Wednesday, April 13, 2016

Paper review. GraphLab: A new Framework for Parallel Machine Learning

This GraphLab paper is from 2010. It is written elegantly and it does a good job of explaining the GraphLab abstraction and foundations. A later VLDB 2012 paper presents how to extend this basic GraphLab abstraction to a distributed GraphLab implementation.

It seems like GraphLab has since took off big time. There are workshops and conferences about GraphLab. And GraphLab is now developed by the company Dato (formerly GraphLab inc.). Dato Inc. raised 6.75M$ from Madrona and New Enterprise Associates in A round, and 18.5M$ in B round from Vulcan Capital and Opus Capital, as well as Madrona and New Enterprise Associates.


The motivation for GraphLab was to hit the sweetspot in development of machine learning solutions. "(Then in 2010) Existing high-level parallel abstractions like MapReduce are insufficiently expressive, while low-level tools like MPI and Pthreads leave machine learning (ML) experts repeatedly solving the same design challenges."

GraphLab aimed to express asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving good parallel performance. This paper provides an efficient multicore parallel (but not distributed) shared-memory implementation of GraphLab. (The C++ reference implementation of the GraphLab is at

Related work

MapReduce works well on embrassingly data parallel tasks, but is not efficient for graph processing and iterative algorithms. This line from the paper gave me a chuckle. "By coercing efficient sequential ML algorithms to satisfy the restrictions imposed by MapReduce, we often produce inefficient parallel algorithms that require many processors to be competitive with comparable sequential methods." (Frank's laptop also took on against GraphLab.)

DAG abstraction represents parallel computation as a directed acyclic graph with data flowing along edges between vertices that correspond to computation/function. Dryad, Storm, Heron fit here. DAG does not naturally express iterative algorithms.

Dataflow abstraction in Naiad is a related work. This being a 2010 paper there is no comparison to Naiad in the paper. This is what Naiad (2013) has to say about GraphLab. "GraphLab and PowerGraph offer a different asynchronous programming model for graph computations, based on a shared memory abstraction. These asynchronous systems are not designed to execute dataflow graphs so the notion of completeness of an epoch or iteration is less important, but the lack of completeness notifications makes it hard to compose asynchronous computations. Although GraphLab and PowerGraph provide a global synchronization mechanism that can be used to write a program that performs one computation after another, they do not achieve task- or pipeline-parallelism between stages of a computation. Naiad allows programs to introduce coordination only where it is required, to support hybrid asynchronous and synchronous computation."

Petuum is also a closely related work. Petuum uses BSP, with SSP relaxation and nonuniform convergence. It looks like  GraphLab allows more fine-granular scheduling and supports data-graph computations and dependency modeling better. On the other hand, Petuum was designed clean-slate to support machine learning algorithms better, and introduced model-parallelism concept. GraphLab, as you will see in the summary below is principally a graph processing framework, with good applicability for graph-based machine learning tasks. Petuum paper says "Petuum ML implementations can run faster than other platforms (e.g. Spark, GraphLab4), because Petuum can exploit model dependencies, uneven convergence and error tolerance". The paper provides performance comparison experiments with GraphLab.

GraphLab abstraction

The minimalism yet generality of the GraphLab framework remind me of LISP. "The data graph G = (V, E) encodes both the problem specific sparse computational structure and directly modifiable program state. The user can associate arbitrary blocks of data (or parameters) with each vertex and directed edge in G." (Also the set scheduler that enables users to compose custom update schedules is clever and LISPy.)

Figure 3 illustrates the overall GraphLab framework. A GraphLab program is composed of the following parts:
1. A data graph which represents the data and computational dependencies.
2. Update functions which describe local computation
3. A Sync mechanism for aggregating global state
4. A data consistency model (i.e., Fully Consistent, Edge Consistent or Vertex Consistent), which determines the extent to which computation can overlap.
5. Scheduling primitives which express the order of computation and may depend dynamically on the data.

Data Model

The GraphLab data model consists of two parts: a directed data graph and a shared data table. The data graph G = (V, E) encodes both the problem specific sparse computational structure and directly modifiable program state. The user can associate arbitrary blocks of data (or parameters) with each vertex and directed edge in G. To support globally shared state, GraphLab provides a shared data table (SDT) which is an associative map, T: [Key] --> Value, between keys and arbitrary blocks of data. Here, Key can be a vertex, edge, result of a sync computation as explained below, or a global variable, say model parameter.

User defined computation

Computation in GraphLab can be performed either through an update function which defines the local computation, or through the sync mechanism which defines global aggregation. The Update Function is analogous to the Map in MapReduce, and sync mechanism is analogous to the Reduce operation. A GraphLab program may consist of multiple update functions and it is up to the scheduling model to determine which update functions are applied to which vertices and in which parallel order.


The GraphLab update schedule describes the order in which update functions are applied to vertices and is represented by a parallel data-structure called the scheduler. The scheduler abstractly represents a dynamic list of tasks (vertex-function pairs) which are to be executed by the GraphLab engine. This is minimalist yet flexible and general model. Since writing a scheduler is hard, GraphLab provides a default BSP style scheduler or a round-robin scheduler.

Since many ML algorithms (e.g., Lasso, CoEM, Residual BP) require more control over the tasks that are created and the order in which they are executed, GraphLab also allows update functions to add and reorder tasks. For this, GraphLab provides 1) FIFO schedulers that only permit task creation but do not permit task reordering, and 2) prioritized schedules that permit task reordering at the cost of increased overhead.


The paper  demonstrates the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing.


GraphLab Wikipedia page

Paper Review. Petuum: A new platform for distributed machine learning on big data


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