Use Of High Performance Computing Research Articles

Portability Study of an OpenCL Algorithm for Automatic Target Detection in Hyperspectral Images

In the last decades, the problem of target detection has received considerable attention in remote sensing applications. When this problem is tackled using hyperspectral images with hundreds of bands, the use of high-performance computing (HPC) is essential. One of the most popular algorithms in the hyperspectral image analysis community for this purpose is the automatic target detection and classification algorithm (ATDCA). Previous research has already investigated the mapping of ATDCA on HPC platforms such as multicore processors, graphics processing units (GPUs), and field-programmable gate arrays (FPGAs), showing impressive speedup factors (after careful fine-tuning) that allow for its exploitation in time-critical scenarios. However, the lack of standardization resulted in most implementations being too specific to a given architecture, eliminating (or at least making extremely difficult) code reusability across different platforms. In order to address this issue, we present a portability study of an implementation of ATDCA developed using the open computing language (OpenCL). We focus on cross-platform parameters such as performance, energy consumption, and code design complexity, as compared to previously developed (hand-tuned) implementations. Our portability study analyzes different strategies to expose data parallelism as well as enable the efficient exploitation of complex memory hierarchies in heterogeneous devices. We also conduct an assessment of energy consumption and discuss metrics to analyze the quality of our code. The conducted experiments—using synthetic and real hyperspectral data sets collected by the Hyperspectral Digital Imagery Collection Experiment (HYDICE) and NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS)—demonstrate, for the first time in the literature, that portability across different HPC platforms can be achieved for real-time target detection in hyperspectral missions.

SPH Modeling of Water-Related Natural Hazards

This paper collects some recent smoothed particle hydrodynamic (SPH) applications in the field of natural hazards connected to rapidly varied flows of both water and dense granular mixtures including sediment erosion and bed load transport. The paper gathers together and outlines the basic aspects of some relevant works dealing with flooding on complex topography, sediment scouring, fast landslide dynamics, and induced surge wave. Additionally, the preliminary results of a new study regarding the post-failure dynamics of rainfall-induced shallow landslide are presented. The paper also shows the latest advances in the use of high performance computing (HPC) techniques to accelerate computational fluid dynamic (CFD) codes through the efficient use of current computational resources. This aspect is extremely important when simulating complex three-dimensional problems that require a high computational cost and are generally involved in the modeling of water-related natural hazards of practical interest. The paper provides an overview of some widespread SPH free open source software (FOSS) codes applied to multiphase problems of theoretical and practical interest in the field of hydraulic engineering. The paper aims to provide insight into the SPH modeling of some relevant physical aspects involved in water-related natural hazards (e.g., sediment erosion and non-Newtonian rheology). The future perspectives of SPH in this application field are finally pointed out.

HYPERDOCK: Improving virtual screening through parallel hyperheuristics

Virtual screening (VS) methods aid clinical research by predicting the interaction of ligands with pharmacological targets. The computational requirements of VS, along with the size of the databases, propitiate the use of high-performance computing. METADOCK is a tool for the application of metaheuristics to VS in heterogeneous clusters of computers based on central processing unit (CPU) and graphics processing unit (GPU). HYPERDOCK represents a step forward; the exploration for satisfactory metaheuristics is systematically approached by means of hyperheuristics working on top of the metaheuristic schema of METADOCK. Multiple metaheuristics are explored, so the process is computationally demanding. HYPERDOCK exploits the parallelism of METADOCK and includes parallelism at its own level. The different levels of parallelism can be used to exploit the parallelism offered by computational systems composed of multicore CPU + multi-GPUs. The efficient exploitation of these systems enables HYPERDOCK to improve ligand–receptor binding.

Provenance-based fault tolerance technique recommendation for cloud-based scientific workflows: a practical approach

Scientific workflows are abstractions composed of activities, data and dependencies that model a computer simulation and are managed by complex engines named scientific workflow management system (SWfMS). Many workflows demand many computational resources once their executions may involve a number of different programs processing a massive volume of data. Thus, the use of high-performance computing (HPC) and data-intensive scalable computing environments allied to parallelization techniques provides the necessary support for the execution of such workflows. Clouds are environments that already offer HPC capabilities and workflows can explore them. Although clouds offer advantages such as elasticity and availability, failures are a reality rather than a possibility in this environment. Thus, existing SWfMS must be fault-tolerant. There are several types of fault tolerance techniques used in SWfMS such as Checkpoint/Restart, Re-Execution and Over-provisioning, but it is far from trivial to choose the suitable fault tolerance technique for a workflow execution that is not going to jeopardize the parallel execution. The major problem is that the suitable fault tolerance technique may be different for each workflow, activity or activation since programs associated with activities may present different behaviors. This article aims at analyzing several fault-tolerance techniques in a cloud-based SWfMS named SciCumulus, and recommend the suitable one for user’s workflow activities and activations using machine learning techniques and provenance data, thus aiming at improving resiliency.

Shape optimization of wave energy converters for broadband directional incident waves

Here, through a systematic methodology and the use of high performance computing, we calculate the optimum shape for a wave energy converter under the action of incident waves of (i) monochromatic unidirectional, (ii) monochromatic directional, (iii) polychromatic unidirectional and (iv) polychromatic directional (with both directional symmetry and asymmetry). As a benchmark for our study, without loss of generality, we consider a submerged planar pressure differential wave energy converter, and use the Genetic Algorithm to search through a wide range of shapes. A new parametric description of absorber shape based on Fourier decomposition of geometrical shapes is introduced, and for each shape hydrodynamic coefficients are calculated, optimum power take-off parameters are obtained, and overall efficiency is determined. We show that an optimum geometry of the absorber plate can absorb significantly higher energy (in some cases few times higher) when compared to a circular shape of the same area. Specifically, for a unidirectional incident wave, the optimum shape, as expected, is obtained to be the most elongated shape. For directional incident waves, a butterfly-shape is the optimum geometry whose details depend on not only the amplitude and direction of incident wave components, but also the relative phases of those components. For the latter effect, we find an optimally averaged profile through a statistical analysis.

Impact of using multi-levels of parallelism on HPC applications performance hosted on Azure cloud computing

The use of high-performance computing (HPC) applications has increased progressively in scientific research and industry. Cloud computing attracts HPC users because of its extreme cost efficiency. The reduced cost is the result of the successful employment of multilayer-virtualisation enabling dynamic elastic resource-sharing between different tenants. In this paper, we evaluate the impact of using multi-levels of parallelism on computationally intensive parallel tasks hosted on a cloud virtualised HPC cluster. We use multi-levels of parallelism through a set of experiments employing both message passing and multi-threading techniques. Our evaluation addresses two main perspectives, the performance of applications and cost of running HPC applications on clouds. We use millions of operations per seconds (MOPS) and speed-up to evaluate the computational performance. To evaluate the cost we use United States Dollar/MOPS (USD/MOPS). The experiments on two different clouds are compared against each other and with published results for Amazon EC2 cloud. Results show that balancing the workload between processes and threads per process is the key factor to maintain high performance with reasonable cost.

Training bioinformaticians in High Performance Computing

In the last decade, bioinformatics has become an indispensable branch of modern science research, experiencing an explosion in financial support, developed applications and data collection. The growth of the datasets that are emerging from research laboratories, industry, the health sector, etc., are increasingly raising the levels of demand in computing power and storage. Processing biological data, in the large scales of these datasets, often requires the use of High Performance Computing (HPC) resources, especially when dealing with certain types of omics data, such as genomic and metagenomic data. Such computational resources not only require substantial investments, but they also involve high maintenance costs. More importantly, in order to keep good returns from the investments, specific training needs to be put in place to ensure that wasting is minimized. Furthermore, given that bioinformatics is a highly interdisciplinary field where several other domains intersect (such as biology, chemistry, physics and computer science), researchers from these areas also require bioinformatics-specific training in HPC, in order to fully take advantage of supercomputing centers. In this document, we describe our experience in training researchers from several different disciplines in HPC, as applied to bioinformatics under the framework of the leading European bioinformatics platform ELIXIR, and analyze both the content and outcomes of the course.

A GPU Implementation of the Correlation Technique for Real-time Fourier Domain Pulsar Acceleration Searches

Abstract The study of binary pulsars enables tests of general relativity. Orbital motion in binary systems causes the apparent pulsar spin frequency to drift, reducing the sensitivity of periodicity searches. Acceleration searches are methods that account for the effect of orbital acceleration. Existing methods are currently computationally expensive, and the vast amount of data that will be produced by next-generation instruments such as the Square Kilometre Array necessitates real-time acceleration searches, which in turn requires the use of high-performance computing (HPC) platforms. We present our implementation of the correlation technique for the Fourier Domain Acceleration Search (FDAS) algorithm on Graphics Processor Units (GPUs). The correlation technique is applied as a convolution with multiple finite impulse response (FIR) filters in the Fourier domain. Two approaches are compared: the first uses the NVIDIA cuFFT library for applying Fast Fourier transforms (FFTs) on the GPU, and the second contains a custom FFT implementation in GPU shared memory. We find that the FFT shared-memory implementation performs between 1.5 and 3.2 times faster than our cuFFT-based application for smaller but sufficient filter sizes. It is also 4–6 times faster than the existing GPU and OpenMP implementations of FDAS. This work is part of the AstroAccelerate project, a many-core accelerated time-domain signal-processing library for radio astronomy.

NSF’s Inaugural Software Institutes: The Science Gateways Community Institute and the Molecular Sciences Software Institute

The National Strategic Computing Initiative (NSCI) creates a framework for partnerships among government, industry, and academia to advance the use of high-performance computing. The National Science Foundations Office of Advanced Cyberinfrastructure furthers this mission through the instantiation of two new software institutes. Although the program predates NSCI, The Science Gateways Community Institute and the Molecular Sciences Software Institute advance NSCIs objectives by acting as hubs of excellence, serving broad communities and creating software and workforce ecosystems.

Mission Innovation task force reports on enabling Gigatonne-scale CO2 storage

Mission Innovation task force reports on enabling Gigatonne-scale CO2 storage

Risk-averse structural topology optimization under random fields using stochastic expansion methods

This work proposes a level-set based approach for solving risk-averse structural topology optimization problems considering random field loading and material uncertainty. The use of random fields increases the dimensionality of the stochastic domain, which poses several computational challenges related to the minimization of the Excess Probability as a measure of risk awareness. This problem is addressed both from the theoretical and numerical viewpoints. First, an existence result under a typical geometrical constraint on the set of admissible shapes is proved. Second, a level-set continuous approach to find the numerical solution of the problem is proposed. Since the considered cost functional has a discontinuous integrand, the numerical approximation of the functional and its sensitivity combine an adaptive anisotropic Polynomial Chaos (PC) approach with a Monte-Carlo (MC) sampling method for uncertainty propagation. Furthermore, to address the increment of dimensionality induced by the random field, an anisotropic sparse grid stochastic collocation method is used for the efficient computation of the PC coefficients. A key point is that the non-intrusive nature of such an approach facilitates the use of High Performance Computing (HPC) to alleviate the computational burden of the problem. Several numerical experiments including random field loading and material uncertainty are presented to show the feasibility of the proposal.

A Parallel Finite Element Method for 3D Two-Phase Moving Contact Line Problems in Complex Domains

Moving contact line problem plays an important role in fluid-fluid interface motion on solid surfaces. The problem can be described by a phase-field model consisting of the coupled Cahn–Hilliard and Navier–Stokes equations with the generalized Navier boundary condition (GNBC). Accurate simulation of the interface and contact line motion requires very fine meshes, and the computation in 3D is even more challenging. Thus, the use of high performance computers and scalable parallel algorithms are indispensable. In this paper, we generalize the GNBC to surfaces with complex geometry and introduce a finite element method on unstructured 3D meshes with a semi-implicit time integration scheme. A highly parallel solution strategy using different solvers for different components of the discretization is presented. More precisely, we apply a restricted additive Schwarz preconditioned GMRES method to solve the systems arising from implicit discretization of the Cahn–Hilliard equation and the velocity equation, and an algebraic multigrid preconditioned CG method to solve the pressure Poisson system. Numerical experiments show that the strategy is efficient and scalable for 3D problems with complex geometry and on a supercomputer with a large number of processors.

Parallelizing Multi-featured Content Based Search and Retrieval of Videos through High Performance Computing

<p>Video Retrieval is an important technology that helps to design video search engines and allow users to browse and retrieve videos of interest from huge databases. Though, there are many existing techniques to search and retrieve videos based on spatial and temporal features but are unable to perform well resulting in high ranking of irrelevant videos leading to poor user satisfaction. In this paper an efficient multi-featured method for matching and extraction is proposed in parallel paradigm to retrieve videos accurately and quickly from the collection. Proposed system is tested on datasets that contains various categories of videos of varying length such as traffic, sports, nature etc. Experimental results show that around 80% of accuracy is achieved in searching and retrieving video. Through the use of high performance computing, the parallel execution performs 5 times faster in locating and retrieving videos of intrest than the sequential execution.</p>

Impact of using multi-levels of parallelism on HPC applications performance hosted on Azure cloud computing

The use of high-performance computing (HPC) applications has increased progressively in scientific research and industry. Cloud computing attracts HPC users because of its extreme cost efficiency. The reduced cost is the result of the successful employment of multilayer-virtualisation enabling dynamic elastic resource-sharing between different tenants. In this paper, we evaluate the impact of using multi-levels of parallelism on computationally intensive parallel tasks hosted on a cloud virtualised HPC cluster. We use multi-levels of parallelism through a set of experiments employing both message passing and multi-threading techniques. Our evaluation addresses two main perspectives, the performance of applications and cost of running HPC applications on clouds. We use millions of operations per seconds (MOPS) and speed-up to evaluate the computational performance. To evaluate the cost we use United States Dollar/MOPS (USD/MOPS). The experiments on two different clouds are compared against each other and with published results for Amazon EC2 cloud. Results show that balancing the workload between processes and threads per process is the key factor to maintain high performance with reasonable cost.

Accelerating the problem of microrheology in colloidal systems on a GPU

Complex fluids are characterized with both solid and fluid properties by their elasticity and viscosity, or rheological behaviour. The flow of complex fluids is a focus of interest in a very wide range of applications in biophysics and soft matter, such as problems involving live cells, processing of plastic, glass, paints, foods, oil recovery and so on. Recently, microrheology has been developed as an accurate technique to obtain rheological properties in soft matter from the microscopic motion of colloidal tracers used as probes, either freely diffusing in the host medium (passive), or subjected to external forces (active). A drawback for the models that simulate these techniques is their high computational cost. Therefore, the use of high performance computing is mandatory to develop the microrheology models. In this work, a microrheology model based on simulations of a tracer in a bath of Brownian quasi-hard spheres is proposed and parallelized. The analysis of the results of the evaluation using different sizes of the problem shows that GPU programming is very appropriate to accelerate these kinds of models.

Emotional robot control architecture implementation using FPGAs

Embedded robotic computing system.Design of a FPGA-based emotional co-processor.Emotional architecture for robotic agents. Emotional control architectures are becoming promising solutions for the design of the robots of the future. The emotions motivate the robot behaviors to fulfill the objectives. The workload of the emotions increases with the complexity of the applications, which reduces the capacity of the processor to undertake more complex problems. A simple but costly solution would be the use of high performance computers to solve the complex problems. However, taking advantage of the potential parallelism of the emotions, this paper proposes a less expensive solution using low cost Cyclone FPGAs. Different FPGA devices are compared to analyze their suitability to cope with different industrial mobile robotic applications. The experiments are conducted taking into account different environmental conditions, robot dynamics and emotional states. The results of the evaluation show that Cyclone V outperforms the original RT-SW solution in 5 times and can undertake the 97% of the problems of the robot, while Cyclone IV improves the performances in about 4 times solving medium and few complex problems. Cyclone 2 and 3 double the performance of the RT-SW and are useful for simple robotic applications.

Guest Editorial: Engineering Design in the “Lower for Longer” Environment

Guest editorial A recurring theme in the earning statements of oil and gas companies in recent months is the desire to survive not only “lower (prices) for longer,” but to use the current market downturn, and the resulting urgency, as an opportunity to reshape organizations to ensure that they are stronger and better able to thrive in the years ahead. Clearly, the industry needs to reduce costs and continue to innovate without compromising safety, but how? In other industry crunches, such as that experienced by automotive manufacturers in 2008–09, simulation played a key role in helping to reduce engineering design costs and lead times, while allowing manufacturers to continue to deliver new and innovative solutions. While each company’s view on how to tackle this new paradigm will differ, for many it will be to continue the day job (deliver quality and innovative products and solutions), but at reduced development time and cost, without compromising safety. First principles-based engineering simulation tools will be a key enabler, as companies will increasingly rely on simulation-based design exploration. The initial driver will be to reduce engineering costs and time, but companies will also benefit from simulation as an innovation enabler. Key to this has been the rapid improvement in computer hardware and software technology, as well as innovative licensing models that enable, rather than penalize, the use of high-performance computing and cloud solutions. Through the development of cloud computing, engineers now have ready and cost-effective access to computer resources that would have been unimaginable a decade ago. These improvements allow engineers to increase the number of design and operating conditions evaluated, and to move beyond the physical test to simulate real-world conditions at full scale, such as subsea separators with real process fluids at high temperature and pressure, or the behavior of a floating platform during a hurricane. The importance of simulation is evident in three specific examples: two from the recent CD-adapco Oil, Gas, and Chemical Computational Fluid Dynamics (CFD) Conference, and one from outside our industry.

An intersecting chord method for minimum circumscribed sphere and maximum inscribed sphere evaluations of sphericity error

As one of the Geometrical Product Specifications that are widely applied in industrial manufacturing and measurement, sphericity error can synthetically scale a 3D structure and reflects the machining quality of a spherical workpiece. Following increasing demands in the high motion performance of spherical parts, sphericity error is becoming an indispensable component in the evaluation of form error. However, the evaluation of sphericity error is still considered to be a complex mathematical issue, and the related research studies on the development of available models are lacking. In this paper, an intersecting chord method is first proposed to solve the minimum circumscribed sphere and maximum inscribed sphere evaluations of sphericity error. This new modelling method leverages chord relationships to replace the characteristic points, thereby significantly reducing the computational complexity and improving the computational efficiency. Using the intersecting chords to generate a virtual centre, the reference sphere in two concentric spheres is simplified as a space intersecting structure. The position of the virtual centre on the space intersecting structure is determined by characteristic chords, which may reduce the deviation between the virtual centre and the centre of the reference sphere. In addition,two experiments are used to verify the effectiveness of the proposed method with real datasets from the Cartesian coordinates. The results indicate that the estimated errors are in perfect agreement with those of the published methods. Meanwhile, the computational efficiency is improved. For the evaluation of the sphericity error, the use of high performance computing is a remarkable change.

Optimizing performance-per-watt on GPUs in high performance computing

The magnitude of the real-time digital signal processing challenge attached to large radio astronomical antenna arrays motivates use of high performance computing (HPC) systems. The need for high power efficiency (performance per watt) at remote observatory sites parallels that in HPC broadly, where efficiency is an emerging critical metric. We investigate how the performance per watt of graphics processing units (GPUs) is affected by temperature, core clock frequency and voltage. Our results highlight how the underlying physical processes that govern transistor operation affect power efficiency. In particular, we show experimentally that GPU power consumption grows non-linearly with both temperature and supply voltage, as predicted by physical transistor models. We show lowering GPU supply voltage and increasing clock frequency while maintaining a low die temperature increases the power efficiency of an NVIDIA K20 GPU by up to 37-48% over default settings when running xGPU, a compute-bound code used in radio astronomy. We discuss how temperature-aware power models could be used to reduce power consumption for future HPC installations. Automatic temperature-aware and application-dependent voltage and frequency scaling (T-DVFS and A-DVFS) may provide a mechanism to achieve better power efficiency for a wider range of codes running on GPUs

Review: High-performance computing to detect epistasis in genome scale data sets.

It is becoming clear that most human diseases have a complex etiology that cannot be explained by single nucleotide polymorphisms (SNPs) or simple additive combinations; the general consensus is that they are caused by combinations of multiple genetic variations. The limited success of some genome-wide association studies is partly a result of this focus on single genetic markers. A more promising approach is to take into account epistasis, by considering the association of multiple SNP interactions with disease. However, as genomic data continues to grow in resolution, and genome and exome sequencing become more established, the number of combinations of variants to consider increases rapidly. Two potential solutions should be considered: the use of high-performance computing, which allows us to consider a larger number of variables, and heuristics to make the solution more tractable, essential in the case of genome sequencing. In this review, we look at different computational methods to analyse epistatic interactions within disease-related genetic data sets created by microarray technology. We also review efforts to use epistatic analysis results to produce biomarkers for diagnostic tests and give our views on future directions in this field in light of advances in sequencing technology and variants in non-coding regions.