h d d c c b b a a b b i i h h a a i i b b e e f f j j c c Scheduler The process repeats until the scheduler is empty.

8. Sequential Consistency Models – Full Consistency – Edge Consistency Write Canonical Lock Ordering ReadWrite Read Write

9. Consistency Through Scheduling Edge Consistency Model: – Two vertices can be Updated simultaneously if they do not share an edge. Graph Coloring: – Two vertices can be assigned the same color if they do not share an edge. Barrier Phase 1 Barrier Phase 2 Barrier Phase 3

10. Algorithms Implemented PageRank Loopy Belief Propagation Gibbs Sampling CoEM Graphical Model Parameter Learning Probabilistic Matrix/Tensor Factorization Alternating Least Squares Lasso with Sparse Features Support Vector Machines with Sparse Features Label-Propagation …

12. The Table