Use Of High Performance Computing Research Articles

SEMSim Cloud Service: Large-scale urban systems simulation in the cloud

Large-scale urban systems simulations are complex and with a large number of active simulation entities the computational workload is extensive. Workstation computers have only limited capabilities of delivering results for large-scale simulations. This leads to the problem that many researchers and engineers have to either reduce the scope of their experiments or fail to execute as many experiments as they would like in a given time frame. The use of high-performance computing (HPC) infrastructure offers a solution to the problem. Users of such simulations are often domain experts with no or little experience with HPC environments. In addition users do not necessarily have access to an HPC. In this paper we propose an architecture for a cloud-based urban systems simulation platform which specifically aims at making large-scale simulations available to typical users. The proposed architecture also addresses the issue of data confidentiality. In addition we describe the Scalable Electro-Mobility Simulation (SEMSim) Cloud Service that implements the proposed architecture.

Guest Editorial: Deep Learning

Deep Learning methods aim at learning feature hierarchies. Applications of deep learning to vision tasks date back to convolutional networks in the early 1990s. These methods have been the subject of a recent surge of interest for two main reasons: when labeled data is scarce, unsupervised learning algorithms can learn useful feature hierarchies. When labeled data is abundant, supervised methods can be used to train very large networks on very large datasets through the use of high-performance computers. Such large networks have been shown to outperform previous state-of-theart methods on several perceptual tasks, including categorylevel object recognition, object detection and semantic segmentation. In “Stacked Predictive Sparse Decomposition for Classification of Histology Sections” (doi:10.1007/s11263-0140790-9) the authors propose the use of an unsupervised feature learning algorithm for the analysis of biological tissue imagery. Biomedical applications is one domainwhere unsupervised learning can be very useful because of the paucity of labeled images available at training time.

Design and Development of Grid Enabled, G2PU Accelerated Java Application (Protein Sequence Study) for Grid Performance Analysis

Abstract Recent advancement in the field of structural biology has generated huge volume of data and analyzing such data is vital to know the hidden truths of life but such analysis is compute intensive in nature and requires huge computational power resulting in extensive use of high performance computing (Multi Core Computing, G2PU Computing, CPU-GPU Hybrid computing, Cluster, Grid) models. Grid is one of the widely used HPC model which is commonly used in computational biology and to execute a compute intensive tasks on grid, the applications must also be grid enabled. Performance of grid depends on appropriate selection of load balancing strategy (at server and node level) and maximum utilization of computational resources of nodes (multiple cores of CPU and execution units of GPU) by the grid enabled applications. In this paper an attempt has been made to establish an experimental grid, develop a grid enabled application and to operate the grid with its highest possible performance by the grid enabled applications.

High Performance computing improvements on bioinformatics consistency-based multiple sequence alignment tools

BGT method improves the execution time of progressive alignment by 62%.OLM decreases the memory requirements by 75%, aligning up twice more sequences than previous method.MTA improves the alignments accuracy, letting it to regain the quality lost due to the performance improvements. Multiple Sequence Alignment (MSA) is essential for a wide range of applications in Bioinformatics. Traditionally, the alignment accuracy was the main metric used to evaluate the goodness of MSA tools. However, with the growth of sequencing data, other features, such as performance and the capacity to align larger datasets, are gaining strength. To achieve these new requirements, without affecting accuracy, the use of high-performance computing (HPC) resources and techniques is crucial. In this paper, we apply HPC techniques in T-Coffee, one of the more accurate but less scalable MSA tools. We integrate three innovative solutions into T-Coffee: the Balanced Guide Tree to increase the parallelism/performance, the Optimized Library Method with the aim of enhancing the scalability and the Multiple Tree Alignment, which explores different alignments in parallel to improve the accuracy. The results obtained show that the resulting tool, MTA-TCoffee, is able to improve the scalability in both the execution time and also the number of sequences to be aligned. Furthermore, not only is the alignment accuracy not affected by these improvements, as would be expected, but it improves significantly. Finally, we emphasize that the presented methods are not just restricted to T-Coffee, but may be implemented in any other alignment tools that use similar algorithms (progressive alignment, consistency or guide trees).

OptQC: An optimized parallel quantum compiler

The software package Qcompiler (Chen and Wang 2013) provides a general quantum compilation framework, which maps any given unitary operation into a quantum circuit consisting of a sequential set of elementary quantum gates. In this paper, we present an extended software OptQC, which finds permutation matrices P and Q for a given unitary matrix U such that the number of gates in the quantum circuit of U=QTPTU′PQ is significantly reduced, where U′ is equivalent to U up to a permutation and the quantum circuit implementation of each matrix component is considered separately. We extend further this software package to make use of high-performance computers with a multiprocessor architecture using MPI. We demonstrate its effectiveness in reducing the total number of quantum gates required for various unitary operators. Program summaryProgram title: OptQCCatalogue identifier: AEUA_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEUA_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 178435No. of bytes in distributed program, including test data, etc.: 491574Distribution format: tar.gzProgramming language: Fortran, MPI.Computer: Any computer with Fortran compiler and MPI library.Operating system: Linux.Classification: 4.15.Nature of problem: It aims to minimize the number of quantum gates required to implement a given unitary operation.Solution method: It utilizes a threshold-based acceptance strategy for simulated annealing to select permutation matrices P and Q for a given unitary matrix U such that the number of gates in the quantum circuit of U=QTPTU′PQ is minimized, where U′ is equivalent to U up to a permutation. The decomposition of a unitary operator is performed by recursively applying the cosine–sine decomposition.Running time: Running time increases with the size of the unitary matrix, as well as the prescribed maximum number of iterations for qubit permutation selection and the subsequent simulated annealing algorithm. Running time estimates are provided for each example in Section 4. All simulation results presented in this paper are obtained from running the program on the Fornax supercomputer managed by iVEC@UWA with Intel Xeon X5650 CPUs.

COMPUTATIONAL GRIDS TO SOLVE LARGE SCALE OPTIMIZATION PROBLEMS WITH UNCERTAIN DATA

In this paper we discuss the use computational grids to solve stochastic optimization problems. These problems are generally difficult to solve and are often characterized by a high number of variables and constraints. Furthermore, for some applications it is required to achieve a real-time solution. Obtaining reasonable results is a difficult objective without the use of high performance computing. Here we present a grid-enabled path-following algorithm and we discuss some experimental results.

WE-G-16A-01: Evolution of Radiation Treatment Planning

WE-G-16A-01: Evolution of Radiation Treatment Planning

A Simulation Framework for Optimal Energy Storage Sizing

Despite the increasing interest in Energy Storage Systems (ESS), quantification of their technical and economical benefits remains a challenge. To assess the use of ESS, a simulation approach for ESS optimal sizing is presented. The algorithm is based on an adapted Unit Commitment, including ESS operational constraints, and the use of high performance computing (HPC). Multiple short-term simulations are carried out within a multiple year horizon. Evaluation is performed for Chile's Northern Interconnected Power System (SING). The authors show that a single year evaluation could lead to sub-optimal results when evaluating optimal ESS size. Hence, it is advisable to perform long-term evaluations of ESS. Additionally, the importance of detailed simulation for adequate assessment of ESS contributions and to fully capture storage value is also discussed. Furthermore, the robustness of the optimal sizing approach is evaluated by means of a sensitivity analyses. The results suggest that regulatory frameworks should recognize multiple value streams from storage in order to encourage greater ESS integration.

A performance/cost evaluation for a GPU-based drug discovery application on volunteer computing.

Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC) platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs) has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO). This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor.

Detailed Term Accounting calculations of plasma opacity

Radiative opacity is a key parameter to understanding the transport of energy in high energy density plasmas. Many calculations of opacity apply approximations that do not fully resolve the true line structure of the frequency dependent absorption spectrum and the accuracy of such results is uncertain for simulations containing many complex ions. The DAVROS opacity code has been developed to perform Detailed Term Accounting (DTA) calculations with the aim of resolving the fine structure resulting from the myriad line transitions due to arbitrarily complex atomic configurations. By making use of High Performance Computing (HPC), such calculations, whilst not yet routine, are now more feasible. Some illustrative results from DAVROS are presented and comparisons made with experiment data.

HPC in the Automotive Industry — Trends and Prospects

Abstract The beginnings of high-performance applications in motor vehicle development date back some 25 years. Today, virtual development has become a permanent feature of the vehicle development process. Applications with high computation time requirements, such as aerodynamics, crash safety or the combustion process require the use of high performance computers. Increasing requirements placed on motor vehicles, especially with regard to safety, lightweight construction and emissions, taking into account economic, market and competitive factors, have resulted in the increasing use of HPC applications. Currently, it can be observed that HPC requirements are doubling every 14 to 16 months. In addition to conventional structural mechanics, growth drivers here include new material and joining technologies, fluid dynamics applications, as well as robustness evaluations, optimisation processes and multi-disciplinary simulations.

Accelerating wildfire susceptibility mapping through GPGPU

In the field of wildfire risk management the so-called burn probability maps (BPMs) are increasingly used with the aim of estimating the probability of each point of a landscape to be burned under certain environmental conditions. Such BPMs are usually computed through the explicit simulation of thousands of fires using fast and accurate models. However, even adopting the most optimized algorithms, the building of simulation-based BPMs for large areas results in a highly intensive computational process that makes mandatory the use of high performance computing. In this paper, General-Purpose Computation with Graphics Processing Units (GPGPU) is applied, in conjunction with a wildfire simulation model based on the Cellular Automata approach, to the process of BPM building. Using three different GPGPU devices, the paper illustrates several implementation strategies to speedup the overall mapping process and discusses some numerical results obtained on a real landscape.

Easy use of high performance computers for fusion simulations

Fusion Modelling and Simulation are very challenging and the high performance computing issues are addressed here. Based on the framework developed by the European Integrated Tokamak Modelling project and on the EUFORIA infrastructure, a tool solving nicely these difficulties has been developed for the end users and applied to several fusion simulation cases. The first part recalls the issues with GRID and high performance computing, while the second part presents the solutions and the tool for developing easily a GRID/HPC actor. The last part reports the use of this tool in MHD equilibrium and plasma edge simulations.

Parallel Computing with a Bayesian Item Response Model

Item response theory (IRT) is a modern test theory that has been used in various aspects of educational and psychological measurement. The fully Bayesian approach shows promise for estimating IRT models. Given that it is computation- ally expensive, the procedure is limited in practical applications. It is hence important to seek ways to reduce the execution time. A suitable solution is the use of high performance computing. This study focuses on the fully Bayesian algorithm for a conventional IRT model so that it can be implemented on a high performance parallel machine. Empirical results suggest that this parallel version of the algorithm achieves a considerable speedup and thus reduces the execution time considerably.

An enumerative stepwise ansatz enables atomic-accuracy RNA loop modeling

Atomic-accuracy structure prediction of macromolecules should be achievable by optimizing a physically realistic energy function but is presently precluded by incomplete sampling of a biopolymer's many degrees of freedom. We present herein a working hypothesis, called the "stepwise ansatz," for recursively constructing well-packed atomic-detail models in small steps, enumerating several million conformations for each monomer, and covering all build-up paths. By making use of high-performance computing and the Rosetta framework, we provide first tests of this hypothesis on a benchmark of 15 RNA loop-modeling problems drawn from riboswitches, ribozymes, and the ribosome, including 10 cases that are not solvable by current knowledge-based modeling approaches. For each loop problem, this deterministic stepwise assembly method either reaches atomic accuracy or exposes flaws in Rosetta's all-atom energy function, indicating the resolution of the conformational sampling bottleneck. As a further rigorous test, we have carried out a blind all-atom prediction for a noncanonical RNA motif, the C7.2 tetraloop/receptor, and validated this model through nucleotide-resolution chemical mapping experiments. Stepwise assembly is an enumerative, ab initio build-up method that systematically outperforms existing Monte Carlo and knowledge-based methods for 3D structure prediction.

Skyrme energy-density functional approach to collective dynamics

Our recent developments for the microscopic description of nuclear collective dynamics in the framework of the time-dependent density functional theory is presented. It is shown that the quasiparticle random-phase approximation is ready for the systematic investigation of the giant resonances in the entire mass region of nuclear chart and that the collective Hamiltonian approach gives the quantitative description of the low-lying states in transitional nuclei with the Skyrme and pairing energy density functionals as a microscopic input. We put emphasis on necessity of the massive use of high performance computers to carry out such microscopic calculations.

Synthesis of scanning electron microscopy images by high performance computing for the metrology of advanced CMOS processes

Scanning electron microscopy is still the technique of choice for imaging and for in-line measurement of critical dimensions and overlay accuracy in most of the core technology processes. In particular, critical dimension microscopy provides information about design template matching and edge placement errors through links with design having proven beneficial effects on process yield and product reliability. In this paper, the use of high performance computing is demonstrated to simulate linescans and 2D secondary electron images to be used in optical proximity error correction strategies for nanometer scale technologies.

The use of high-performance and high-throughput computing for the fertilization of digital earth and global change studies

The study of global climate change seeks to understand: (1) the components of the Earth's varying environmental system, with a particular focus on climate; (2) how these components interact to determine present conditions; (3) the factors driving these components; (4) the history of global change and the projection of future change; and (5) how knowledge about global environmental variability and change can be applied to present-day and future decision-making. This paper addresses the use of high-performance computing and high-throughput computing for a global change study on the Digital Earth (DE) platform. Two aspects of the use of high-performance computing (HPC)/high-throughput computing (HTC) on the DE platform are the processing of data from all sources, especially Earth observation data, and the simulation of global change models. The HPC/HTC is an essential and efficient tool for the processing of vast amounts of global data, especially Earth observation data. The current trend involves running complex global climate models using potentially millions of personal computers to achieve better climate change predictions than would ever be possible using the supercomputers currently available to scientists.

HPDGIS 2010 workshop report

The increasingly computational and data intensive nature of geospatial problem solving and the requirement to provide collaborative support across numerous fields, necessitates the use of high performance computing and distributed systems which have emerged as prominent elements in the landscape of computing and information technologies. The inaugural ACM SIGSPATIAL HPDGIS workshop was hence launched to bring together researchers and practitioners to map out the fundamental research areas centered on synergistic advances in Geographic Information Systems (GIS) and spatial analysis, high performance computing, and distributed systems.

Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers

The use of High Performance Computing (HPC) in commercial and consumer IT applications is becoming popular. HPC users need the ability to gain rapid and scalable access to high-end computing capabilities. Cloud computing promises to deliver such a computing infrastructure using data centers so that HPC users can access applications and data from a Cloud anywhere in the world on demand and pay based on what they use. However, the growing demand drastically increases the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high energy cost which will reduce the profit margin of Cloud providers, but also high carbon emissions which are not environmentally sustainable. Hence, there is an urgent need for energy-efficient solutions that can address the high increase in the energy consumption from the perspective of not only the Cloud provider, but also from the environment. To address this issue, we propose near-optimal scheduling policies that exploit heterogeneity across multiple data centers for a Cloud provider. We consider a number of energy efficiency factors (such as energy cost, carbon emission rate, workload, and CPU power efficiency) which change across different data centers depending on their location, architectural design, and management system. Our carbon/energy based scheduling policies are able to achieve on average up to 25% of energy savings in comparison to profit based scheduling policies leading to higher profit and less carbon emissions.