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While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill this critical void, we introduced the GraphLab abstraction which naturally expresses asynchronous, dynamic, graph-parallel computation while ensuring data consistency and achieving a high degree of parallel performance in the shared-memory setting. In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees. We develop graph based extensions to pipelined locking and data versioning to reduce network congestion and mitigate the effect of network latency. We also introduce fault tolerance to the GraphLab abstraction using the classic Chandy-Lamport snapshot algorithm and demonstrate how it can be easily implemented by exploiting the GraphLab abstraction itself. Finally, we evaluate our distributed implementation of the GraphLab abstraction on a large Amazon EC2 deployment and show 1-2 orders of magnitude performance gains over Hadoop-based implementations.

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We propose Shotgun, a parallel coordinate descent algorithm for minimizing L 1regularized losses. Though coordinate descent seems inherently sequential, we prove convergence bounds for Shotgun which predict linear speedups, up to a problemdependent limit. We present a comprehensive empirical study of Shotgun for Lasso and sparse logistic regression. Our theoretical predictions on the potential for parallelism closely match behavior on real data. Shotgun outperforms other published solvers on a range of large problems, proving to be one of the most scalable algorithms for L 1 .

The running time of nested parallel programs on shared-memory machines depends in significant part on how well the scheduler mapping the program to the machine is optimized for the organization of caches and processor cores on the machine. Recent work proposed "space-bounded schedulers" for scheduling such programs on the multilevel cache hierarchies of current machines. The main benefit of this class of schedulers is that they provably preserve locality of the program at every level in the hierarchy, which can result in fewer cache misses and better use of bandwidth than the popular work-stealing scheduler. On the other hand, compared to work stealing, space-bounded schedulers are inferior at load balancing and may have greater scheduling overheads, raising the question as to the relative effectiveness of the two schedulers in practice.In this article, we provide the first experimental study aimed at addressing this question. To facilitate this study, we built a flexible experimental framework with separate interfaces for programs and schedulers. This enables a head-to-head comparison of the relative strengths of schedulers in terms of running times and cache miss counts across a range of benchmarks. (The framework is validated by comparisons with the Intel R Cilk TM Plus work-stealing scheduler.) We present experimental results on a 32-core Xeon R 7560 comparing work stealing, hierarchy-minded work stealing, and two variants of space-bounded schedulers on both divide-and-conquer microbenchmarks and some popular algorithmic kernels. Our results indicate that space-bounded schedulers reduce the number of L3 cache misses compared to work-stealing schedulers by 25% to 65% for most of the benchmarks, but incur up to 27% additional scheduler and load-imbalance overhead. Only for memory-intensive benchmarks can the reduction in cache misses overcome the added overhead, resulting in up to a 25% improvement in running time for synthetic benchmarks and about 20% improvement for algorithmic kernels. We also quantify runtime improvements varying the available bandwidth per core (the "bandwidth gap") and show up to 50% improvements in the running times of kernels as this gap increases fourfold. As part of our study, we generalize prior definitions of space-bounded schedulers to allow for more practical variants (while still preserving their guarantees) and explore implementation tradeoffs.

While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill this critical void, we introduced the GraphLab abstraction which naturally expresses asynchronous, dynamic, graph-parallel computation while ensuring data consistency and achieving a high degree of parallel performance in the shared-memory setting. In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees.We develop graph based extensions to pipelined locking and data versioning to reduce network congestion and mitigate the effect of network latency. We also introduce fault tolerance to the GraphLab abstraction using the classic Chandy-Lamport snapshot algorithm and demonstrate how it can be easily implemented by exploiting the GraphLab abstraction itself. Finally, we evaluate our distributed implementation of the GraphLab abstraction on a large Amazon EC2 deployment and show 1-2 orders of magnitude performance gains over Hadoop-based implementations.

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