BZPEERPapers, people, evidence and research paths
Menu

Discover

SearchFind papers, people, topics, institutions and opportunities from one place.GraphSee how papers, authors, topics and evidence connect.

Research tools

HomeContinue from the papers, people and topics that matter now.CollectionsTurn saved papers into reading lists, evidence maps and shareable context.ResearchReconstruct the path that led you from search to paper to question.CollaborationMove from a paper, topic or expert request into a focused thread.PublishCreate full-text research articles with metadata, formulas and versions.

Network

ExpertsFind specialists whose work and reviews explain why they can help.CommunitiesJoin research groups organized around papers, topics and shared evidence.

Opportunities

ListingsBrowse roles, calls and collaborations connected to real research context.

Account

Log inReturn to your saved papers, graph views and active threads.Create accountStart saving papers, building a research trail and joining teams.
Log inCreate account

Topic overview

Performance

725 works2635 researchers
Open mapBrowse papers

Map preview

Start with the graph, then narrow the list

725works
2635researchers

Next steps

Use the topic as a working map

Open the full map for clusters, then return here to scan ranked papers and people.

Topic graph

See the topic as a live network

Open full explorer

Inspect nearby papers, researchers, institutions and communities without opening a separate graph page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Papers in this area

24 paper(s) to start with

Showing a focused slice from 725 total papers. Open the graph or search to expand the full area.

preprint2017arXiv

Experiences with Some Benchmarks for Deductive Databases and Implementations of Bottom-Up Evaluation

OpenRuleBench is a large benchmark suite for rule engines, which includes deductive databases. We previously proposed a translation of Datalog to C++ based on a method that "pushes" derived tuples immediately to places where they are used. In this paper, we report performance results of various implementation variants of this method compared to XSB, YAP and DLV. We study only a fraction of the OpenRuleBench problems, but we give a quite detailed analysis of each such task and the factors which influence performance. The results not only show the potential of our method and implementation approach, but could be valuable for anybody implementing systems which should be able to execute tasks of the discussed types.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Memory transfer optimization for a lattice Boltzmann solver on Kepler architecture nVidia GPUs

The Lattice Boltzmann method (LBM) for solving fluid flow is naturally well suited to an efficient implementation for massively parallel computing, due to the prevalence of local operations in the algorithm. This paper presents and analyses the performance of a 3D lattice Boltzmann solver, optimized for third generation nVidia GPU hardware, also known as `Kepler'. We provide a review of previous optimisation strategies and analyse data read/write times for different memory types. In LBM, the time propagation step (known as streaming), involves shifting data to adjacent locations and is central to parallel performance; here we examine three approaches which make use of different hardware options. Two of which make use of `performance enhancing' features of the GPU; shared memory and the new shuffle instruction found in Kepler based GPUs. These are compared to a standard transfer of data which relies instead on optimised storage to increase coalesced access. It is shown that the more simple approach is most efficient; since the need for large numbers of registers per thread in LBM limits the block size and thus the efficiency of these special features is reduced. Detailed res

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

M/g/c/c state dependent queueing model for road traffic simulation

In this paper, we present a stochastic queuing model for the road traffic, which captures the stationary density-flow relationships in both uncongested and congestion conditions. The proposed model is based on the $M/g/c/c$ state dependent queuing model of Jain and Smith, and is inspired from the deterministic Godunov scheme for the road traffic simulation. We first propose a reformulation of the $M/g/c/c$ state dependent model that works with density-flow fundamental diagrams rather than density-speed relationships. We then extend this model in order to consider upstream traffic demand as well as downstream traffic supply. Finally, we calculate the speed and travel time distributions for the $M/g/c/c$ state dependent queuing model and for the proposed model, and derive stationary performance measures (expected number of cars, blocking probability, expected travel time, and throughput). A comparison with results predicted by the $M/g/c/c$ state dependent queuing model shows that the proposed model correctly represents the dynamics of traffic and gives good performances measures. The results illustrate the good accuracy of the proposed model.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Accelerated Convolutions for Efficient Multi-Scale Time to Contact Computation in Julia

Convolutions have long been regarded as fundamental to applied mathematics, physics and engineering. Their mathematical elegance allows for common tasks such as numerical differentiation to be computed efficiently on large data sets. Efficient computation of convolutions is critical to artificial intelligence in real-time applications, like machine vision, where convolutions must be continuously and efficiently computed on tens to hundreds of kilobytes per second. In this paper, we explore how convolutions are used in fundamental machine vision applications. We present an accelerated n-dimensional convolution package in the high performance computing language, Julia, and demonstrate its efficacy in solving the time to contact problem for machine vision. Results are measured against synthetically generated videos and quantitatively assessed according to their mean squared error from the ground truth. We achieve over an order of magnitude decrease in compute time and allocated memory for comparable machine vision applications. All code is packaged and integrated into the official Julia Package Manager to be used in various other scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Fault-Tolerant Adaptive Parallel and Distributed Simulation

Discrete Event Simulation is a widely used technique that is used to model and analyze complex systems in many fields of science and engineering. The increasingly large size of simulation models poses a serious computational challenge, since the time needed to run a simulation can be prohibitively large. For this reason, Parallel and Distributes Simulation techniques have been proposed to take advantage of multiple execution units which are found in multicore processors, cluster of workstations or HPC systems. The current generation of HPC systems includes hundreds of thousands of computing nodes and a vast amount of ancillary components. Despite improvements in manufacturing processes, failures of some components are frequent, and the situation will get worse as larger systems are built. In this paper we describe FT-GAIA, a software-based fault-tolerant extension of the GAIA/ARTÌS parallel simulation middleware. FT-GAIA transparently replicates simulation entities and distributes them on multiple execution nodes. This allows the simulation to tolerate crash-failures of computing nodes; furthermore, FT-GAIA offers some protection against byzantine failures since synchronization mes

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Application-aware Retiming of Accelerators: A High-level Data-driven Approach

Flexibility at hardware level is the main driving force behind adaptive systems whose aim is to realise microarhitecture deconfiguration 'online'. This feature allows the software/hardware stack to tolerate drastic changes of the workload in data centres. With emerge of FPGA reconfigurablity this technology is becoming a mainstream computing paradigm. Adaptivity is usually accompanied by the high-level tools to facilitate multi-dimensional space exploration. An essential aspect in this space is memory orchestration where on-chip and off-chip memory distribution significantly influences the architecture in coping with the critical spatial and timing constraints, e.g. Place and Route. This paper proposes a memory smart technique for a particular class of adaptive systems: Elastic Circuits which enjoy slack elasticity at fine level of granularity. We explore retiming of a set of popular benchmarks via investigating the memory distribution within and among accelerators. The area, performance and power patterns are adopted by our high-level synthesis framework, with respect to the behaviour of the input descriptions, to improve the quality of the synthesised elastic circuits.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Transient performance analysis of zero-attracting LMS

Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. It combines the LMS framework and $\ell_1$-norm regularization to promote sparsity, and relies on subgradient iterations. Despite the significant interest in ZA-LMS, few works analyzed its transient behavior. The main difficulty lies in the nonlinearity of the update rule. In this work, a detailed analysis in the mean and mean-square sense is carried out in order to examine the behavior of the algorithm. Simulation results illustrate the accuracy of the model and highlight its performance through comparisons with an existing model.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Faster 64-bit universal hashing using carry-less multiplications

Intel and AMD support the Carry-less Multiplication (CLMUL) instruction set in their x64 processors. We use CLMUL to implement an almost universal 64-bit hash family (CLHASH). We compare this new family with what might be the fastest almost universal family on x64 processors (VHASH). We find that CLHASH is at least 60% faster. We also compare CLHASH with a popular hash function designed for speed (Google's CityHash). We find that CLHASH is 40% faster than CityHash on inputs larger than 64 bytes and just as fast otherwise.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

A Generalized Performance Evaluation Framework for Parallel Systems with Output Synchronization

Frameworks, such as MapReduce and Hadoop are abundant nowadays. They seek to reap benefits of parallelization, albeit subject to a synchronization constraint at the output. Fork-Join (FJ) queuing models are used to analyze such systems. Arriving jobs are split into tasks each of which is mapped to exactly one server. A job leaves the system when all of its tasks are executed. As a metric of performance, we consider waiting times for both work-conserving and non-work conserving server systems under a mathematical set-up general enough to take into account possible phase-type behavior of the servers, and as suggested by recent evidences, bursty arrivals. To this end, we present a Markov-additive process framework for an FJ system and provide computable bounds on tail probabilities of steady-state waiting times, for both types of servers separately. We apply our results to three scenarios, namely, non-renewal (Markov-modulated) arrivals, servers showing phase-type behavior, and Markov-modulated arrivals and services. We compare our bounds against estimates obtained through simulations and also provide a theoretical conceptualization of provisions in FJ systems. Finally, we calibrate o

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Dissecting demand response mechanisms: the role of consumption forecasts and personalized offers

Demand-Response (DR) programs, whereby users of an electricity network are encouraged by economic incentives to rearrange their consumption in order to reduce production costs, are envisioned to be a key feature of the smart grid paradigm. Several recent works proposed DR mechanisms and used analytical models to derive optimal incentives. Most of these works, however, rely on a macroscopic description of the population that does not model individual choices of users. In this paper, we conduct a detailed analysis of those models and we argue that the macroscopic descriptions hide important assumptions that can jeopardize the mechanisms' implementation (such as the ability to make personalized offers and to perfectly estimate the demand that is moved from a timeslot to another). Then, we start from a microscopic description that explicitly models each user's decision. We introduce four DR mechanisms with various assumptions on the provider's capabilities. Contrarily to previous studies, we find that the optimization problems that result from our mechanisms are complex and can be solved numerically only through a heuristic. We present numerical simulations that compare the

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Data Center Server Provision: Distributed Asynchronous Control for Coupled Renewal Systems

This paper considers a cost minimization problem for data centers with N servers and randomly arriving service requests. A central router decides which server to use for each new request. Each server has three types of states (active, idle, setup) with different costs and time durations. The servers operate asynchronously over their own states and can choose one of multiple sleep modes when idle. We develop an online distributed control algorithm so that each server makes its own decisions, the request queues are bounded and the overall time average cost is near optimal with probability 1. The algorithm does not need probability information for the arrival rate or job sizes. Next, an improved algorithm that uses a single queue is developed via a "virtualization" technique which is shown to provide the same (near optimal) costs. Simulation experiments on a real data center traffic trace demonstrate the efficiency of our algorithm compared to other existing algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Modeling LRU caches with Shot Noise request processes

In this paper we analyze Least Recently Used (LRU) caches operating under the Shot Noise requests Model (SNM). The SNM was recently proposed to better capture the main characteristics of today Video on Demand (VoD) traffic. We investigate the validity of Che's approximation through an asymptotic analysis of the cache eviction time. In particular, we provide a large deviation principle, a law of large numbers and a central limit theorem for the cache eviction time, as the cache size grows large. Finally, we derive upper and lower bounds for the "hit" probability in tandem networks of caches under Che's approximation.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Spatial multi-LRU: Distributed Caching for Wireless Networks with Coverage Overlaps

This article introduces a novel family of decentralised caching policies, applicable to wireless networks with finite storage at the edge-nodes (stations). These policies, that are based on the Least-Recently-Used replacement principle, are here referred to as spatial multi-LRU. They update cache inventories in a way that provides content diversity to users who are covered by, and thus have access to, more than one station. Two variations are proposed, the multi-LRU-One and -All, which differ in the number of replicas inserted in the involved caches. We analyse their performance under two types of traffic demand, the Independent Reference Model (IRM) and a model that exhibits temporal locality. For IRM, we propose a Che-like approximation to predict the hit probability, which gives very accurate results. Numerical evaluations show that the performance of multi-LRU increases the more the multi-coverage areas increase, and it is close to the performance of centralised policies, when multi-coverage is sufficient. For IRM traffic, multi-LRU-One is preferable to multi-LRU-All, whereas when the traffic exhibits temporal locality the -All variation can perform better. Both variations outp

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

PageRank Pipeline Benchmark: Proposal for a Holistic System Benchmark for Big-Data Platforms

The rise of big data systems has created a need for benchmarks to measure and compare the capabilities of these systems. Big data benchmarks present unique scalability challenges. The supercomputing community has wrestled with these challenges for decades and developed methodologies for creating rigorous scalable benchmarks (e.g., HPC Challenge). The proposed PageRank pipeline benchmark employs supercomputing benchmarking methodologies to create a scalable benchmark that is reflective of many real-world big data processing systems. The PageRank pipeline benchmark builds on existing prior scalable benchmarks (Graph500, Sort, and PageRank) to create a holistic benchmark with multiple integrated kernels that can be run together or independently. Each kernel is well defined mathematically and can be implemented in any programming environment. The linear algebraic nature of PageRank makes it well suited to being implemented using the GraphBLAS standard. The computations are simple enough that performance predictions can be made based on simple computing hardware models. The surrounding kernels provide the context for each kernel that allows rigorous definition of both the input and the

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Julia Implementation of the Dynamic Distributed Dimensional Data Model

Julia is a new language for writing data analysis programs that are easy to implement and run at high performance. Similarly, the Dynamic Distributed Dimensional Data Model (D4M) aims to clarify data analysis operations while retaining strong performance. D4M accomplishes these goals through a composable, unified data model on associative arrays. In this work, we present an implementation of D4M in Julia and describe how it enables and facilitates data analysis. Several experiments showcase scalable performance in our new Julia version as compared to the original Matlab implementation.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Geographical Load Balancing across Green Datacenters

"Geographic Load Balancing" is a strategy for reducing the energy cost of data centers spreading across different terrestrial locations. In this paper, we focus on load balancing among micro-datacenters powered by renewable energy sources. We model via a Markov Chain the problem of scheduling jobs by prioritizing datacenters where renewable energy is currently available. Not finding a convenient closed form solution for the resulting chain, we use mean field techniques to derive an asymptotic approximate model which instead is shown to have an extremely simple and intuitive steady state solution. After proving, using both theoretical and discrete event simulation results, that the system performance converges to the asymptotic model for an increasing number of datacenters, we exploit the simple closed form model's solution to investigate relationships and trade-offs among the various system parameters.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Benchmarking the Graphulo Processing Framework

Graph algorithms have wide applicablity to a variety of domains and are often used on massive datasets. Recent standardization efforts such as the GraphBLAS specify a set of key computational kernels that hardware and software developers can adhere to. Graphulo is a processing framework that enables GraphBLAS kernels in the Apache Accumulo database. In our previous work, we have demonstrated a core Graphulo operation called \textit{TableMult} that performs large-scale multiplication operations of database tables. In this article, we present the results of scaling the Graphulo engine to larger problems and scalablity when a greater number of resources is used. Specifically, we present two experiments that demonstrate Graphulo scaling performance is linear with the number of available resources. The first experiment demonstrates cluster processing rates through Graphulo's TableMult operator on two large graphs, scaled between $2^{17}$ and $2^{19}$ vertices. The second experiment uses TableMult to extract a random set of rows from a large graph ($2^{19}$ nodes) to simulate a cued graph analytic. These benchmarking results are of relevance to Graphulo users who wish to apply Graphu

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Benchmarking SciDB Data Import on HPC Systems

SciDB is a scalable, computational database management system that uses an array model for data storage. The array data model of SciDB makes it ideally suited for storing and managing large amounts of imaging data. SciDB is designed to support advanced analytics in database, thus reducing the need for extracting data for analysis. It is designed to be massively parallel and can run on commodity hardware in a high performance computing (HPC) environment. In this paper, we present the performance of SciDB using simulated image data. The Dynamic Distributed Dimensional Data Model (D4M) software is used to implement the benchmark on a cluster running the MIT SuperCloud software stack. A peak performance of 2.2M database inserts per second was achieved on a single node of this system. We also show that SciDB and the D4M toolbox provide more efficient ways to access random sub-volumes of massive datasets compared to the traditional approaches of reading volumetric data from individual files. This work describes the D4M and SciDB tools we developed and presents the initial performance results. This performance was achieved by using parallel inserts, a in-database merging of arrays as well

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Queueing Analysis of a Large-Scale Bike Sharing System through Mean-Field Theory

The bike sharing systems are fast increasing as a public transport mode in urban short trips, and have been developed in many major cities around the world. A major challenge in the study of bike sharing systems is that large-scale and complex queueing networks have to be applied through multi-dimensional Markov processes, while their discussion always suffers a common difficulty: State space explosion. For this reason, this paper provides a mean-field computational method to study such a large-scale bike sharing steps: Firstly, a multi-dimensional Markov process is set up for expressing the states of the bike sharing system, and the empirical process of the multi-dimensional Markov process is given to partly overcome the difficulty of state space explosion. Based on this, the mean-field equations are derived by means of a virtual time-inhomogeneous M(t)/M(t)/1/K queue whose arrival and service rates are determined by the mean-field computation. Secondly, the martingale limit is employed to investigate the limiting behavior of the empirical process, the fixed point is proved to be unique so that it can be computed by means of a nonlinear birth-death process, the asymptotic independ

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Broadcast Strategies and Performance Evaluation of IEEE 802.15.4 in Wireless Body Area Networks WBAN

The rapid advances in sensors and ultra-low power wireless communication has enabled a new generation of wireless sensor networks: Wireless Body Area Networks (WBAN). To the best of our knowledge the current paper is the first to address broadcast in WBAN. We first analyze several broadcast strategies inspired from the area of Delay Tolerant Networks (DTN). The proposed strategies are evaluated via the OMNET++ simulator that we enriched with realistic human body mobility models and channel models issued from the recent research on biomedical and health informatics. Contrary to the common expectation, our results show that existing research in DTN cannot be transposed without significant modifications in WBANs area. That is, existing broadcast strategies for DTNs do not perform well with human body mobility. However, our extensive simulations give valuable insights and directions for designing efficient broadcast in WBAN. Furthermore, we propose a novel broadcast strategy that outperforms the existing ones in terms of end-to-end delay, network coverage and energy consumption. Additionally, we performed investigations of independent interest related to the ability of all the studied

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Comparison Between IPv4 to IPv6 Transition Techniques

The IPv4 addresses exhaustion demands a protocol transition from IPv4 to IPv6. The original transition technique, the dual stack, is not widely deployed yet and it demanded the creation of new transition techniques to extend the transition period. This work makes an experimental comparison of techniques that use dual stack with a limited IPv4 address. This limited address might be a RFC 1918 address with a NAT at the Internet Service Provider (ISP) gateway, also known as Carrier Grade NAT (CGN), or an Address Plus Port (A+P) shared IPv4 address. The chosen techniques also consider an IPv6 only ISP network. The transport of the IPv4 packets through the IPv6 only networks may use IPv4 packets encapsulated on IPv6 packets or a double translation, by making one IPv4 to IPv6 translation to enter the IPv6 only network and one IPv6 to IPv4 translation to return to the IPv4 network. The chosen techniques were DS-Lite, 464XLAT, MAP-E and MAP-T. The first part of the test is to check some of the most common usages of the Internet by a home user and the impacts of the transition techniques on the user experience. The second part is a measured comparison considering bandwidth, jitter and laten

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Support vector regression model for BigData systems

Nowadays Big Data are becoming more and more important. Many sectors of our economy are now guided by data-driven decision processes. Big Data and business intelligence applications are facilitated by the MapReduce programming model while, at infrastructural layer, cloud computing provides flexible and cost effective solutions for allocating on demand large clusters. In such systems, capacity allocation, which is the ability to optimally size minimal resources for achieve a certain level of performance, is a key challenge to enhance performance for MapReduce jobs and minimize cloud resource costs. In order to do so, one of the biggest challenge is to build an accurate performance model to estimate job execution time of MapReduce systems. Previous works applied simulation based models for modeling such systems. Although this approach can accurately describe the behavior of Big Data clusters, it is too computationally expensive and does not scale to large system. We try to overcome these issues by applying machine learning techniques. More precisely we focus on Support Vector Regression (SVR) which is intrinsically more robust w.r.t other techniques, like, e.g., neural networks, and

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

On the energy efficiency of client-centric data consistency management under random read/write access to Big Data with Apache HBase

The total estimated energy bill for data centers in 2010 was \$11.5 billion, and experts estimate that the energy cost of a typical data center doubles every five years. On the other hand, computational developments have started to lag behind storage advancements, therein becoming a future bottleneck for the ongoing data growth which already approaches Exascale levels. We investigate the relationship among data throughput and energy footprint on a large storage cluster, with the goal of formalizing it as a metric that reflects the trading among consistency and energy. Employing a client-centric consistency approach, and while honouring ACID properties of the chosen columnar store for the case study (Apache HBase), we present the factors involved in the energy consumption of the system as well as lessons learned to underpin further design of energy-efficient cluster scale storage systems.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Serving the Grid: an Experimental Study of Server Clusters as Real-Time Demand Response Resources

Demand response is a crucial technology to allow large-scale penetration of intermittent renewable energy sources in the electric grid. This paper is based on the thesis that datacenters represent especially attractive candidates for providing flexible, real-time demand response services to the grid; they are capable of finely-controllable power consumption, fast power ramp-rates, and large dynamic range. This paper makes two main contributions: (a) it provides detailed experimental evidence justifying this thesis, and (b) it presents a comparative investigation of three candidate software interfaces for power control within the servers. All of these results are based on a series of experiments involving real-time power measurements on a lab-scale server cluster. This cluster was specially instrumented for accurate and fast power measurements on a time-scale of 100 ms or less. Our results provide preliminary evidence for the feasibility of large scale demand response using datacenters, and motivates future work on exploiting this capability.

0 citations0 reviewsNo code linkedOpen access

People in this topic

12 visible researcher(s)

Ranked from 2635 researcher records connected to this topic.