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Thread-Level Speculation (TLS) overcomes limitations intrinsic with conservative compile-time autoparallelizing tools by extracting parallel threads optimistically and only ensuring absence of data dependence violations at runtime. A significant barrier for adopting TLS (implemented in software) is the overheads associated with maintaining speculative state. Based on previous TLS limit studies, we observe that on future multicore systems we will likely have more cores idle than those which traditional TLS would be able to harness. This implies that a TLS system should focus on optimizing for small number of cores and find efficient ways to take advantage of the idle cores. Furthermore, research on optimistic systems has covered two important implementation design points: eager vs. lazy version management. With this knowledge, we propose new simple and effective techniques to reduce the execution time overheads for both of these design points.This article describes a novel compact version management data structure optimized for space overhead when using a small number of TLS threads. Furthermore, we describe two novel software runtime parallelization systems that utilize this compact data structure. The first software TLS system, MiniTLS, relies on eager memory data management (in-place updates) and, thus, when a misspeculation occurs a rollback process is required. MiniTLS takes advantage of the novel compact version management representation to parallelize the rollback process and is able to recover from misspeculation faster than existing software eager TLS systems.The second one, Lector (Lazy inspECTOR) is based on lazy version management. Since we have idle cores, the question is whether we can create "helper" tasks to determine whether speculation is actually needed without stopping or damaging the speculative execution. In Lector, for each conventional TLS thread running speculatively with lazy version management, there is associated with it a lightweight inspector. The inspector threads execute alongside to verify quickly whether data dependencies will occur. Inspector threads are generated by standard techniques for inspector/executor parallelization.We have applied both TLS systems to seven Java sequential benchmarks, including three benchmarks from SPECjvm2008. Two out of the seven benchmarks exhibit misspeculations. MiniTLS experiments report average speedups of 1.8x for 4 threads increasing close to 7x speedups with 32 threads. Facilitated by our novel compact representation, MiniTLS reduces the space overhead over state-of-the-art software TLS systems between 96% on 2 threads and 40% on 32 threads. The experiments for Lector, report average speedups of 1.7x for 2 threads (that is 1 TLS + 1 Inspector threads) increasing close to 8.2x speedups with 32 threads (16+16 threads). Compared to a well established software TLS baseline, Lector performs on average 1.7x faster for 32 threads and in no case (x TLS +x Inspector threads) Lector delivers worse performance than the baseline TLS with the equivalent nu...

Abstract. Oza's Online Boosting algorithm provides a version of AdaBoost which can be trained in an online way for stationary problems. One perspective is that this enables the power of the boosting framework to be applied to datasets which are too large to fit into memory. The online boosting algorithm assumes the data distribution to be independent and identically distributed (i.i.d.) and therefore has no provision for concept drift. We present an algorithm called Online Non-Stationary Boosting (ONSBoost) that, like Online Boosting, uses a static ensemble size without generating new members each time new examples are presented, and also adapts to a changing data distribution. We evaluate the new algorithm against Online Boosting, using the STAGGER dataset and three challenging datasets derived from a learning problem inside a parallelising virtual machine. We find that the new algorithm provides equivalent performance on the STAGGER dataset and an improvement of up to 3% on the parallelisation datasets.

Abstract-Effective Big Data Mining requires scalable and efficient solutions that are also accessible to users of all levels of expertise. Despite this, many current efforts to provide effective knowledge extraction via large-scale Big Data Mining tools focus more on performance than on use and tuning which are complex problems even for experts.Weka is a popular and comprehensive Data Mining workbench with a well-known and intuitive interface; nonetheless it supports only sequential single-node execution. Hence, the size of the datasets and processing tasks that Weka can handle within its existing environment is limited both by the amount of memory in a single node and by sequential execution.This work discusses DistributedWekaSpark, a distributed framework for Weka which maintains its existing user interface. The framework is implemented on top of Spark, a Hadoop-related distributed framework with fast in-memory processing capabilities and support for iterative computations.By combining Weka's usability and Spark's processing power, DistributedWekaSpark provides a usable prototype distributed Big Data Mining workbench that achieves nearlinear scaling in executing various real-world scale workloads -91.4% weak scaling efficiency on average and up to 4x faster on average than Hadoop.

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