José María Cela

Assessing Accelerator-Based HPC Reverse Time Migration

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 1013 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.

Stability of three-dimensional relativistic jets: implications for jet collimation

Context. The stable propagation of jets in FRII sources is remarkable if one takes into account that large-scale jets are subjected to potentially highly disruptive three-dimensional (3D) Kelvin-Helmholtz instabilities. Aims. Numerical simulations can address this problem and help clarify the causes of this remarkable stability. Following previous studies of the stability of relativistic flows in two dimensions (2D), it is our aim to test and extend the conclusions of such works to three dimensions. Methods. We present numerical simulations for the study of the stability properties of 3D, sheared, relativistic flows. This work uses a fully parallelized code (Ratpenat) that solves equations of relativistic hydrodynamics in 3D. Results. The results of the present simulations confirm those in 2D. We conclude that the growth of resonant modes in sheared relativistic flows could be important in explaining the long-term collimation of extragalactic jets.

3D seismic imaging through reverse-time migration on homogeneous and heterogeneous multi-core processors

Reverse-Time Migration (RTM) is a state-of-the-art technique in seismic acoustic imaging, because of the quality and integrity of the images it provides. Oil and gas companies trust RTM with crucial decisions on multi-million-dollar drilling investments. But RTM requires vastly more computational power than its predecessor techniques, and this has somewhat hindered its practical success. On the other hand, despite multi-core architectures promise to deliver unprecedented computational power, little attention has been devoted to mapping efficiently RTM to multi-cores. In this paper, we present a mapping of the RTM computational kernel to the IBM Cell/B.E. processor that reaches close-to-optimal performance. The kernel proves to be memory-bound and it achieves a 98% utilization of the peak memory bandwidth. Our Cell/B.E. implementation outperforms a traditional processor (PowerPC 970MP) in terms of performance (with an 15.0× speedup) and energy-efficiency (with a 10.0× increase in the GFlops/W delivered). Also, it is the fastest RTM implementation available to the best of our knowledge. These results increase the practical usability of RTM. Also, the RTM-Cell/B.E. combination proves to be a strong competitor in the seismic arena.

Alya: Multiphysics engineering simulation toward exascale

Alya is a multi-physics simulation code developed at Barcelona Supercomputing Center (BSC). From its inception Alya code is designed using advanced High Performance Computing programming techniques to solve coupled problems on supercomputers efficiently. The target domain is engineering, with all its particular features: complex geometries and unstructured meshes, coupled multi-physics with exotic coupling schemes and physical models, ill-posed problems, flexibility needs for rapidly including new models, etc. Since its beginnings in 2004, Alya has scaled well in an increasing number of processors when solving single-physics problems such as fluid mechanics, solid mechanics, acoustics, etc. Over time, we have made a concerted effort to maintain and even improve scalability for multi-physics problems. This poses challenges on multiple fronts, including: numerical models, parallel implementation, physical coupling models, algorithms and solution schemes, meshing process, etc. In this paper, we introduce Alyas main features and focus particularly on its solvers. We present Alyas performance up to 100.000 processors in Blue Waters, the NCSA supercomputer with selected multi-physics tests that are representative of the engineering world. The tests are incompressible flow in a human respiratory system, low Mach combustion problem in a kiln furnace, and coupled electro-mechanical contraction of the heart. We show scalability plots for all cases and discuss all aspects of such simulations, including solver convergence.

Finite-difference staggered grids in GPUs for anisotropic elastic wave propagation simulation

The 3D elastic wave equations can be used to simulate the physics of waves traveling through the Earth more precisely than acoustic approximations. However, this improvement in quality has a counterpart in the cost of the numerical scheme. A possible strategy to mitigate that expense is using specialized, high-performing architectures such as GPUs. Nevertheless, porting and optimizing a code for such a platform require a deep understanding of both the underlying hardware architecture and the algorithm at hand. Furthermore, for very large problems, multiple GPUs must work concurrently, which adds yet another layer of complexity to the codes. In this work, we have tackled the problem of porting and optimizing a 3D elastic wave propagation engine which supports both standard- and fully-staggered grids to multi-GPU clusters. At the single GPU level, we have proposed and evaluated many optimization strategies and adopted the best performing ones for our final code. At the distributed memory level, a domain decomposition approach has been used which allows for good scalability thanks to using asynchronous communications and I/O. HighlightsWe use staggered grids to simulate an elastic wave propagation across the earth.As the performance is critical, we make use of GPUs to accelerate the simulation.We show how the work is distributed among the nodes in a domain decomposition case.Regarding the complexity of the simulation, we obtain speed-ups from 10i? to 14i?.

Modulated phase matching and high-order harmonic enhancement mediated by the carrier-envelope phase

The process of high-order harmonic generation in gases is numerically investigated in the presence of a few-cycle pulsed-Bessel-beam pump, featuring a periodic modulation in the peak intensity due to large carrier-envelope-phase mismatch. A two-decade enhancement in the conversion efficiency is observed and interpreted as the consequence of a mechanism known as a nonlinearly induced modulation in the phase mismatch.

High-Performance Reverse Time Migration on GPU

Partial Differential Equations (PDE) are the heart of most simulations in many scientific fields, from Fluid Mechanics to Astrophysics. One the most popular mathematical schemes to solve a PDE is Finite Difference (FD). In this work we map a PDE-FD algorithm called Reverse Time Migration to a GPU using CUDA. This seismic imaging (Geophysics) algorithm is widely used in the oil industry. GPUs are natural contenders in the aftermath of the clock race, in particular for High-performance Computing (HPC). Due to GPU characteristics, the parallelism paradigm shifts from the classical threads plus SIMD to Single Program Multiple Data (SPMD). The NVIDIA GTX 280 implementation outperforms homogeneous CPUs up to 9x (Intel Harpertown E5420) and up to 14x (IBM PPC 970). These preliminary results confirm that GPUs are a real option for HPC, from performance to programmability.

Evaluation of 3D RTM On HPC Platforms

Reverse Time Migration (RTM) has become the latest chapter in seismic imaging for geologically complex subsurface areas. In particular has proven to be very useful for the subsaly oil plays of the US Gulf of Mexico. However, RTM cannot be applied extensively due to the extreme computational demand. The recent availability of multi-core processors, homogeneous and heterogeneous, may provide the required compute power. In this paper, we benchmark an effective RTM algorithm on several HPC platforms to assess viability of hardware.

Hybrid MPI-OpenMP performance in massively parallel computational fluid dynamics

We analyze in this paper the parallel performance of a computational mechanics code, based on a hybrid MPI-OpenMP parallelization. The specific problems considered are an explicit compressible flow solver and an implicit incompressible flow solver. Their performance using a fully MPI approach and a Hybrid approach are compared. The performance obtained on different architextures are also compared.

Edge-based electric field formulation in 3D CSEM simulations: A parallel approach

This paper presents a parallel computing scheme for the data computation that arise when applying one of the most popular electromagnetic methods in exploration geophysics, namely, controlled-source electromagnetic (CSEM). The computational approach is based on linear edge finite element method in 3D isotropic domains. The total electromagnetic field is decomposed into primary and secondary electromagnetic field. The primary field is calculated analytically using an horizontal layered-earth model and the secondary field is discretized by linear edge finite element method. We pre-calculated the primary field through of an embarrassingly-parallel framework in order to exploit the parallelism and the advantages of geometric flexibility. The numerical-computational formulation presented here is able to work with three different orientations for the dipole or excitation source. Our code is implemented on unstructured tetrahedral meshes because are able to represent complex geological structures and they allow local refinement in order to improve the solutions accuracy. The codes performance is studied through a test of scalability.