Emerging high-performance distributed computing environments are enabling new end-to-end formulations in science and engineering that involve multiple interacting processes and data-intensive application workflows. For example, current fusion simulation efforts are exploring coupled models and codes that simultaneously simulate separate application processes, such as the core and the edge turbulence, and run on different high performance computing resources. These components need to interact, at runtime, with each other and with services for data monitoring, data analysis and visualization, and data archiving. As a result, they require efficient support for dynamic and flexible couplings and interactions, which remains a challenge. This paper presents DataSpaces, a flexible interaction and coordination substrate that addresses this challenge. DataSpaces essentially implements a semantically specialized virtual shared space abstraction that can be associatively accessed by all components and services in the application workflow. It enables live data to be extracted from running simulation components, indexes this data online, and then allows it to be monitored, queried and accessed by other components and services via the space using semantically meaningful operators. The underlying data transport is asynchronous, low-overhead and largely memory-to-memory. The design, implementation, and experimental evaluation of DataSpaces using a coupled fusion simulation workflow is presented.

DataSpaces: an interaction and coordination framework for coupled simulation workflows

Although functional RNA molecules are known to be biased in overall composition, the effects of background composition on the probability of finding a particular active site by chance has received little attention. The probability of finding a particular motif has important implications both for understanding the distribution of functional RNAs in ancient and modern organisms with varying genome compositions and for tuning SELEX pools to optimize the chance of finding specific functions. Here we develop a new method for calculating the probability of finding a modular motif containing base-paired regions, and use a computational grid to fold several hundred million random RNA sequences containing the core elements of the isoleucine aptamer and the hammerhead ribozyme to estimate the probability that a sequence containing these structural elements will fold correctly when isolated from background sequences of different compositions. We find that the two motifs are most likely to be found in distinct regions of compositional space, and that the regions of greatest abundance are influenced by the probability of finding the conserved bases, finding the flanking helices, and folding, in that order of importance. Additionally, we can refine our estimates of the number of random sequences required for a 50% probability of finding an example of each site in unbiased random pools of length 100 to 4.1 x 10(9) for the isoleucine aptamer and 1.6 x 10(10) for the hammerhead ribozyme. These figures are consistent with the facile recovery of these motifs from SELEX experiments.

/pdf/abundance-of-correctly-folded-rna-motifs-in-sequence-space-37flv4mw0d.pdf

Abundance of correctly folded RNA motifs in sequence space, calculated on computational grids.

Previous experiments with high-performance Java were initially disappointing. After several years of optimization, this paper investigates the current suitability of such object-oriented middleware for High-Performance and Grid programming. Using a middleware offering high level abstractions (ProActive), we have replaced the standard Java RMI layer with the optimized Ibis RMI interface. Ibis is a grid programming environment featuring efficient communications. Using a 3D electromagnetic application (an object-oriented time domain finite volume solver for 3D Maxwell equations) we have first conducted benchmarks on single clusters, including comparisons with the same application in Fortran MPI. Finally, Grid experiments have been conducted simultaneously on up to 5 different clusters. Overall, the paper reports extremely promising results. For instance, a speed up of 12 on 16 machines (vs. 13.8 for Fortran), a speedup of 100 on 150 machines on a Grid.

A High Performance Java Middleware with a Real Application

Meeting the demands for energy entails a better understanding and characterization of the fundamental processes of reservoirs and of how human made objects affect these systems. The need to perform extensive reservoir studies for either uncertainty assessment or optimal exploitation plans brings up demands of computing power and data management in a more pervasive way. This work focuses on high performance numerical methods, tools and grid-enabled middleware systems for scalable and data-driven computations for multiphysics simulation and decision-making processes in integrated multiphase flow applications. The proposed suite of tools and systems consists of (1) a scalable reservoir simulator, (2) novel stochastic optimization algorithms, (3) decentralized autonomic grid middleware tools, and (4) middleware systems for large-scale data storage, querying, and retrieval. The aforementioned components offer enormous potential for performing data-driven studies and efficient execution of complex, large-scale reservoir models in a collaborative environment.

Models, methods and middleware for grid-enabled multiphysics oil reservoir management

While large-scale parallel/distributed simulations are rapidly becoming critical research modalities in academia and industry, their efficient and scalable parallel implementations present many challenges. A key challenge is the dynamic and complex communication/coordination patterns required by these applications, which depend on states of the phenomenon being modeled and are determined by the specific numerical formulation, the domain decomposition and/or sub-domain refinement algorithms used, and are known only at runtime. In this paper, we present a dynamic geometry-based shared-space interaction framework for scientific applications. The framework provides the flexibility of shared-space coordination models while enabling scalable implementations. The design, prototype implementation and experimental evaluation using an adaptive multi-block oil reservoir simulation are presented.

A dynamic geometry-based shared space interaction framework for parallel scientific applications

A prototype Taskspaces framework for grid computing of scientific computing problems that require intertask communication is presented. The Taskspaces framework is characterized by three major design choices: decentralization provided by an underlying tuple space concept, enhanced direct communication between tasks by means of a communication tuple space distributed over the worker hosts, and object orientation and platform independence realized by implementation in Java. Grid administration tasks, for example resetting worker nodes, are performed by mobile agent objects. We report on large-scale grid computing experiments for iterative linear algebra applications showing that our prototype framework scales well for scientific computing problems that require neighbor-neighbor intertask communication. It is shown in a computational fluid dynamics simulation using a Lattice Boltzmann method that the Taskspaces framework can be used naturally in interactive collaboration mode. The scalable Taskspaces framework runs fully transparently on heterogeneous grids while maintaining a low complexity in terms of installation, maintenance, application programming and grid operation. It thus offers a promising roadway to push scientific grid computing with intertask communication beyond the experimental research setting.

/pdf/a-lightweight-java-taskspaces-framework-for-scientific-26ljw7ki9y.pdf

A lightweight Java taskspaces framework for scientific computing on computational grids

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A lightweight Java taskspaces framework for scientific computing on computational grids