Massively parallel landscape-evolution modelling using general purpose graphical processing units

As our expectations of what computer systems can do and our ability to capture data improves, the desire to perform ever more computationally intensive tasks increases. Often these tasks, comprising vast numbers of repeated computations, are highly interdependent on each other – a closely coupled problem. The process of Landscape-Evolution Modelling is an example of such a problem. In order to produce realistic models it is necessary to process landscapes containing millions of data points over time periods extending up to millions of years. This leads to non-tractable execution times, often in the order of years. Researchers therefore seek multiple orders of magnitude reduction in the execution time of these models. The massively parallel programming environment offered through General Purpose Graphical Processing Units offers the potential for multiple orders of magnitude speedup in code execution times. In this paper we demonstrate how the time dominant parts of a Landscape-Evolution Model can be recoded for a massively parallel architecture providing two orders of magnitude reduction in execution time

Neurons and symbols: a manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty

A performance focused, development friendly and model aided parallelization strategy for scientific applications

The amelioration of high performance computing platforms has provided unprecedented computing power with the evolution of multi-core CPUs, massively parallel architectures such as General Purpose Graphics Processing Units (GPGPUs) and Many Integrated Core (MIC) architectures such as Intel\u27s Xeon phi coprocessor. However, it is a great challenge to leverage capabilities of such advanced supercomputing hardware, as it requires efficient and effective parallelization of scientific applications. This task is difficult mainly due to complexity of scientific algorithms coupled with the variety of available hardware and disparate programming models. To address the aforementioned challenges, this thesis presents a parallelization strategy to accelerate scientific applications that maximizes the opportunities of achieving speedup while minimizing the development efforts. Parallelization is a three step process (1) choose a compatible combination of architecture and parallel programming language, (2) translate base code/algorithm to a parallel language and (3) optimize and tune the application. In this research, a quantitative comparison of run time for various implementations of k-means algorithm, is used to establish that native languages (OpenMP, MPI, CUDA) perform better on respective architectures as opposed to vendor-neutral languages such as OpenCL. A qualitative model is used to select an optimal architecture for a given application by aligning the capabilities of accelerators with characteristics of the application. Once the optimal architecture is chosen, the corresponding native language is employed. This approach provides the best performance with reasonable accuracy (78%) of predicting a fitting combination, while eliminating the need for exploring different architectures individually. It reduces the required development efforts considerably as the application need not be re-written in multiple languages. The focus can be solely on optimization and tuning to achieve the best performance on available architectures with minimized investment in terms of cost and efforts. To verify the prediction accuracy of the qualitative model, the OpenDwarfs benchmark suite, which implements the Berkeley\u27s dwarfs in OpenCL, is used. A dwarf is an algorithmic method that captures a pattern of computation and communication. For the purpose of this research, the focus is on 9 application from various algorithmic domains that cover the seven dwarfs of symbolic computation, which were identified by Phillip Colella, as omnipresent in scientific and engineering applications. To validate the parallelization strategy collectively, a case study is undertaken. This case study involves parallelization of the Lower Upper Decomposition for the Gaussian Elimination algorithm from the linear algebra domain, using conventional trial and error methods as well as the proposed \u27Architecture First, Language Later\u27\u27 strategy. The development efforts incurred are contrasted for both methods. The aforesaid proposed strategy is observed to reduce the development efforts by an average of 50%

Contributions to the efficient use of general purpose coprocessors: kernel density estimation as case study

142 p.The high performance computing landscape is shifting from assemblies of homogeneous nodes towards heterogeneous systems, in which nodes consist of a combination of traditional out-of-order execution cores and accelerator devices. Accelerators provide greater theoretical performance compared to traditional multi-core CPUs, but exploiting their computing power remains as a challenging task.This dissertation discusses the issues that arise when trying to efficiently use general purpose accelerators. As a contribution to aid in this task, we present a thorough survey of performance modeling techniques and tools for general purpose coprocessors. Then we use as case study the statistical technique Kernel Density Estimation (KDE). KDE is a memory bound application that poses several challenges for its adaptation to the accelerator-based model. We present a novel algorithm for the computation of KDE that reduces considerably its computational complexity, called S-KDE. Furthermore, we have carried out two parallel implementations of S-KDE, one for multi and many-core processors, and another one for accelerators. The latter has been implemented in OpenCL in order to make it portable across a wide range of devices. We have evaluated the performance of each implementation of S-KDE in a variety of architectures, trying to highlight the bottlenecks and the limits that the code reaches in each device. Finally, we present an application of our S-KDE algorithm in the field of climatology: a novel methodology for the evaluation of environmental models

Roadmap on Electronic Structure Codes in the Exascale Era

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing.Comment: Submitted as a roadmap article to Modelling and Simulation in Materials Science and Engineering; Address any correspondence to Vikram Gavini ([email protected]) and Danny Perez ([email protected]

Roadmap on Electronic Structure Codes in the Exascale Era

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts

A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing

This work introduces an innovative parallel, fully-distributed finite element framework for growing geometries and its application to metal additive manufacturing. It is well-known that virtual part design and qualification in additive manufacturing requires highly-accurate multiscale and multiphysics analyses. Only high performance computing tools are able to handle such complexity in time frames compatible with time-to-market. However, efficiency, without loss of accuracy, has rarely held the centre stage in the numerical community. Here, in contrast, the framework is designed to adequately exploit the resources of high-end distributed-memory machines. It is grounded on three building blocks: (1) Hierarchical adaptive mesh refinement with octree-based meshes; (2) a parallel strategy to model the growth of the geometry; (3) state-of-the-art parallel iterative linear solvers. Computational experiments consider the heat transfer analysis at the part scale of the printing process by powder-bed technologies. After verification against a 3D benchmark, a strong-scaling analysis assesses performance and identifies major sources of parallel overhead. A third numerical example examines the efficiency and robustness of (2) in a curved 3D shape. Unprecedented parallelism and scalability were achieved in this work. Hence, this framework contributes to take on higher complexity and/or accuracy, not only of part-scale simulations of metal or polymer additive manufacturing, but also in welding, sedimentation, atherosclerosis, or any other physical problem where the physical domain of interest grows in time