Realtime reservoir characterization and beyond: cyber-infrastructure tools and technologies

The advent of the digital oil _x000C_eld and rapidly decreasing cost of computing creates opportunities as well as challenges in simulation based reservoir studies, in particular, real-time reservoir characterization and optimization. One challenge our e_x000B_orts are directed toward is the use of real-time production data to perform live reservoir characterization using high throughput, high performance computing environments. To that end we developed the required tools of parallel reservoir simulator, parallel ensemble Kalman _x000C_lter and a scalable work ow manager. When using this collection of tools, a reservoir modeler is able to perform large scale reservoir management studies in short periods of time. This includes studies with thousands of models that are individually complex and large, involving millions of degrees of freedom. Using parallel processing, we are able to solve these models much faster than we otherwise would on a single, serial machine. This motivated the development of a fast parallel reservoir simulator. Furthermore, distributing those simulations across resources leads to a smaller total time to completion by making use of distributed processing. This allows the development of a scalable high throughput work ow manager. Finally, with thousands of models, each with millions of degrees of freedom, we end up with a super uity of model parameters. This translates directly to billions of degrees of freedom in the reservoir study. To be able to use the ensemble Kalman _x000C_lter on these models, we needed to develop a parallel implementation of the ensemble Kalman _x000C_lter. This thesis discusses the enabling tools and technologies developed to address a speci _x000C_c problem: how to accurately characterize reservoirs, using large numbers of complex detailed models. For these characterization studies to be helpful in making production decisions, the time to solution must be feasible. To that end, our work is focused on developing and extending these tools, and optimizing their performance

Artist studio as interactive art

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.Includes bibliographical references (p. 123-127).Digital technology has radically changed human communication in the last few decades. The digital medium has pushed cultural artifacts towards forms defined by interaction, participation, and social systems. This thesis looks at recent changes of artistic practice in the realm of digital visual arts. In order to study the intersection between interactive art and the digital image, I will describe the design of an online participatory studio system, where an artist can perform the creative process and respond to remote feedback from audience participants at the same time. Such a system opens a conversation between artist and audience that will shed new light on how we can learn, understand and communicate boundaries in digital space and digital interactive art.by Luis Blackaller.S.M

High performance development for high end computing with Python Language Wrapper (PLW

This paper presents a design and implementation of a system that leverages interactive scripting environment to the needs of scientific computing. The system allows seamless translation of high level script codes to highly optimized native language executables that can be ported to parallel systems with high performance hardware and potential lack of the scripting language interpreter. Performance results are given to show various usage scenarios that vary in terms of invested programmer’s effort and resulting performance gains.

Geospatial Computing: Architectures and Algorithms for Mapping Applications

Beginning with the MapTube website (1), which was launched in 2007 for crowd-sourcing maps, this project investigates approaches to exploratory Geographic Information Systems (GIS) using web-based mapping, or ‘web GIS’. Users can log in to upload their own maps and overlay different layers of GIS data sets. This work looks into the theory behind how web-based mapping systems function and whether their performance can be modelled and predicted. One of the important questions when dealing with different geospatial data sets is how they relate to one another. Internet data stores provide another source of information, which can be exploited if more generic geospatial data mining techniques are developed. The identification of similarities between thousands of maps is a GIS technique that can give structure to the overall fabric of the data, once the problems of scalability and comparisons between different geographies are solved. After running MapTube for nine years to crowd-source data, this would mark a natural progression from visualisation of individual maps to wider questions about what additional knowledge can be discovered from the data collected. In the new ‘data science’ age, the introduction of real-time data sets introduces a new challenge for web-based mapping applications. The mapping of real-time geospatial systems is technically challenging, but has the potential to show inter-dependencies as they emerge in the time series. Combined geospatial and temporal data mining of realtime sources can provide archives of transport and environmental data from which to accurately model the systems under investigation. By using techniques from machine learning, the models can be built directly from the real-time data stream. These models can then be used for analysis and experimentation, being derived directly from city data. This then leads to an analysis of the behaviours of the interacting systems. (1) The MapTube website: http://www.maptube.org

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Language Support for Distributed Functional Programming

Software development has taken a fundamental turn. Software today has gone from simple, closed programs running on a single machine, to massively open programs, patching together user experiences byway of responses received via hundreds of network requests spanning multiple machines. At the same time, as data continues to stockpile, systems for big data analytics are on the rise. Yet despite this trend towards distributing computation, issues at the level of the language and runtime abound. Serialization is still a costly runtime affair, crashing running systems and confounding developers. Function closures are being added to APIs for big data processing for use by end-users without reliably being able to transmit them over the network. And much of the frameworks developed for handling multiple concurrent requests byway of asynchronous programming facilities rely on blocking threads, causing serious scalability issues. This thesis describes a number of extensions and libraries for the Scala programming language that aim to address these issues and to provide a more reliable foundation on which to build distributed systems. This thesis presents a new approach to serialization called pickling based on the idea of generating and composing functional pickler combinators statically. The approach shifts the burden of serialization to compile time as much as possible, enabling users to catch serialization errors at compile time rather than at runtime. Further, by virtue of serialization code being generated at compile time, our framework is shown to be significantly more performant than other state-of-the-art serialization frameworks. We also generalize our technique for generating serialization code to generic functions other than pickling. Second, in light of the trend of distributed data-parallel frameworks being designed around functional patterns where closures are transmitted across cluster nodes to large-scale persistent datasets, this thesis introduces a new closure-like abstraction and type system, called spores, that can guarantee closures to be serializable, thread-safe, or even have custom user-defined properties. Crucially, our system is based on the principle of encoding type information corresponding to captured variables in the type of a spore. We prove our type system sound, implement our approach for Scala, evaluate its practicality through a small empirical study, and show the power of these guarantees through a case analysis of real-world distributed and concurrent frameworks that this safe foundation for closures facilitates. Finally, we bring together the above building blocks, pickling and spores, to form the basis of a new programming model called function-passing. Function-passing is based on the idea of a distributed persistent data structure which stores in its nodes transformations to data rather than the distributed data itself, simplifying fault recovery by design. Lazy evaluation is also central to our model; by incorporating laziness into our design only at the point of initiating network communication, our model remains easy to reason about while remaining efficient in time and memory. We formalize our programming model in the form of a small-step operational semantics which includes a precise specification of the semantics of functional fault recovery, and we provide an open-source implementation of our model in and for Scala

Programming Languages and Systems

This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp