SimpleMOC - A performance abstraction for 3D MOC

The method of characteristics (MOC) is a popular method for efficiently solving two-dimensional reactor problems. Extensions to three dimensions have been attempted with mitigated success bringing into question the ability of performing efficient full core three-dimensional (3D) analysis. Although the 3D problem presents many computational difficulties, some simplifications can be made that allow for more efficient computation. In this investigation, we present SimpleMOC, a “mini-app” which mimics the computational performance of a full 3D MOC solver without involving the full physics perspective, allowing for a more straightforward analysis of the computational challenges. A variety of simplifications are implemented that are intended to increase the computational feasibility, including the formation axially-quadratic neutron sources. With the addition of the quadratic approximation to the neutron source, 3D MOC is cast as a CPU-intensive method with the potential for remarkable scalability on next generation computing architectures.United States. Dept. of Energy. Office of Nuclear Energy (Nuclear Energy University Programs Fellowship)United States. Dept. of Energy. Center for Exascale Simulation of Advanced ReactorUnited States. Dept. of Energy. Office of Advanced Scientific Computing Research (Contract DE-AC02-06CH11357

Monte Carlo domain decomposition for robust nuclear reactor analysis

Monte Carlo (MC) neutral particle transport codes are considered the gold-standard for nuclear simulations, but they cannot be robustly applied to high-fidelity nuclear reactor analysis without accommodating several terabytes of materials and tally data. While this is not a large amount of aggregate data for a typical high performance computer, MC methods are only embarrassingly parallel when the key data structures are replicated for each processing element, an approach which is likely infeasible on future machines. The present work explores the use of spatial domain decomposition to make full-scale nuclear reactor simulations tractable with Monte Carlo methods, presenting a simple implementation in a production-scale code. Good performance is achieved for mesh-tallies of up to 2.39 TB distributed across 512 compute nodes while running a full-core reactor benchmark on the Mira Blue Gene/Q supercomputer at the Argonne National Laboratory. In addition, the effects of load imbalances are explored with an updated performance model that is empirically validated against observed timing results. Several load balancing techniques are also implemented to demonstrate that imbalances can be largely mitigated, including a new and efficient way to distribute extra compute resources across finer domain meshes.United States. Dept. of Energy. Center for Exascale Simulation of Advanced Reactor

On the Energy Efficiency and Performance of Irregular Application Executions on Multicore, NUMA and Manycore Platforms

International audienceUntil the last decade, performance of HPC architectures has been almost exclusively quantifiedby their processing power. However, energy efficiency is being recently considered as importantas raw performance and has become a critical aspect to the development of scalablesystems. These strict energy constraints guided the development of a new class of so-calledlight-weight manycore processors. This study evaluates the computing and energy performanceof two well-known irregular NP-hard problems — the Traveling-Salesman Problem (TSP) andK-Means clustering—and a numerical seismic wave propagation simulation kernel—Ondes3D—on multicore, NUMA, and manycore platforms. First, we concentrate on the nontrivial task ofadapting these applications to a manycore, specifically the novel MPPA-256 manycore processor.Then, we analyze their performance and energy consumption on those di↵erent machines.Our results show that applications able to fully use the resources of a manycore can have betterperformance and may consume from 3.8x to 13x less energy when compared to low-power andgeneral-purpose multicore processors, respectivel

A performance evaluation methodology to find the best parallel regions to reduce energy consumption

Due to energy limitations imposed to supercomputers, parallel applications developed for High Performance Computers (HPC) are currently being investigated with energy efficiency metrics. The idea is to reduce the energy footprint of these applications. While some energy reduction strategies consider the application as a whole, certain strategies adjust the core frequency only for certain regions of the parallel code. Load balancing or blocking communication phases could be used as opportunities for energy reduction, for instance. The efficiency analysis of such strategies is usually carried out with traditional methodologies derived from the performance analysis domain. It is clear that a finer grain methodology, where the energy reduction is evaluated per each code region and frequency configuration, could potentially lead to a better understanding of how energy consumption can be reduced for a particular algorithm implementation. To get this, the main challenges are: (a) the detection of such, possibly parallel, code regions and the large number of them; (b) which frequency should be adopted for that region (to reduce energy consumption without too much penalty for the runtime); and (c) the cost to dynamically adjust core frequency. The work described in this dissertation presents a performance analysis methodology to find the best parallel region candidates to reduce energy consumption. The proposal is three folded: (a) a clever design of experiments based on screening, especially important when a large number of parallel regions is detected in the applications; (b) a traditional energy and performance evaluation on the regions that were considered as good candidates for energy reduction; and (c) a Pareto-based analysis showing how hard is to obtain energy gains in optimized codes. In (c), we also show other trade-offs between performance loss and energy gains that might be of interest of the application developer. Our approach is validated against three HPC application codes: Graph500; Breadth-First Search, and Delaunay Refinement.Devido as limitações de consumo energético impostas a supercomputadores, métricas de eficiência energética estão sendo usadas para analisar aplicações paralelas desenvolvidas para computadores de alto desempenho. O objetivo é a redução do custo energético dessas aplicações. Algumas estratégias de redução de consumo energética consideram a aplicação como um todo, outras reduzem ajustam a frequência dos núcleos apenas em certas regiões do código paralelo. Fases de balanceamento de carga ou de comunicação bloqueante podem ser oportunas para redução do consumo energético. A análise de eficiência dessas estratégias é geralmente realizada com metodologias tradicionais derivadas do domínio de análise de desempenho. Uma metodologia de grão mais fino, onde a redução de energia é avaliada para cada região de código e frequência pode lever a um melhor entendimento de como o consumo energético pode ser minimizado para uma determinada implementação. Para tal, os principais desafios são: (a) a detecção de um número possivelmente grande de regiões paralelas; (b) qual frequência deve ser adotada para cada região de forma a limitar o impacto no tempo de execução; e (c) o custo do ajuste dinâmico da frequência dos núcleos. O trabalho descrito nesta dissertação apresenta uma metodologia de análise de desempenho para encontrar, dentre as regiões paralelas, os melhores candidatos a redução do consumo energético. (Cotninua0 Esta proposta consiste de: (a) um design inteligente de experimentos baseado em Plackett-Burman, especialmente importante quando um grande número de regiões paralelas é detectado na aplicação; (b) análise tradicional de energia e desempenho sobre as regiões consideradas candidatas a redução do consumo energético; e (c) análise baseada em eficiência de Pareto mostrando a dificuldade em otimizar o consumo energético. Em (c) também são mostrados os diferentes pontos de equilíbrio entre desempenho e eficiência energética que podem ser interessantes ao desenvolvedor. Nossa abordagem é validada por três aplicações: Graph500, busca em largura, e refinamento de Delaunay

Versatile, Scalable, and Accurate Simulation of Distributed Applications and Platforms

International audienceThe study of parallel and distributed applications and platforms, whether in the cluster, grid, peer-to-peer, volunteer, or cloud computing domain, often mandates empirical evaluation of proposed algorithmic and system solutions via simulation. Unlike direct experimentation via an application deployment on a real-world testbed, simulation enables fully repeatable and configurable experiments for arbitrary hypothetical scenarios. Two key concerns are accuracy (so that simulation results are scientifically sound) and scalability (so that simulation experiments can be fast and memory-efficient). While the scalability of a simulator is easily measured, the accuracy of many state-of-the-art simulators is largely unknown because they have not been sufficiently validated. In this work we describe recent accuracy and scalability advances made in the context of the SimGrid simulation framework. A design goal of SimGrid is that it should be versatile, i.e., applicable across all aforementioned domains. We present quantitative results that show that SimGrid compares favorably to state-of-the-art domain-specific simulators in terms of scalability, accuracy, or the trade-off between the two. An important implication is that, contrary to popular wisdom, striving for versatility in a simulator is not an impediment but instead is conducive to improving both accuracy and scalability

Speeding up computer vision applications on mobile computing platforms

[CATALÀ] Aquest projecte investiga la manera d'accelerar nuclis de visió per computador a través de diferents tècniques d'optimització i paral·lelització. Hem portat l'algoritme KinectFusion a una plataforma mòbil fent servir OpenCL.[ANGLÈS] This project investigates ways of speeding up computer vision kernels through optimisation and parallelisation. We ported the KinectFusion algorithm to a mobile platform using OpenCL

PDE Solvers for Hybrid CPU-GPU Architectures

Many problems of scientific and industrial interest are investigated through numerically solving partial differential equations (PDEs). For some of these problems, the scope of the investigation is limited by the costs of computational resources. A new approach to reducing these costs is the use of coprocessors, such as graphics processing units (GPUs) and Many Integrated Core (MIC) cards, which can execute floating point operations at a higher rate than a central processing unit (CPU) of the same cost. This is achieved through the use of a large number of processors in a single device, each with very limited dedicated memory per thread. Codes for a number of continuum methods, such as boundary element methods (BEM), finite element methods (FEM) and finite difference methods (FDM) have already been implemented on coprocessor architectures. These methods were designed before the adoption of coprocessor architectures, so implementing them efficiently with reduced thread-level memory can be challenging. There are other methods that do operate efficiently with limited thread-level memory, such as Monte Carlo methods (MCM) and lattice Boltzmann methods (LBM) for kinetic formulations of PDEs, but they are not competitive on CPUs and generally have poorer convergence than the continuum methods. In this work, we introduce a class of methods in which the parallelism of kinetic formulations on GPUs is combined with the better convergence of continuum methods on CPUs. We first extend an existing Feynman-Kac formulation for determining the principal eigenpair of an elliptic operator to create a version that can retrieve arbitrarily many eigenpairs. This new method is implemented for multiple GPUs, and combined with a standard deflation preconditioner on multiple CPUs to create a hybrid concurrent method with superior convergence to that of the deflation preconditioner alone. The hybrid method exhibits good parallelism, with an efficiency of 80% on a problem with 300 million unknowns, run on a configuration of 324 CPU cores and 54 GPUs.Doctor of Philosoph