The Poisson operator, aka the Laplacian, is a second order elliptic differential operator and defined in an n-dimensional Cartesian space by: The Poisson operator appears in the definition of the Helmholtz differential equation: The Helmholtz differential equation reduces to the Poisson equation: The Poisson equation is used in the modeling of various boundary value physical problems: e.g. electric potential in electrostatics, potential flow in fluid dynamics etc. The definition of the appropriate boundary conditions, Dirichlet and Neumann, allows for the solution of the Poisson problem. A numerical solution requires the discretization of the continuous Poisson’s equation, e.g. by the standard centered-difference approximation, as well as a discrete handling of the Dirichlet and Neumann boundary conditions. The discrete Poisson operator, the focus of this project, is given by the following stencil: Contact information: Meriem Ben Salah: UC Berkeley ME Graduate Student, ParLab, Razvan Corneliu Carbunescu: UC Berkeley CS Graduate Student, ParLab, Andrew Gearhart: UC Berkeley CS Graduate Student, ParLab, James Demmel:UC Berkeley Math & CS Faculty, Phillip Colella:LBNL ANAG, Brian Van Sraalen: LBNL ANAG, CHOMBO is a framework for implementing finite difference methods for the solution of partial differential equations on block structured adaptively refined rectangular grids. CHOMBO provides elliptic and time-dependent modules, as well as support for standardized self-describing file formats. Chombo is architecture and operating system independent.  For the use on parallel platforms, CHOMBO provides solely a distributed memory implementation in MPI (Message Passing Interface). Is the use of a distributed memory implementation always beneficial? Theoretical Background Targeted Architectures Investigations and Results Conclusions and Future Work Project Strategy and Goals Poisson Operator in CHOMBO Motivation For the sake of a demonstrative exposition, we use the Poisson potential flow solve done in the incompressible Navier Stokes equations. A Poisson solve is conducted at the beginning of an incompressible flow simulation to obtain initial conditions to the evolution of the velocity and pressure. CHOMBO BoxTools Calculations over union of rectangles AMRTools Communication btw. refinement levels EBTools Embedded boundary discretization AMRElliptic Multigrid solvers on discretized elliptic (poisson, resistivity etc) and parabolic equations AMRTimeDependent Subcycling of time dependent calculations ParticleTools Particle dynamics References: [1] John Kubiatowicz, 2009, “CS252 Graduate Computer Architecture Lecture 24, Network Interface Design Memory Consistency Models” [2] P. Colella et al., 2009, “Chombo Software Package for AMR Applications Design Document” [3]Machine images obtained from nersc.gov, krunker.com, compsource,com and bit-tech.net Poisson Solver OPTIMIZATION OF THE POISSON OPERATOR IN CHOMBO Razvan CarbunescuMeriem Ben SalahAndrew Gearhart 1. Focus on the stencil kernel which applies the poisson operator on a two dimensional cell- centered data that is embedded in the AMRElliptic tool, a multigrid-based elliptic and parabolic equation solver for adaptive mesh hierarchies library. 2. Start with the serial and distributed memory implementation supplied in CHOMBO for reference. 3. Implement parallel shared memory architectures. 4. Conduct a parameter study to analyze the benefits and the drawbacks of each of the implementations in terms of computational time. 5. Draw global conclusions on the consequences of the limitations of the parallel implementation on CHOMBO. Fig.1 Flow Boundary Condition InitialializationFig. 2 Flow Evolution Start-Up After A Poisson Solve Existing Implementations Our interest CHOMBO’s implementation is currently tuned for distributed memory with the domain being split up into small boxes on the order of 32 squared for 2D and 32 cubed for 3D and each box is being assigned to a processor which runs serial Fortran 77 code. Because of the small size of the box there is not a lot of computational intensity to use a threaded shared memory implementation or to hide the cost of the transfer to the GPU. While CHOMBO’s code obtains good performance for MPI on a distributed memory system our study is aimed at determining whether we can improve this speedup through the use of locality and faster access time to on-chip cores. Our current implementation on the shared memory model only uses a limited basic strip-mining technique since it maintains the abstractions of a data iterator going through all the boxes each step but a more relaxed model could help with time also being allowed to be blocked. Another interesting opportunity for speedup is running the operator on the GPU but the benefits must outweigh the cost of moving the data onto and off the GPU. The previous points above raise the issue of threads and how for small boxes the lightweight threads are really important to allow for fast context switching and therefore hopefully increase performance. To allow for the use of pthreads and CUDA we implemented a C version of the operator. The particular reason for this choice of study is to allow for the creation of heterogeneous systems that would automatically adjust their code to either MPI. Pthreads or Cuda underneath to achieve the best result. The serial, the distributed memory and the shared memory implementations of the Poisson solve have been run on the NERSC Cray XT4 system, called Franklin, a massively parallel processing system with 9,532 compute nodes (quad processor cores) and 38,128 processors. The implementation on the GPU is being conducted on personal Linux boxes as well as the r56 and 57 nodes of the Millennium cluster. The number of grid cells in the spatial partition as well as the number of grid refinements affect the computation of the discrete Poisson stencil and are the focus of these test runs. In order to have some sense of fair comparison, the number of grid cells and the number of grid refinements are kept equal through the test runs. To account for different loading of the machine architecture each test was repeated 5 times and its corresponding results averaged. The shared memory implementation has beneficial results vs. the C serial code but more analysis is required to correctly compare to the Fortran MPI code. At the time of this presentation no results have yet completed from the GPU. It will be interesting to find a correct methodology to compare the GPU results from Millennium with the results of MPI and Shared memory implementations. Since Franklin does not provide GPUs, the GPU test runs have to be verified independently. This work will be reported later in the project document. It is obvious that the choice of the C implementation led to a loss of computational performance, and therefore a Fortran 77 shared memory implementation could be attractive. However, a POSIX threads interface to Fortran 77 is currently not available. A improvement on the shared memory implementation might exist in creating the OpenMP Fortran 77 solver code. Currently our simulations are only performed on the Cray XT4 and the GPU. It would be interesting to conduct these studies on different machine architectures. Figure 3 shows a comparison of the runtimes of the C serial code vs the Fortran 77 serial code. Besides the growing time, it is interesting to note that despite the similar matrix storage in Fortran77 and C (column major), the Fortran 77 code is accessed fastest when loops are ordered as n,j,i vs the C code that is indexed i,j,n. Figure 4 depicts the speed-up of the various parallel implementations with respect to the associated serial C version, C_MPI44 refers to running the MPI version with all 4 cores on 1 node and C_MPI41 refers to running the MPI version with 4 cores but 1 core per node. Figure 5 presents the relative speedup of all the codes with respect to the fastest (Fortran) serial version. ncell time Speed up Fig.4 Speedup vs. C serial implementation Fig.5 Speedup vs. F serial implementation Fig.3 Fortran vs C serial implementation