GraphLab: A New Framework for Parallel Machine Learning

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

On-the-fly tracing for data-centric computing : parallelization, workflow and applications

As data-centric computing becomes the trend in science and engineering, more and more hardware systems, as well as middleware frameworks, are emerging to handle the intensive computations associated with big data. At the programming level, it is crucial to have corresponding programming paradigms for dealing with big data. Although MapReduce is now a known programming model for data-centric computing where parallelization is completely replaced by partitioning the computing task through data, not all programs particularly those using statistical computing and data mining algorithms with interdependence can be re-factorized in such a fashion. On the other hand, many traditional automatic parallelization methods put an emphasis on formalism and may not achieve optimal performance with the given limited computing resources. In this work we propose a cross-platform programming paradigm, called on-the-fly data tracing , to provide source-to-source transformation where the same framework also provides the functionality of workflow optimization on larger applications. Using a big-data approximation computations related to large-scale data input are identified in the code and workflow and a simplified core dependence graph is built based on the computational load taking in to account big data. The code can then be partitioned into sections for efficient parallelization; and at the workflow level, optimization can be performed by adjusting the scheduling for big-data considerations, including the I/O performance of the machine. Regarding each unit in both source code and workflow as a model, this framework enables model-based parallel programming that matches the available computing resources. The techniques used in model-based parallel programming as well as the design of the software framework for both parallelization and workflow optimization as well as its implementations with multiple programming languages are presented in the dissertation. Then, the following experiments are performed to validate the framework: i) the benchmarking of parallelization speed-up using typical examples in data analysis and machine learning (e.g. naive Bayes, k-means) and ii) three real-world applications in data-centric computing with the framework are also described to illustrate the efficiency: pattern detection from hurricane and storm surge simulations, road traffic flow prediction and text mining from social media data. In the applications, it illustrates how to build scalable workflows with the framework along with performance enhancements

Dynamic Scheduling for Energy Minimization in Delay-Sensitive Stream Mining

Numerous stream mining applications, such as visual detection, online patient monitoring, and video search and retrieval, are emerging on both mobile and high-performance computing systems. These applications are subject to responsiveness (i.e., delay) constraints for user interactivity and, at the same time, must be optimized for energy efficiency. The increasingly heterogeneous power-versus-performance profile of modern hardware presents new opportunities for energy saving as well as challenges. For example, employing low-performance processing nodes can save energy but may violate delay requirements, whereas employing high-performance processing nodes can deliver a fast response but may unnecessarily waste energy. Existing scheduling algorithms balance energy versus delay assuming constant processing and power requirements throughout the execution of a stream mining task and without exploiting hardware heterogeneity. In this paper, we propose a novel framework for dynamic scheduling for energy minimization (DSE) that leverages this emerging hardware heterogeneity. By optimally determining the processing speeds for hardware executing classifiers, DSE minimizes the average energy consumption while satisfying an average delay constraint. To assess the performance of DSE, we build a face detection application based on the Viola-Jones classifier chain and conduct experimental studies via heterogeneous processor system emulation. The results show that, under the same delay requirement, DSE reduces the average energy consumption by up to 50% in comparison to conventional scheduling that does not exploit hardware heterogeneity. We also demonstrate that DSE is robust against processing node switching overhead and model inaccuracy

Asynchronous Parallel Stochastic Gradient Descent - A Numeric Core for Scalable Distributed Machine Learning Algorithms

The implementation of a vast majority of machine learning (ML) algorithms boils down to solving a numerical optimization problem. In this context, Stochastic Gradient Descent (SGD) methods have long proven to provide good results, both in terms of convergence and accuracy. Recently, several parallelization approaches have been proposed in order to scale SGD to solve very large ML problems. At their core, most of these approaches are following a map-reduce scheme. This paper presents a novel parallel updating algorithm for SGD, which utilizes the asynchronous single-sided communication paradigm. Compared to existing methods, Asynchronous Parallel Stochastic Gradient Descent (ASGD) provides faster (or at least equal) convergence, close to linear scaling and stable accuracy