![]() ![]() In your kernel you have an appropriate thread-check such as: _global_ void k(. Therefore it is assumed that you want to launch a grid of blocks that is large enough to cover your dimensions. The dimx, dimy, and dimz may not be whole-number divisible by block.x, block.y, and block.z respectively. When defining a variable of type dim3, any component left. dim3 is an integer vector type based on uint3 that is used to specify dimensions. However, the access pattern depends on how you are interpreting your data and also how you are accessing them by 1D, 2D and 3D blocks of threads. ĭim3 grid((dimx+block.x-1)/block.x, (dimy+block.y-1)/block.y, (dimz+block.z-1)/block.z) The memory is always a 1D continuous space of bytes. 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.Typically you would compute them as follows: int dimx =. Int k = z / gridspacing The grid parameter gives the number of grid points in each dimension of the lattice. In fact, grids and blocks are 3D arrays of blocks and threads, respectively. Sine we are multiplying 2-D matrices it only makes sense to arrange the thread-blocks and grid in 2-D. 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. When we call a kernel using the instruction <<< > we automatically define a dim3 type variable defining the number of blocks per grid and threads per block. processint Row + threadIdx.y // Calculate the column of. A thread-block or grid can be arranged in 1-D, 2-D or 3-D.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 Once we determine a grid and a block size from our volume, we allocate memory for the. 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. int k (blockIdx.y / gy) blockDim.z + threadIdx.z. dim3 Dg: grid dimensions in blocks dim3 Db: block dimensions in threads. Visualizing, animating, and analyzing bio-molecular systems More than 200,000 users as of 2012 Batch (movie making) vs. Realities of integrated circuits: need to cluster computation and storage. 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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