![]() ![]() HostArray, BLOCKS * BLOCKS * sizeof(int), As you can see, we take care of a two dimensional array, using BLOCKS*BLOCKS when allocating:ĬudaMalloc( (void**)&deviceArray, BLOCKS * BLOCKS * sizeof(int) ) Next, we allocate the memory needed for our array on the device. Then we define a 2d array, a pointer for copying to/from the GPU and our dim3 variable: So, why is it dim3? Well, in the future CUDA C might support 3d-arrays as well, but for now, it’s only reserved, so when you create the array, you specify the dimension of the X-axis, and the Y-axis, and then the 3rd axis automatically is set to 1.įirst of all, include stdio.h and define the size of our block array: How do we do this? First of all, we will need to use a keyword from the CUDA C library, and define our variable. Basically, it’s all the same as before, but we used multidimensional indexing. But since they are 2d, you can think of them as a coordinate system where you have blocks in the x- and y-axis. These types of blocks work just the same way as the other blocks we have seen so far in this tutorial. We will create the same program as in the last tutorial, but instead display a 2d-array of blocks, each displaying a calculated value. In this short tutorial, we will look at how to launch multidimensional blocks on the GPU (grids). Void cenergy(float *energygrid, dim3 grid, float gridspacing, float z, const float *atoms, int numatoms) Needs to be done as an atomic operation ©Wen-mei W.Welcome to part 5 of the Parallel Computing tutorial. Int k = z / gridspacing The grid parameter gives the number of grid points in each dimension of the lattice. Hwu and David Kirk/NVIDIA, Urbana, August 13-17, 2012Īlgorithm is input oriented For each input atom, calculate its contribution to all grid points in an x-y slice Output (energygrid) is regular Simple linear mapping between grid point indices and modeled physical coordinates Input (atom) is irregular Modeled x,y,z coordinate of each atom needs to be stored in the atom array The algorithm is efficient in performing minimal calculations on distances, coordinates, etc. Hwu and David Kirk/NVIDIA, Urbana, August 13-17, 2012Ītoms come from modeled molecular structures, solvent (water) and ions Irregular by necessity Energy grid models the electrostatic potential value at regularly spaced points Regular by design ©Wen-mei W. One way to compute the electrostatic potentials on a grid, ideally suited for the GPU All atoms affect all map lattice points, most accurate For each lattice point, sum potential contributions for all atoms in the simulated structure: potential += charge / (distance to atom) Approximation-based methods such as cut-off summation can achieve much higher performance at the cost of some numerical accuracy and flexibilityħ Direct Coulomb Summation (DCS) Algorithm DetailĪt each lattice point, sum potential contributions for all atoms in the simulated structure: potential += charge / (distance to atom) ri,j: distance from lattice point j to atom potential: potential at lattice point being evaluated atom ©Wen-mei W. In the Direct Coulomb Summation method, the total potential at lattice point j is the sum of contributions from all atoms in the system.Ħ Overview of Direct Coulomb Summation (DCS) Algorithm The contribution of atom to the electrostatic potential at lattice is potential = atom.charge / rij. ri,j: distance from lattice point j to atom potential: potential at lattice point being evaluated atom interactive mode Run on laptops, desktops, clusters, supercomputersĬalculate initial electrostatic potential map around the simulated structure considering the contributions of all atoms Most time consuming, focus of our example. Visualizing, animating, and analyzing bio-molecular systems More than 200,000 users as of 2012 Batch (movie making) vs. Presentation on theme: "ECE408 / CS483 Applied Parallel Programming Lecture 23: Application Case Study – Electrostatic Potential Calculation."- Presentation transcript:ġ ECE408 / CS483 Applied Parallel Programming Lecture 23: Application Case Study – Electrostatic Potential CalculationĢ Objective To learn how to apply parallel programming techniques to an application Thread coarsening for more work efficiency Data structure padding for reduced divergence Memory access locality and pre-computation techniques ![]()
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