Energy Efficient Seismic Wave Propagation Simulation on a Low-power Manycore Processor.

International audienceLarge-scale simulation of seismic wave propagation is an active research topic. Its high demand for processing power makes it a good match for High Performance Computing (HPC). Although we have observed a steady increase on the processing capabilities of HPC platforms, their energy efficiency is still lacking behind. In this paper, we analyze the use of a low-power manycore processor, the MPPA-256, for seismic wave propagation simulations. First we look at its peculiar characteristics such as limited amount of on-chip memory and describe the intricate solution we brought forth to deal with this processor's idiosyncrasies. Next, we compare the performance and energy efficiency of seismic wave propagation on MPPA-256 to other commonplace platforms such as general-purpose processors and a GPU. Finally, we wrap up with the conclusion that, even if MPPA-256 presents an increased software development complexity, it can indeed be used as an energy efficient alternative to current HPC platforms, resulting in up to 71% and 5.18x less energy than a GPU and a general-purpose processor, respectively

Alternating direction implicit time integrations for finite difference acoustic wave propagation: Parallelization and convergence

This work studies the parallelization and empirical convergence of two finite difference acoustic wave propagation methods on 2-D rectangular grids, that use the same alternating direction implicit (ADI) time integration. This ADI integration is based on a second-order implicit Crank-Nicolson temporal discretization that is factored out by a Peaceman-Rachford decomposition of the time and space equation terms. In space, these methods highly diverge and apply different fourth-order accurate differentiation techniques. The first method uses compact finite differences (CFD) on nodal meshes that requires solving tridiagonal linear systems along each grid line, while the second one employs staggered-grid mimetic finite differences (MFD). For each method, we implement three parallel versions: (i) a multithreaded code in Octave, (ii) a C++ code that exploits OpenMP loop parallelization, and (iii) a CUDA kernel for a NVIDIA GTX 960 Maxwell card. In these implementations, the main source of parallelism is the simultaneous ADI updating of each wave field matrix, either column-wise or row-wise, according to the differentiation direction. In our numerical applications, the highest performances are displayed by the CFD and MFD CUDA codes that achieve speedups of 7.21x and 15.81x, respectively, relative to their C++ sequential counterparts with optimal compilation flags. Our test cases also allow to assess the numerical convergence and accuracy of both methods. In a problem with exact harmonic solution, both methods exhibit convergence rates close to 4 and the MDF accuracy is practically higher. Alternatively, both convergences decay to second order on smooth problems with severe gradients at boundaries, and the MDF rates degrade in highly-resolved grids leading to larger inaccuracies. This transition of empirical convergences agrees with the nominal truncation errors in space and time.Comment: 20 pages, 5 figure

TABARNAC: Visualizing and Resolving Memory Access Issues on NUMA Architectures

International audienceIn modern parallel architectures, memory accesses represent a common bottleneck. Thus, optimizing the way applications access the memory is an important way to improve performance and energy consumption. Memory accesses are even more important with NUMA machines, as the access time to data depends on its location in the memory. Many efforts were made to develop adaptive tools to improve memory accesses at the runtime by optimizing the mapping of data and threads to NUMA nodes. However, theses tools are not able to change the memory access pattern of the original application, therefore a code written without considering memory performance might not benefit from them. Moreover, automatic mapping tools take time to converge towards the best mapping, losing optimization opportunities. A deeper understanding of the memory behavior can help optimizing it, removing the need for runtime analysis. In this paper, we present TABARNAC , a tool for analyzing the memory behavior of parallel applications with a focus on NUMA architectures. TABARNAC provides a new visualization of the memory access behavior, focusing on the distribution of accesses by thread and by structure. Such visualization allows the developer to easily understand why performance issues occur and how to fix them. Using TABARNAC , we explain why some applications do not benefit from data and thread mapping. Moreover, we propose several code modifications to improve the memory access behavior of several parallel applications. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credi

Sur la conception de solveurs linéaires hybrides pour les architectures parallèles modernes

In the context of this thesis, our focus is on numerical linear algebra, more precisely on solution of large sparse systems of linear equations. We focus on designing efficient parallel implementations of MaPHyS, an hybrid linear solver based on domain decomposition techniques. First we investigate the MPI+threads approach. In MaPHyS, the first level of parallelism arises from the independent treatment of the various subdomains. The second level is exploited thanks to the use of multi-threaded dense and sparse linear algebra kernels involved at the subdomain level. Such an hybrid implementation of an hybrid linear solver suitably matches the hierarchical structure of modern supercomputers and enables a trade-off between the numerical and parallel performances of the solver. We demonstrate the flexibility of our parallel implementation on a set of test examples. Secondly, we follow a more disruptive approach where the algorithms are described as sets of tasks with data inter-dependencies that leads to a directed acyclic graph (DAG) representation. The tasks are handled by a runtime system. We illustrate how a first task-based parallel implementation can be obtained by composing task-based parallel libraries within MPI processes throught a preliminary prototype implementation of our hybrid solver. We then show how a task-based approach fully abstracting the hardware architecture can successfully exploit a wide range of modern hardware architectures. We implemented a full task-based Conjugate Gradient algorithm and showed that the proposed approach leads to very high performance on multi-GPU, multicore and heterogeneous architectures.Dans le contexte de cette thèse, nous nous focalisons sur des algorithmes pour l’algèbre linéaire numérique, plus précisément sur la résolution de grands systèmes linéaires creux. Nous mettons au point des méthodes de parallélisation pour le solveur linéaire hybride MaPHyS. Premièrement nous considerons l'aproche MPI+threads. Dans MaPHyS, le premier niveau de parallélisme consiste au traitement indépendant des sous-domaines. Le second niveau est exploité grâce à l’utilisation de noyaux multithreadés denses et creux au sein des sous-domaines. Une telle implémentation correspond bien à la structure hiérarchique des supercalculateurs modernes et permet un compromis entre les performances numériques et parallèles du solveur. Nous démontrons la flexibilité de notre implémentation parallèle sur un ensemble de cas tests. Deuxièmement nous considérons un approche plus innovante, où les algorithmes sont décrits comme des ensembles de tâches avec des inter-dépendances, i.e., un graphe de tâches orienté sans cycle (DAG). Nous illustrons d’abord comment une première parallélisation à base de tâches peut être obtenue en composant des librairies à base de tâches au sein des processus MPI illustrer par un prototype d’implémentation préliminaire de notre solveur hybride. Nous montrons ensuite comment une approche à base de tâches abstrayant entièrement le matériel peut exploiter avec succès une large gamme d’architectures matérielles. À cet effet, nous avons implanté une version à base de tâches de l’algorithme du Gradient Conjugué et nous montrons que l’approche proposée permet d’atteindre une très haute performance sur des architectures multi-GPU, multicoeur ainsi qu’hétérogène

EuroEXA - D2.6: Final ported application software

This document describes the ported software of the EuroEXA applications to the single CRDB testbed and it discusses the experiences extracted from porting and optimization activities that should be actively taken into account in future redesign and optimization. This document accompanies the ported application software, found in the EuroEXA private repository (https://github.com/euroexa). In particular, this document describes the status of the software for each of the EuroEXA applications, sketches the redesign and optimization strategy for each application, discusses issues and difficulties faced during the porting activities and the relative lesson learned. A few preliminary evaluation results have been presented, however the full evaluation will be discussed in deliverable 2.8

Green Wave : A Semi Custom Hardware Architecture for Reverse Time Migration

Over the course of the last few decades the scientific community greatly benefited from steady advances in compute performance. Until the early 2000's this performance improvement was achieved through rising clock rates. This enabled plug-n-play performance improvements for all codes. In 2005 the stagnation of CPU clock rates drove the computing hardware manufactures to attain future performance through explicit parallelism. Now the HPC community faces a new, even bigger challenge. So far performance gains were achieved through replication of general-purpose cores and nodes. Unfortunately, rising cluster sizes resulted in skyrocketing energy costs - a paradigm change in HPC architecture design is inevitable. In combination with the increasing costs of data movement, the HPC community started exploring alternatives like GPUs and large arrays of simple, low-power cores (e.g. BlueGene) to offer the better performance per Watt and greatest scalability. As in general science, the seismic community faces large-scale, complex computational challenges that can only be limited solved with available compute capabilities. Such challenges include the physically correct modeling of subsurface rock layers. This thesis analyzes the requirements and performance of isotropic (ISO), vertical transverse isotropic (VTI) and tilted transverse isotropic (TTI) wave propagation kernels as they appear in the Reverse Time Migration (RTM) imaging method. It finds that even with leading-edge, commercial off-the-shelf hardware, large-scale survey sizes cannot be imaged within reasonable time and power constraints. This thesis uses a novel architecture design method leveraging a hardware/software co-design approach, adopted from the mobile- and embedded market, for HPC. The methodology tailors an architecture design to a class of applications without loss of generality like in full custom designs. This approach was first applied in the Green Flash project, which proved that the co-design approach has the potential for high energy efficiency gains. This thesis presents the novel Green Wave architecture that is derived from the Green Flash project. Rather than focusing on climate codes, like Green Flash, Green Wave chooses RTM wave propagation kernels as its target application. Thus, the goal of the application-driven, co-design Green Wave approach, is to enable full programmability while allowing greater computational efficiency than general-purpose processors or GPUs by offering custom extensions to the processor's ISA and correctly sizing software-managed memories and an efficient on-chip network interconnect. The lowest level building blocks of the Green Wave design are pre-verified IP components. This minimizes the amount of custom logic in the design, which in turn reduces verification costs and design uncertainty. In this thesis three Green Wave architecture designs derived from ISO, VTI and TTI kernel analysis are introduced. Further, a programming model is proposed capable of hiding all communication latencies. With production-strength, cycle-accurate hardware simulators Green Wave's performance is benchmarked and its performance compared to leading on-market systems from Intel, AMD and NVidia. Based on a large-scale example survey, the results show that Green Wave has the potential of an energy efficiency improvement of 5x compared to x86 and 1.4x-4x to GPU-based clusters for ISO, VTI and TTI kernels

Geophysics for Mineral Exploration

This Special Issue contains ten papers which focus on emerging geophysical techniques for mineral exploration, novel modeling, and interpretation methods, including joint inversions of multi physics data, and challenging case studies. The papers cover a wide range of mineral deposits, including banded iron formations, epithermal gold–silver–copper–iron–molybdenum deposits, iron-oxide–copper–gold deposits, and prospecting forgroundwater resources

Modeling for inversion in exploration geophysics

Seismic inversion, and more generally geophysical exploration, aims at better understanding the earth's subsurface, which is one of today's most important challenges. Firstly, it contains natural resources that are critical to our technologies such as water, minerals and oil and gas. Secondly, monitoring the subsurface in the context of CO2 sequestration, earthquake detection and global seismology are of major interests with regard to safety and the environment hazards. However, the technologies to monitor the subsurface or find resources are scientifically extremely challenging. Seismic inversion can be formulated as a mathematical optimization problem that minimizes the difference between field recorded data and numerically modeled synthetic data. The process of solving this optimization problem then requires to numerically model, thousands of times, wave-propagation in large three-dimensional representations of part of the earth subsurface. The mathematical and computational complexity of this problem, therefore, calls for software design that abstracts these requirements and facilitates algorithm and software development. My thesis addresses some of the challenges that arise from these problems; mainly the computational cost and access to the right software for research and development. In the first part, I will discuss a performance metric that improves the current runtime-only benchmarks in exploration geophysics. This metric, the roofline model, first provides insight at the hardware level of the performance of a given implementation relative to the maximum achievable performance. Second, this study demonstrates that the choice of numerical discretization has a major impact on the achievable performance depending on the hardware at hand and shows that a flexible framework with respect to the discretization parameters is necessary. In the second part, I will introduce and describe Devito, a symbolic finite-difference DSL that provides a high-level interface to the definition of partial differential equations (PDE) such as the wave equation. Devito, from the symbolic definition of PDEs, then generates and compiles highly optimized C code on-the-fly to compute the solution of the PDE. The combination of the high-level abstractions and the just-in-time compiler enable research for geophysical exploration and PDE-constrainted optimization based on the paradigm of separation of concerns. This allows researchers to concentrate on their respective field of study while having access to computationally performant solvers with a flexible and easy to use interface to successfully implement complex representations of the physics. The second part of my thesis will be split into two sub-parts; first describing the symbolic application programming interface (API), before describing and benchmarking the just-in-time compiler. I will end my thesis with concluding remarks, the latest developments and a brief description of projects that were enabled by Devito.Ph.D