In this paper, we evaluate the error criticality of radiation-induced errors on modern High-Performance Computing (HPC) accelerators (Intel Xeon Phi and NVIDIA K40) through a dedicated set of metrics. We show that, as long as imprecise computing is concerned, the simple mismatch detection is not sufficient to evaluate and compare the radiation sensitivity of HPC devices and algorithms. Our analysis quantifies and qualifies radiation effects on applications' output correlating the number of corrupted elements with their spatial locality. Also, we provide the mean relative error (dataset-wise) to evaluate radiation-induced error magnitude. We apply the selected metrics to experimental results obtained in various radiation test campaigns for a total of more than 400 hours of beam time per device. The amount of data we gathered allows us to evaluate the error criticality of a representative set of algorithms from HPC suites. Additionally, based on the characteristics of the tested algorithms, we draw generic reliability conclusions for broader classes of codes. We show that arithmetic operations are less critical for the K40, while Xeon Phi is more reliable when executing particles interactions solved through Finite Difference Methods. Finally, iterative stencil operations seem the most reliable on both architectures.

During the last years, High Performance Computing (HPC) resources have undergone a dramatic transformation, with an explosion on the available parallelism and the use of special purpose processors. There are international initiatives focusing on redesigning hardware and software in order to achieve the Exaflop capability. With this aim, the HPC4E project is applying the new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale simulations for different energy sources that are the present and the future of energy: wind energy production and design, efficient combustion systems for biomass-derived fuels (biogas), and exploration geophysics for hydrocarbon reservoirs. HPC4E joins efforts of several institutions settled in Brazil and Europe.

Within the fusion community, a large number of codes are in use to simulate various aspects of the plasma behaviour. Many codes have been written by physicists with a big emphasis on the physics without using the latest technologies available in computer science. For improving this situation, a project with acronym EUFORIA was created. The main target of this project is to increase the performance of key existing codes, either parallelizing the sequential codes or improving their parallelization. However, in general these codes were not thought for new supercomputers based on muticore architectures and only take advantage of the task level parallelism, basically using Message Passing Interface (MPI). In this paper we discuss several possible ways to apply hybrid parallelization techniques in fusion codes for exploiting new multiprocessor supercomputers under development.

Oil and gas companies trust Reverse Time Migration (RTM), the most advanced seismic imaging technique, with crucial decisions on drilling investments. The economic value of the oil reserves that require RTM to be localized is in the order of 10^{13} dollars. But RTM requires vast computational power, which somewhat hindered its practical success. Although, accelerator-based architectures deliver enormous computational power, little attention has been devoted to assess the RTM implementations effort. The aim of this paper is to identify the major limitations imposed by different accelerators during RTM implementations, and potential bottlenecks regarding architecture features. Moreover, we suggest a wish list, that from our experience, should be included as features in the next generation of accelerators, to cope with the requirements of applications like RTM. We present an RTM algorithm mapping to the IBM Cell/B.E., NVIDIA Tesla and an FPGA platform modeled after the Convey HC-1. All three implementations outperform a traditional processor (Intel Harpertown) in terms of performance (10x), but at the cost of huge development effort, mainly due to immature development frameworks and lack of well-suited programming models. These results show that accelerators are well positioned platforms for this kind of workload. Due to the fact that our RTM implementation is based on an explicit high order finite difference scheme, some of the conclusions of this work can be extrapolated to applications with similar numerical scheme, for instance, magneto-hydrodynamics or atmospheric flow simulations.

The Integrated Tokamak Modelling Task Force (ITM-TF) is developing an infrastructure where the validation needs, as being formulated in terms of multi-device data access and detailed physics comparisons aiming for inclusion of synthetic diagnostics in the simulation chain, are key components. A device independent approach to data transport and a standardized approach to data management (data structures, naming, and access) is being developed in order to allow cross validation between different fusion devices using a single toolset. The effort is focused on ITER plasmas and ITER scenario development on current fusion device. The modeling tools are, however, aimed for general use and can be promoted in other areas of modelling as well. Extensive work has already gone into the development of standardized descriptions of the data (Consistent Physical Objects) providing initial steps towards a complete fusion modelling ontology. The longer term aim is a complete simulation platform which is expected to last and be extended in different ways for the coming 30 years. The technical underpinning is therefore of vital importance. In particular, the platform needs to be extensible and open-ended to be able to take full advantage of not only today's most advanced technologies but also be able to marshal future developments. A full level comprehensive prediction of ITER physics rapidly becomes expensive in terms of computing resources and may cover a range of computing paradigms. The simulation framework therefore needs to be able to use both grid and HPC computing facilities. Hence, data access and code coupling technologies are required to be available for a heterogeneous, possibly distributed, environment. The developments in this area are pursued in a separate project - EUFORIA (EU Fusion for ITER Applications). The current status of ITM-TF and EUFORIA is presented and discussed.

Indirect addressing is known for being slow on conventional architectures, due to the extra step of gathering data before computations can be done. There have been proposed many methods for optimizing indirect addressing. However, these almost exclusively, merely try to change the order in which data is accessed, so as to better utilize the cache. Furthermore, vector instructions can not be used, since data is not accessed continuously, and therefore valuable processing power can not be exploited. The Cell/B.E. architecture has multiple powerful DMA engines, suitable for gathering scattered data. Unfortunately, at fine data granularity, they have several constraints which make them inefficient. In this paper, a novel solution called DMA list Interlacing (DLI) is explored, which overcomes the DMA constraints and enables the usage of vector instructions, without any extra effort. It is shown that DLI can achieve speedups of several orders of magnitude, compared to conventional processors.

Abstract This work focuses on the use of computational Grids for processing the large set of jobs arising in parameter sweep applications. In particular, we tackle the mapping of molecular potential energy hypersurfaces. For computationally intensive parameter sweep problems, performance models are developed to compare the parallel computation in a multiprocessor system with the computation on an Internet‐based Grid of computers. We find that the relative performance of the Grid approach increases with the number of processors, being independent of the number of jobs. The experimental data, obtained using electronic structure calculations, fit the proposed performance expressions accurately. To automate the mapping of potential energy hypersurfaces, an application based on GRID superscalar is developed. It is tested on the prototypical case of the internal dynamics of acetone. Copyright © 2006 John Wiley & Sons, Ltd.

We present a parallel algebraic preconditioner for the Schur complement system. This preconditioner aims to be a robust black box solver for commercial Finite Element packages. The preconditioner is based in a strongly dropped factorization of the matrices. Two levels of parallelism are exploited using PVM and openMP. The preconditioner is tested with an scalar convection-diffusion equation, and a set of industrial test cases arising from the finite element package PERMAS and the Davis collection. The experimental results shown the robustness of it preconditioner, and it has a high scalability. We obtain quasi-linear spped-up until 32 processors.