src/gromacs/ewald/pme_gpu_program_impl.h

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  35 /*! \internal \file
  36  * \brief
  37  * Declares PmeGpuProgramImpl, which stores PME GPU (compiled) kernel handles.
  38  *
  39  * \author Aleksei Iupinov <a.yupinov@gmail.com>
  40  * \ingroup module_ewald
  41  */
  42 #ifndef GMX_EWALD_PME_PME_GPU_PROGRAM_IMPL_H
  43 #define GMX_EWALD_PME_PME_GPU_PROGRAM_IMPL_H
  44
  45 #include "config.h"
  46
  47 #include "gromacs/gpu_utils/device_context.h"
  48 #include "gromacs/utility/classhelpers.h"
  49
  50 class DeviceContext;
  51 struct DeviceInformation;
  52
  53 /*! \internal
  54  * \brief
  55  * PME GPU persistent host program/kernel data, which should be initialized once for the whole execution.
  56  *
  57  * Primary purpose of this is to not recompile GPU kernels for each OpenCL unit test,
  58  * while the relevant GPU context (e.g. cl_context) instance persists.
  59  * In CUDA, this just assigns the kernel function pointers.
  60  * This also implicitly relies on the fact that reasonable share of the kernels are always used.
  61  * If there were more template parameters, even smaller share of all possible kernels would be used.
  62  *
  63  * \todo In future if we would need to react to either user input or
  64  * auto-tuning to compile different kernels, then we might wish to
  65  * revisit the number of kernels we pre-compile, and/or the management
  66  * of their lifetime.
  67  *
  68  * This also doesn't manage cuFFT/clFFT kernels, which depend on the PME grid dimensions.
  69  *
  70  * TODO: pass cl_context to the constructor and not create it inside.
  71  * See also Issue #2522.
  72  */
  73 struct PmeGpuProgramImpl
  74 {
  75     /*! \brief
  76      * This is a handle to the GPU context, which is just a dummy in CUDA,
  77      * but is created/destroyed by this class in OpenCL.
  78      */
  79     const DeviceContext& deviceContext_;
  80
  81     //! Conveniently all the PME kernels use the same single argument type
  82 #if GMX_GPU_CUDA
  83     using PmeKernelHandle = void (*)(const struct PmeGpuCudaKernelParams);
  84 #elif GMX_GPU_OPENCL
  85     using PmeKernelHandle = cl_kernel;
  86 #else
  87     using PmeKernelHandle = void*;
  88 #endif
  89
  90     /*! \brief
  91      * Maximum synchronous GPU thread group execution width.
  92      * "Warp" is a CUDA term which we end up reusing in OpenCL kernels as well.
  93      * For CUDA, this is a static value that comes from gromacs/gpu_utils/cuda_arch_utils.cuh;
  94      * for OpenCL, we have to query it dynamically.
  95      */
  96     size_t warpSize_;
  97
  98     //@{
  99     /**
 100      * Spread/spline kernels are compiled only for order of 4.
 101      * There are multiple versions of each kernel, paramaretized according to
 102      *   Number of threads per atom. Using either order(4) or order*order (16) threads per atom is
 103      * supported If the spline data is written in the spline/spread kernel and loaded in the gather
 104      *   or recalculated in the gather.
 105      * Spreading kernels also have hardcoded X/Y indices wrapping parameters,
 106      * as a placeholder for implementing 1/2D decomposition.
 107      * The kernels are templated separately for spreading on one grid (one or
 108      * two sets of coefficients) or on two grids (required for energy and virial
 109      * calculations).
 110      */
 111     size_t spreadWorkGroupSize;
 112
 113     PmeKernelHandle splineKernelSingle;
 114     PmeKernelHandle splineKernelThPerAtom4Single;
 115     PmeKernelHandle spreadKernelSingle;
 116     PmeKernelHandle spreadKernelThPerAtom4Single;
 117     PmeKernelHandle splineAndSpreadKernelSingle;
 118     PmeKernelHandle splineAndSpreadKernelThPerAtom4Single;
 119     PmeKernelHandle splineAndSpreadKernelWriteSplinesSingle;
 120     PmeKernelHandle splineAndSpreadKernelWriteSplinesThPerAtom4Single;
 121     PmeKernelHandle splineKernelDual;
 122     PmeKernelHandle splineKernelThPerAtom4Dual;
 123     PmeKernelHandle spreadKernelDual;
 124     PmeKernelHandle spreadKernelThPerAtom4Dual;
 125     PmeKernelHandle splineAndSpreadKernelDual;
 126     PmeKernelHandle splineAndSpreadKernelThPerAtom4Dual;
 127     PmeKernelHandle splineAndSpreadKernelWriteSplinesDual;
 128     PmeKernelHandle splineAndSpreadKernelWriteSplinesThPerAtom4Dual;
 129     //@}
 130
 131     //@{
 132     /** Same for gather: hardcoded X/Y unwrap parameters, order of 4, plus
 133      * it can either reduce with previous forces in the host buffer, or ignore them.
 134      * Also similarly to the gather we can use either order(4) or order*order (16) threads per atom
 135      * and either recalculate the splines or read the ones written by the spread
 136      * The kernels are templated separately for using one or two grids (required for
 137      * calculating energies and virial).
 138      */
 139     size_t gatherWorkGroupSize;
 140
 141     PmeKernelHandle gatherKernelSingle;
 142     PmeKernelHandle gatherKernelThPerAtom4Single;
 143     PmeKernelHandle gatherKernelReadSplinesSingle;
 144     PmeKernelHandle gatherKernelReadSplinesThPerAtom4Single;
 145     PmeKernelHandle gatherKernelDual;
 146     PmeKernelHandle gatherKernelThPerAtom4Dual;
 147     PmeKernelHandle gatherKernelReadSplinesDual;
 148     PmeKernelHandle gatherKernelReadSplinesThPerAtom4Dual;
 149     //@}
 150
 151     //@{
 152     /** Solve kernel doesn't care about the interpolation order, but can optionally
 153      * compute energy and virial, and supports XYZ and YZX grid orderings.
 154      * The kernels are templated separately for grids in state A and B.
 155      */
 156     size_t solveMaxWorkGroupSize;
 157
 158     PmeKernelHandle solveYZXKernelA;
 159     PmeKernelHandle solveXYZKernelA;
 160     PmeKernelHandle solveYZXEnergyKernelA;
 161     PmeKernelHandle solveXYZEnergyKernelA;
 162     PmeKernelHandle solveYZXKernelB;
 163     PmeKernelHandle solveXYZKernelB;
 164     PmeKernelHandle solveYZXEnergyKernelB;
 165     PmeKernelHandle solveXYZEnergyKernelB;
 166     //@}
 167
 168     PmeGpuProgramImpl() = delete;
 169     //! Constructor for the given device
 170     explicit PmeGpuProgramImpl(const DeviceContext& deviceContext);
 171     ~PmeGpuProgramImpl();
 172     GMX_DISALLOW_COPY_AND_ASSIGN(PmeGpuProgramImpl);
 173
 174     //! Return the warp size for which the kernels were compiled
 175     int warpSize() const { return warpSize_; }
 176
 177 private:
 178     // Compiles kernels, if supported. Called by the constructor.
 179     void compileKernels(const DeviceInformation& deviceInfo);
 180 };
 181
 182 #endif