Parallelization of an object-oriented FEM dynamics code: influence of the strategies on the Speedup

This paper presents an implementation in C++ of an explicit parallel finite element code dedicated to the simulation of impacts. We first present a brief overview of the kinematics and the explicit integration scheme with details concerning some particular points. Then we present the OpenMP parallelization toolkit used in order to parallelize our FEM code, and we focus on how the parallelization of the DynELA FEM code has been conducted for a shared memory system using OpenMP. Some examples are then presented to demonstrate the efficiency and accuracy of the proposed implementations concerning the Speedup of the code. Finally, an impact simulation application is presented and results are compared with the ones obtained by the commercial Abaqus explicit FEM code

PoisFFT - A Free Parallel Fast Poisson Solver

A fast Poisson solver software package PoisFFT is presented. It is available as a free software licensed under the GNU GPL license version 3. The package uses the fast Fourier transform to directly solve the Poisson equation on a uniform orthogonal grid. It can solve the pseudo-spectral approximation and the second order finite difference approximation of the continuous solution. The paper reviews the mathematical methods for the fast Poisson solver and discusses the software implementation and parallelization. The use of PoisFFT in an incompressible flow solver is also demonstrated

An LLVM Instrumentation Plug-in for Score-P

Reducing application runtime, scaling parallel applications to higher numbers of processes/threads, and porting applications to new hardware architectures are tasks necessary in the software development process. Therefore, developers have to investigate and understand application runtime behavior. Tools such as monitoring infrastructures that capture performance relevant data during application execution assist in this task. The measured data forms the basis for identifying bottlenecks and optimizing the code. Monitoring infrastructures need mechanisms to record application activities in order to conduct measurements. Automatic instrumentation of the source code is the preferred method in most application scenarios. We introduce a plug-in for the LLVM infrastructure that enables automatic source code instrumentation at compile-time. In contrast to available instrumentation mechanisms in LLVM/Clang, our plug-in can selectively include/exclude individual application functions. This enables developers to fine-tune the measurement to the required level of detail while avoiding large runtime overheads due to excessive instrumentation.Comment: 8 page

SPH-EXA: Enhancing the Scalability of SPH codes Via an Exascale-Ready SPH Mini-App

Numerical simulations of fluids in astrophysics and computational fluid dynamics (CFD) are among the most computationally-demanding calculations, in terms of sustained floating-point operations per second, or FLOP/s. It is expected that these numerical simulations will significantly benefit from the future Exascale computing infrastructures, that will perform 10^18 FLOP/s. The performance of the SPH codes is, in general, adversely impacted by several factors, such as multiple time-stepping, long-range interactions, and/or boundary conditions. In this work an extensive study of three SPH implementations SPHYNX, ChaNGa, and XXX is performed, to gain insights and to expose any limitations and characteristics of the codes. These codes are the starting point of an interdisciplinary co-design project, SPH-EXA, for the development of an Exascale-ready SPH mini-app. We implemented a rotating square patch as a joint test simulation for the three SPH codes and analyzed their performance on a modern HPC system, Piz Daint. The performance profiling and scalability analysis conducted on the three parent codes allowed to expose their performance issues, such as load imbalance, both in MPI and OpenMP. Two-level load balancing has been successfully applied to SPHYNX to overcome its load imbalance. The performance analysis shapes and drives the design of the SPH-EXA mini-app towards the use of efficient parallelization methods, fault-tolerance mechanisms, and load balancing approaches.Comment: arXiv admin note: substantial text overlap with arXiv:1809.0801

MPI+X: task-based parallelization and dynamic load balance of finite element assembly

The main computing tasks of a finite element code(FE) for solving partial differential equations (PDE's) are the algebraic system assembly and the iterative solver. This work focuses on the first task, in the context of a hybrid MPI+X paradigm. Although we will describe algorithms in the FE context, a similar strategy can be straightforwardly applied to other discretization methods, like the finite volume method. The matrix assembly consists of a loop over the elements of the MPI partition to compute element matrices and right-hand sides and their assemblies in the local system to each MPI partition. In a MPI+X hybrid parallelism context, X has consisted traditionally of loop parallelism using OpenMP. Several strategies have been proposed in the literature to implement this loop parallelism, like coloring or substructuring techniques to circumvent the race condition that appears when assembling the element system into the local system. The main drawback of the first technique is the decrease of the IPC due to bad spatial locality. The second technique avoids this issue but requires extensive changes in the implementation, which can be cumbersome when several element loops should be treated. We propose an alternative, based on the task parallelism of the element loop using some extensions to the OpenMP programming model. The taskification of the assembly solves both aforementioned problems. In addition, dynamic load balance will be applied using the DLB library, especially efficient in the presence of hybrid meshes, where the relative costs of the different elements is impossible to estimate a priori. This paper presents the proposed methodology, its implementation and its validation through the solution of large computational mechanics problems up to 16k cores

Loo.py: From Fortran to performance via transformation and substitution rules

A large amount of numerically-oriented code is written and is being written in legacy languages. Much of this code could, in principle, make good use of data-parallel throughput-oriented computer architectures. Loo.py, a transformation-based programming system targeted at GPUs and general data-parallel architectures, provides a mechanism for user-controlled transformation of array programs. This transformation capability is designed to not just apply to programs written specifically for Loo.py, but also those imported from other languages such as Fortran. It eases the trade-off between achieving high performance, portability, and programmability by allowing the user to apply a large and growing family of transformations to an input program. These transformations are expressed in and used from Python and may be applied from a variety of settings, including a pragma-like manner from other languages.Comment: ARRAY 2015 - 2nd ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming (ARRAY 2015

OP2-Clang : a source-to-source translator using Clang/LLVM LibTooling

Domain Specific Languages or Active Library frameworks have recently emerged as an important method for gaining performance portability, where an application can be efficiently executed on a wide range of HPC architectures without significant manual modifications. Embedded DSLs such as OP2, provides an API embedded in general purpose languages such as C/C++/Fortran. They rely on source-to-source translation and code refactorization to translate the higher-level API calls to platform specific parallel implementations. OP2 targets the solution of unstructured-mesh computations, where it can generate a variety of parallel implementations for execution on architectures such as CPUs, GPUs, distributed memory clusters and heterogeneous processors making use of a wide range of platform specific optimizations. Compiler tool-chains supporting source-to-source translation of code written in mainstream languages currently lack the capabilities to carry out such wide-ranging code transformations. Clang/LLVM’s Tooling library (LibTooling) has long been touted as having such capabilities but have only demonstrated its use in simple source refactoring tasks. In this paper we introduce OP2-Clang, a source-to-source translator based on LibTooling, for OP2’s C/C++ API, capable of generating target parallel code based on SIMD, OpenMP, CUDA and their combinations with MPI. OP2-Clang is designed to significantly reduce maintenance, particularly making it easy to be extended to generate new parallelizations and optimizations for hardware platforms. In this research, we demonstrate its capabilities including (1) the use of LibTooling’s AST matchers together with a simple strategy that use parallelization templates or skeletons to significantly reduce the complexity of generating radically different and transformed target code and (2) chart the challenges and solution to generating optimized parallelizations for OpenMP, SIMD and CUDA. Results indicate that OP2-Clang produces near-identical parallel code to that of OP2’s current source-to-source translator. We believe that the lessons learnt in OP2-Clang can be readily applied to developing other similar source-to-source translators, particularly for DSLs