Kernels for Below-Upper-Bound Parameterizations of the Hitting Set and Directed Dominating Set Problems

In the {\sc Hitting Set} problem, we are given a collection $\cal F$ of subsets of a ground set $V$ and an integer $p$, and asked whether $V$ has a $p$-element subset that intersects each set in $\cal F$. We consider two parameterizations of {\sc Hitting Set} below tight upper bounds: $p=m-k$ and $p=n-k$. In both cases $k$ is the parameter. We prove that the first parameterization is fixed-parameter tractable, but has no polynomial kernel unless coNP$\subseteq$NP/poly. The second parameterization is W[1]-complete, but the introduction of an additional parameter, the degeneracy of the hypergraph $H=(V,{\cal F})$, makes the problem not only fixed-parameter tractable, but also one with a linear kernel. Here the degeneracy of $H=(V,{\cal F})$ is the minimum integer $d$ such that for each $X\subset V$ the hypergraph with vertex set $V\setminus X$ and edge set containing all edges of $\cal F$ without vertices in $X$, has a vertex of degree at most $d.$ In {\sc Nonblocker} ({\sc Directed Nonblocker}), we are given an undirected graph (a directed graph) $G$ on $n$ vertices and an integer $k$, and asked whether $G$ has a set $X$ of $n-k$ vertices such that for each vertex $y\not\in X$ there is an edge (arc) from a vertex in $X$ to $y$. {\sc Nonblocker} can be viewed as a special case of {\sc Directed Nonblocker} (replace an undirected graph by a symmetric digraph). Dehne et al. (Proc. SOFSEM 2006) proved that {\sc Nonblocker} has a linear-order kernel. We obtain a linear-order kernel for {\sc Directed Nonblocker}

Distributed GraphLab: A Framework for Machine Learning in the Cloud

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.Comment: VLDB201

Exploring Dynamic Compilation and Cross-Layer Object Management Policies for Managed Language Applications

Recent years have witnessed the widespread adoption of managed programming languages that are designed to execute on virtual machines. Virtual machine architectures provide several powerful software engineering advantages over statically compiled binaries, such as portable program representations, additional safety guarantees, automatic memory and thread management, and dynamic program composition, which have largely driven their success. To support and facilitate the use of these features, virtual machines implement a number of services that adaptively manage and optimize application behavior during execution. Such runtime services often require tradeoffs between efficiency and effectiveness, and different policies can have major implications on the system's performance and energy requirements. In this work, we extensively explore policies for the two runtime services that are most important for achieving performance and energy efficiency: dynamic (or Just-In-Time (JIT)) compilation and memory management. First, we examine the properties of single-tier and multi-tier JIT compilation policies in order to find strategies that realize the best program performance for existing and future machines. Our analysis performs hundreds of experiments with different compiler aggressiveness and optimization levels to evaluate the performance impact of varying if and when methods are compiled. We later investigate the issue of how to optimize program regions to maximize performance in JIT compilation environments. For this study, we conduct a thorough analysis of the behavior of optimization phases in our dynamic compiler, and construct a custom experimental framework to determine the performance limits of phase selection during dynamic compilation. Next, we explore innovative memory management strategies to improve energy efficiency in the memory subsystem. We propose and develop a novel cross-layer approach to memory management that integrates information and analysis in the VM with fine-grained management of memory resources in the operating system. Using custom as well as standard benchmark workloads, we perform detailed evaluation that demonstrates the energy-saving potential of our approach. We implement and evaluate all of our studies using the industry-standard Oracle HotSpot Java Virtual Machine to ensure that our conclusions are supported by widely-used, state-of-the-art runtime technology

GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra

We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS comes with a benchmark specification based on extensive literature review, prescribing representative problems, algorithms, and datasets. Second, GMS offers a carefully designed software platform for seamless testing of different fine-grained elements of graph mining algorithms, such as graph representations or algorithm subroutines. The platform includes parallel implementations of more than 40 considered baselines, and it facilitates developing complex and fast mining algorithms. High modularity is possible by harnessing set algebra operations such as set intersection and difference, which enables breaking complex graph mining algorithms into simple building blocks that can be separately experimented with. GMS is supported with a broad concurrency analysis for portability in performance insights, and a novel performance metric to assess the throughput of graph mining algorithms, enabling more insightful evaluation. As use cases, we harness GMS to rapidly redesign and accelerate state-of-the-art baselines of core graph mining problems: degeneracy reordering (by up to >2x), maximal clique listing (by up to >9x), k-clique listing (by 1.1x), and subgraph isomorphism (by up to 2.5x), also obtaining better theoretical performance bounds

Compiler-assisted multiple instruction rollback recovery using a read buffer

Multiple instruction rollback (MIR) is a technique that has been implemented in mainframe computers to provide rapid recovery from transient processor failures. Hardware-based MIR designs eliminate rollback data hazards by providing data redundancy implemented in hardware. Compiler-based MIR designs have also been developed which remove rollback data hazards directly with data-flow transformations. This paper describes compiler-assisted techniques to achieve multiple instruction rollback recovery. We observe that some data hazards resulting from instruction rollback can be resolved efficiently by providing an operand read buffer while others are resolved more efficiently with compiler transformations. The compiler-assisted scheme presented consists of hardware that is less complex than shadow files, history files, history buffers, or delayed write buffers, while experimental evaluation indicates performance improvement over compiler-based schemes

A Distributed and Self-Organizing Scheduling Algorithm for Energy-Efficient Data Aggregation in Wireless Sensor Networks

Wireless sensor networks (WSNs) are increasingly being used to monitor various parameters in a wide range of environmental monitoring applications. In many instances, environmental scientists are interested in collecting raw data using long-running queries injected into a WSN for analyzing at a later stage, rather than injecting snap-shot queries containing data-reducing operators (e.g., MIN, MAX, AVG) that aggregate data. Collection of raw data poses a challenge to WSNs as very large amounts of data need to be transported through the network. This not only leads to high levels of energy consumption and thus diminished network lifetime but also results in poor data quality as much of the data may be lost due to the limited bandwidth of present-day sensor nodes. We alleviate this problem by allowing certain nodes in the network to aggregate data by taking advantage of spatial and temporal correlations of various physical parameters and thus eliminating the transmission of redundant data. In this article we present a distributed scheduling algorithm that decides when a particular node should perform this novel type of aggregation. The scheduling algorithm autonomously reassigns schedules when changes in network topology, due to failing or newly added nodes, are detected. Such changes in topology are detected using cross-layer information from the underlying MAC layer. We first present the theoretical performance bounds of our algorithm. We then present simulation results, which indicate a reduction in message transmissions of up to 85% and an increase in network lifetime of up to 92% when compared to collecting raw data. Our algorithm is also capable of completely eliminating dropped messages caused by buffer overflow