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[서평]「Computer Organization and Design, The Hardware/Software Interface」

The prevalence of mobile phones, the internet-of-things technology, and networks of sensors has led to an enormous and ever increasing amount of data that are now more commonly available in a streaming fashion [1]-[5]. Often, it is assumed - either implicitly or explicitly - that the process generating such a stream of data is stationary, that is, the data are drawn from a fixed, albeit unknown probability distribution. In many real-world scenarios, however, such an assumption is simply not true, and the underlying process generating the data stream is characterized by an intrinsic nonstationary (or evolving or drifting) phenomenon. The nonstationarity can be due, for example, to seasonality or periodicity effects, changes in the users' habits or preferences, hardware or software faults affecting a cyber-physical system, thermal drifts or aging effects in sensors. In such nonstationary environments, where the probabilistic properties of the data change over time, a non-adaptive model trained under the false stationarity assumption is bound to become obsolete in time, and perform sub-optimally at best, or fail catastrophically at worst.

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Learning in Nonstationary Environments: A Survey

Ensemble-based methods are among the most widely used techniques for data stream classification. Their popularity is attributable to their good performance in comparison to strong single learners while being relatively easy to deploy in real-world applications. Ensemble algorithms are especially useful for data stream learning as they can be integrated with drift detection algorithms and incorporate dynamic updates, such as selective removal or addition of classifiers. This work proposes a taxonomy for data stream ensemble learning as derived from reviewing over 60 algorithms. Important aspects such as combination, diversity, and dynamic updates, are thoroughly discussed. Additional contributions include a listing of popular open-source tools and a discussion about current data stream research challenges and how they relate to ensemble learning (big data streams, concept evolution, feature drifts, temporal dependencies, and others).

A Survey on Ensemble Learning for Data Stream Classification

Kai Hwang and Zhiwei Xu  McGraw-Hill, Boston, 1998, 802 pp. ISBN 0-07-031798-4, $97.30 This text is an in depth introduction to the concepts of Parallel Computing. Designed for use in university level computer science courses, the text covers scalable architecture and parallel programming of symmetric muli-processors, clusters of workstations, massively parallel processors, and Internet-based metacomputing platforms. Hwang and Xu give an excellent overview in these topics while keeping the text easily comprehensible. The text is organized into four parts. Part I covers scalability and clustering. Part II deals with the technology used to construct a parallel system. Part III pertains to the architecture of scalable systems. Finally, Part IV presents methods of parallel programming on various platforms and languages. The first chapter presents different models on scalability as divided into resources, applications, and technology. It defines three abstract models (PRAM, BSP, and phase parallel models) and five physical models (PVP, SMP, MPP, COW, and MPP systems). Chapter 2 introduces the ideas behind parallel programming including processes, tasks, threads and environments. Chapter 3 introduces performance issues and metrics. As an introduction to Part II, Chapter 4 introduces the history of microprocessor types and their applications in the architectures of current systems. Chapter 5 deals with the issues of distributed memory. It discusses several models such as UMA, NORMA, CC-NUMA, COMA, and DSM. Chapter 6 presents gigabit networks, switched interconnects, and other various high-speed networking architectures to construct clusters. Chapter 7 discusses the overheads created by parallel computing, such as threads, synchronization, and efficient communication between nodes. Part III, Chapter 8, 9 and 11, give comparisons between various types of scalable systems (SMP, CC-NUMA, Clusters, and MPP). The comparisons are based on hardware architecture, the system software, and special features that make each system unique. Chapter 10 compares various research and commercial clusters with an in depth study of the Berkeley NOW, IBM SP2, and Digital TruCluster systems. Chapter 12 introduces the concepts of Part IV with details into parallel programming paradigms. Chapter 13 discusses communications between the processors using message passing programming (such as MPI and PVM libraries). Chapter 14 studies the data parallel approach with an emphasis in Fortran 90 and HPF. With attention to detail through examples, Hwang and Xu have created a well-written introduction to Parallel Computing. The authors are distinguished for their contributions in this field. This text is based on cutting-edge research, providing the current theories that are in use in industry today. Bin Cong, Shawn Morrison and Michael Yorg, Department of Computer Science  California Polytechnic State University at San Luis Obispo

Scalable Parallel Computing: Technology, Architecture, Programming

While both cost-sensitive learning and online learning have been studied separately, these two issues have seldom been addressed simultaneously. Yet, there are many applications where both aspects are important. This paper investigates a class of algorithmic approaches suitable for online cost-sensitive learning, designed for such problems. The basic idea is to leverage existing methods for online ensemble algorithms, and combine these with batch mode methods for cost-sensitive bagging/boosting algorithms. Within this framework, we describe several theoretically sound online cost-sensitive bagging and online cost-sensitive boosting algorithms, and show that the convergence of the proposed algorithms is guaranteed under certain conditions. We then present extensive experimental results on benchmark datasets to compare the performance of the various proposed approaches.

Online Bagging and Boosting for Imbalanced Data Streams

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 Distributed Weka Spark, 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, Distributed Weka Spark provides a usable prototype distributed Big Data Mining workbench that achieves near-linear scaling in executing various real-world scale workloads - 91.4% weak scaling efficiency on average and up to 4x faster on average than Hadoop.

A Parallel Distributed Weka Framework for Big Data Mining Using Spark

http://www.dcs.gla.ac.uk/~jsinger/pdfs/nanopatterns.pdf

Fundamental Nano-Patterns to Characterize and Classify Java Methods

Thread-Level Speculation (TLS) overcomes limitations intrinsic with conservative compile-time auto-parallelizing 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 96p on 2 threads and 40p on 32 threads. The experiments for Lector, report average speedups of 1.7x for 2 threads (that is 1 TLS p 1 Inspector threads) increasing close to 8.2x speedups with 32 threads (16 p 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 p x Inspector threads) Lector delivers worse performance than the baseline TLS with the equivalent number of TLS threads (i.e. x TLS threads) nor doubling the equivalent number of TLS threads (i.e., x p x TLS threads).

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Optimizing software runtime systems for speculative parallelization

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.

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Online non-stationary boosting

Thread-Level Speculation (TLS) facilitates the extraction of parallel threads from sequential applications. Most prior work has focused on developing the compiler and architecture for this execution paradigm. Such studies often narrowly concentrated on a specific design point. On the other hand, other studies have attempted to assess how well TLS performs if some architectural/ compiler constraint is relaxed. Unfortunately, such previous studies have failed to truly assess TLS performance potential, because they have been bound to some specific TLS architecture and have ignored one or another important TLS design choice, such as support for out-of-order task spawn or support for intermediate checkpointing.

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Paraskevas Yiapanis

Papers

A Parallel Distributed Weka Framework for Big Data Mining Using Spark

Fundamental Nano-Patterns to Characterize and Classify Java Methods

Optimizing software runtime systems for speculative parallelization

Online non-stationary boosting

Toward a more accurate understanding of the limits of the TLS execution paradigm