Frontiers in Massive Data Analysis

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Recent developments of high-end processors recognize energy monitoring and tuning as one of the main challenges towards achieving higher performance given the growing power and temperature constraints. Our thermal energy model is based on application-specific parameters such as consumed power, execution time, and equilibrium temperature as well as hardware-specific parameters such as half time for thermal rise or fall. As observed with the out-of-band instrumentation and monitoring infrastructure on our experimental cluster with air cooling, the temperature changes follow a relatively slow capacitor-style charge-discharge process.

Therefore, we use the lumped thermal model that initiates an exponential process whenever there is a change in processor's power consumption. Experiments with two codes — Firestarter and Nekbone — validate our approach and demonstrate its use for analyzing and potentially improving the application-specific balance between temperature, power, and performance.

While the very far future well beyond exaflops computing may encompass such paradigm shifts as quantum computing or neuromorphic computing, a critical window of change exists within the domain of semiconductor digital logic technology but beyond conventional practices of architecture, system software, and programming. Mainstream computer architecture in HPC has been inhibited in innovation by the original von Neumann architecture of seven decades ago. Although notably diverse in form of parallelism exploited, six major epochs of computer architecture through to the present are all von Neumann derivatives.

However, in the modern age, FPUs consume only a small part of die real estate while the plethora of mechanisms to achieve maximum floating point efficiency take up the majority of the chip. The von Neumann bottleneck, the separation of memory and processor, is also retained. Data streams are a sequence of data flowing between source and destination processes.

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Streaming is widely used for signal, image and video processing for its efficiency in pipelining and effectiveness in reducing demand for memory. The goal of this work is to extend the use of data streams to support both conventional scientific applications and emerging data analytics applications running on HPC platforms. MPIStream supports data streams either within a single application or among multiple applications.

We show the convenience of using MPI streams to support the needs from both traditional HPC and emerging data analytics applications running on supercomputers. Communication networks in recent high performance computing machines often have multi-dimensional torus topologies, which influences the way jobs should be scheduled into the system.

With the rapid growth of the size of modern HPC system's interconnect, network contention has become a critical issue for the performance of parallel jobs, especially for those which are communication-intensive and not tolerant to inter-job interference.

Moreover, to improve the runtime consistency, a contiguous allocation strategy is usually adopted, and each job is allocated a convex prism.

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However, using this strategy brings in internal and external fragmentation, which can degrade the system utilization. To this end, in this work, we investigate and develop various strategies in topology-aware job scheduling strategies for multidimensional torus-based systems, with the objective of improving job performance and system utilization. Because of the steadily growing volume and complexity of the data exchanged through the Internet and among connected devices, and the need to rapidly elaborate the incoming information, new challenges have been posed to High Performance Computing HPC.

Several architectures, programming languages, Cloud services and software have been proposed to efficiently deal with modern HPC applications, in particular with Big Data, and solve the related issues that arise. As a consequence, users often find it difficult to select the right platform, application or service fitting their requirements. In order to support them in deploying their applications, Patterns have been adopted and exploited, providing a base canvas on which users can develop their own applications. In this paper we offer an overview of modern Pattern advancements for HPC and Big Data, also providing an insight on semantic-based technologies which have been successfully applied to provide a flexible representation thereof.

While many massive data sets can be produced within a single administrative domain, many more massive data sets can be, and must be, assembled from multiple sources. Aggregating data from multiple sources can be a tedious task. First, the locations of the desired data must be known. Second, access to the data sets must be allowed. For publicly accessible data, this may not pose a serious problem. However, many application domains and user groups may wish to facilitate, and have some degree of control over, how their resources are discovered and shared.

Such collaboration requirements are addressed by federation management technologies. In this paper, we argue that effective, widely-adopted federation management tools, i.

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To this end, we re-visit the NIST cloud deployment models to extract and identify the fundamental aspects of federation management: crossing trust boundaries, trust topologies, and deployment topologies. We then review possible barriers to adoption and relevant, existing tooling and standards to facilitate the emergence of a common practice for Big Identity.

A main research goal of IT nowadays is to investigate and design new scalable solutions for big data analysis. This goal asks for coupling scalable algorithms with high-performance programming tools and platforms. Addressing these challenges requires a seamless integration of the scalable computing techniques and big data analytics research approaches and frameworks. In fact, scalability is a key item for big data analysis and machine learning applications.

Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of HPC systems and clouds, whereas in the next future exascale systems will be used to implement extreme scale data analysis. This chapter introduces and discusses cloud models that support the design and development of scalable data mining applications and report on challenges and issues to be addressed and solved for developing data analysis algorithms on extreme-scale systems. However, further research is necessary in order to understand how growing data sizes from data intensive simulations coupled with the limited DRAM capacity in High End Computing systems will impact the effectiveness of this approach.

Moreover, the complex and dynamic data exchange patterns exhibited by the workflows coupled with the varied data access behaviors make efficient data placement within the staging area challenging. In this paper, we explore how we can use deep memory levels for data staging and develop a multi-tiered data staging method that spans both DRAM and solid state disks SSD.

This approach allows us to support both code coupling and data management for data-intensive simulation workflows. We also show how adaptive application-aware data placement mechanisms can dynamically manage and optimize data placement vertically across the DRAM and SSD storage levels and horizontally across different staging nodes in this multi-tiered data staging method.

The evaluation results demonstrate that our approach can effectively improve data access performance and overall efficiency of coupled scientific workflows.

This paper proposes model rotation as a general approach to parallelize big data machine learning applications. To solve the big model problem in parallelization, we distribute the model parameters to inter-node workers and rotate different model parts in a ring topology. The advantage of model rotation comes from maximizing the effect of parallel model updates for algorithm convergence while minimizing the overhead of communication. We formulate a solution using computation models, programming interfaces, and system implementations as design principles and derive a machine learning framework with three algorithms built on top of it: Latent Dirichlet Allocation using Collapsed Gibbs Sampling, Matrix Factorization using Stochastic Gradient Descent and Cyclic Coordinate Descent.

The performance results on an Intel Haswell cluster with max 60 nodes show that our solution achieves faster model convergence speed and higher scalability than previous work by others. Today, about 55 per cent of the world's population lives in urban areas, a proportion that is expected to increase to 66 per cent by Such a steadily increasing urbanization is already bringing huge social, economic and environmental transformations and, at the same time, poses big challenges in city management issues, like resource planning water, electricity , traffic, air and water quality, public policy and public safety services.

To face such challenges, the exploitation of information coming from urban environments and the development of Smart City applications to enhance quality, improve performance and safety of urban services, are key elements. This chapter discusses how the analysis of urban data may be exploited for forecasting crimes and presents an approach, based on seasonal auto-regressive models, for reliably forecasting crime trends in urban areas.

In particular, the main goal of this work is to discuss the impact of data mining on urban crime analysis and design a predictive model to forecast the number of crimes that will happen in rolling time horizons. As a case study, we present the analysis performed on an area of Chicago. Experimental evaluation results show that the proposed methodology can achieve high predictive accuracy for long term crime forecasting, thus can be successfully exploited to predict the time evolution of the number of crimes in urban environments. However, data science applications are often posed as questions about discrete objects such as graphs while problems in modeling and simulation are usually stated initially in terms of classical mathematical analysis.

We will present examples and arguments to show that the two points of view are not as distinct as one might think. Recognizing the connections between the two problem sets will be essential to development of algorithms capable of exascale performance. Our main examples will be from applications of Monte Carlo to attacking hard problems of the kind that occur both in data science and in computational modeling of physical phenomena. In this model, we consider the complexity of solving families of database query problems that correspond to hyper -graph algorithms on multipartite graphs and hypergraphs, including problems like triangle-enumeration and path-finding.

For single round computation, in many instances we show that simple well-known MapReduce algorithms provide optimal tradeoffs between processors and the load. We also analyze how the best algorithms become more complicated as the distribution of degrees in the input graphs and hypergraphs becomes more skewed. For multiple rounds, analyzing the general model requires understanding of circuit complexity over non-standard bases. However, in a more restricted version of the model which is still sufficiently general to simulate MapReduce we show some of the first lower bounds for multi-round computations on MapReduce-like models.

Joint work with Paraschos Koutris and Dan Suciu. Louis Sample and Prune: An Efficient MapReduce Method for Submodular Optimization [ Slides pdf ] MapReduce has been widely considered for optimizing submodular functions over large data sets and several techniques have been developed. In this talk, we will discuss the Sample and Prune procedure. Sample and Prune is a distributed sampling method used to efficiency discover a small set of representative elements from a large data set. We discuss how Sample and Prune can be used for submodular optimization in MapReduce. In particular, we show how this procedure can be utilized to simulate a class of greedy sequential algorithms for submodular optimization in MapReduce.

We will further discuss its use to construct new distributed algorithms for submodular optimization and how it could possibly be extended to construct efficient algorithms for a wide range of problems in the distributed setting. Her main research interests are machine learning, computational aspects in economics and game theory, and algorithms. Paul received his B. He was a Post-doctoral Research Associate at M. Paul's research is concerned primarily with computational complexity.


His main interest is in proving lower bounds on the resources needed for solving computational problems. Such topics include communication complexity, time-space tradeoff lower bounds, proof complexity, and data structures. In addition, Paul is interested in problems related to formal reasoning and verification.

  • Workshop Theme.
  • With Big Data, New Frontiers.
  • Front Matter | Frontiers in Massive Data Analysis | The National Academies Press.
  • He has worked on the application and extension of the techniques of symbolic model checking for the verification of software specifications. Ravi Kumar has been a senior staff research scientist in Google since His primary interests are web and data mining, social networks, algorithms for large data sets, and theory of computation. His research areas include algorithms, algorithmic game theory, combinatorial optimization, and social networks analysis. At Google, he is mainly working on algorithmic and economic problems related to search and online advertising.

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    Recently he is working on online ad allocation problems, distributed algorithms for large-scale graph mining, and mechanism design for advertising exchanges. Louis in July Prior to that, he was a visiting professor at Sandia National Laboratories in and a research intern at Yahoo! Research in and Professor Moseley's research interests are broadly focused in the field of theoretical computer science. Specifically, he is interested in the design, analysis and limitations of algorithms.

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    He is also interested in the applications of algorithms. Recently, his work has focused on problems arising in resource allocation, large data analysis and sustainable computing. He is interested in computation with limited resources, including sublinear-time algorithms, streaming algorithms, and algorithms for modern parallel systems. His research interests lie in the design, analysis, and applications of algorithms; current application areas of interest include social networks, participatory democracy, Internet commerce, and large scale data processing.

    Professor Goel is a recipient of an Alfred P. He was a co-author on the paper that won the best paper award at WWW , and an Edelman Laureate in Professor Goel was a research fellow and technical advisor at Twitter, Inc.

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