CUDA内核只启动并运行在某些网格大小

我实际上正在使用CUDA，并且正在尝试使用此技术来优化程序。所以我有一个大的内核，我必须在100k +时间和100M +时间或者数十亿之间启动？CUDA内核只启动并运行在某些网格大小

所以我读使用为dim3变量允许线程推出的量（参见：https://devtalk.nvidia.com/default/topic/621867/size-limitation-for-1d-arrays-in-cuda-/?offset=7）

我得到了一个示例代码（在我的gtx970）运行一段时间并且有时没有。

#ifndef PROPAGATORSAT_CUH_ 
# define PROPAGATORSAT_CUH 
# define M_PI (3.14159265358979323846) 
# define TWO_PI (2 * M_PI) 
# define TOTAL_TIME (615359.772) 
# define STEP (0.771) 
# define NB_IT (TOTAL_TIME/(double)STEP) 
# define NB_THREADS (1024) 
# define NB_BLOCKS (int)((NB_IT + NB_THREADS - 1)/NB_THREADS) 

# include <cmath> 
# include <cfloat> 
# include <stdio.h> 
# include "../common/book.h" 
# include "cuda_runtime.h" 
# include "device_launch_parameters.h" 
# define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); } 
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort = true) 
{ 
    if (code != cudaSuccess) 
    { 
     fprintf(stderr, "GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line); 
     if (abort) exit(code); 
    } 
} 

class       Global 
{ 
public: 
    const double    _ITURadEarth = 6378145.0; 
    const double    _ITUGravCst = 3.986012E5; 
    const double    _ITUJ2 = 0.001082636; 
    const double    _J2000AngleDeg = 0;//-79.8058; 
    const double    _J2000AngleRad = 0;//TO_RAD(_J2000AngleDeg); 
    const double    _ITUAngleRateEarthRot = 4.1780745823E-3; 
    const double    _ITUAngleRateEarthRotRad = degToRad(_ITUAngleRateEarthRot); 

public: 
      __device__ double myAsin(double angle); 
    __host__ __device__ double myAcos(double angle); 
    __host__ __device__ double negPiToPi(double angle); 
    __host__ __device__ double degToRad(double angle); 
      __device__ double radToDeg(double angle); 
}; 

class        Cartesian 
{ 
public: 
    double       _X; 
    double       _Y; 
    double       _Z; 

private: 
    double       _m; 

public: 
    __host__ __device__    Cartesian(double x, double y, double z) : _X(x), _Y(y), _Z(z), _m(-1) {} 
}; 

class      Propagator 
{ 
public: 
    double     _iDeg; 
    double     _a; 
    double     _omega_0; 
    double     _OMEGA_0; 
    double     _omega_r; 
    double     _OMEGA_r; 
    double     _rho; 
    double     _SinI; 
    double     _CosI; 
    double     _p; 
    double     _e; 
    double     _ReKm; 
    double     _n0; 
    double     _n_bar; 
    double     _M0; 
    double     _sqrt_e; 
    int      _orbitCase = -1; 
    double     _WdeltaRad; 
    double     _precessionRateRad; 
    double     _artificialPrecessionRad = DBL_MIN; 
    double     _simulationDuration = DBL_MIN; 
    double     _incrementWdeltaRad; 

    void     propagator(double    smaKm, 
             double    incDeg, 
             double    e, 
             double    raanDeg, 
             double    aopDeg, 
             double    trueAnomalyDeg, 
             bool     stationKeeping, 
             double    WdeltaDeg, 
             bool     precessionMechanismSupplied, 
             double    precessionRateDeg); 
    __device__ Cartesian evaluate(double     timeSec, 
            double     simulationDuration, 
            double     artificialPrecessionRad, 
            bool     ECImode); 
    __device__ double  solveKepler(double    M, 
             double    e, 
             double    epsilon); 
    __device__ Cartesian rotateOrbitalElements(Cartesian pq0, 
                double omega, 
                double OMEGA, 
                double CosI, 
                double SinI); 
}; 

#endif /* !PROPAGATORSAT_CUH_ */ 

__host__ __device__ double Global::myAcos(double angle) 
{ 
    return (acos(((angle > 1) ? (1) : (angle < -1) ? (-1) : (angle)))); 
} 

__device__ double Global::myAsin(double angle) 
{ 
    return (asin(((angle > 1) ? (1) : (angle < -1) ? (-1) : (angle)))); 
} 

__host__ __device__ double Global::degToRad(double angle) 
{ 
    return (angle * M_PI/180.0); 
} 

__device__ double Global::radToDeg(double angle) 
{ 
    return (angle * 180.0/M_PI); 
} 

__host__ __device__ double Global::negPiToPi(double angle) 
{ 
    double   output; 

    output = fmod(angle, TWO_PI); 
    output = fmod(angle + TWO_PI, TWO_PI); 
    return ((output > M_PI) ? (output - TWO_PI) : (output)); 
} 

void  Propagator::propagator(double smaKm, double incDeg, double e, double raanDeg, double aopDeg, double trueAnomalyDeg, bool stationKeeping, double WdeltaDeg, bool precessionMechanismSupplied, double precessionRateDeg) 
{ 
    double   iRad, trueAnomalyRad, cosV, E, mu; 
    Global   global; 

    _iDeg = incDeg; 
    iRad = global.degToRad(_iDeg); 
    _CosI = cos(iRad); 
    _SinI = sin(iRad); 
    _e = e; 
    _a = smaKm; 
    trueAnomalyRad = global.degToRad(trueAnomalyDeg); 
    if (e == 0) 
     _M0 = trueAnomalyRad; 
    else 
    { 
     cosV = cos(trueAnomalyRad); 
     E = global.myAcos((e + cosV)/(1 + e * cosV)); 
     if (global.negPiToPi(trueAnomalyRad) < 0) 
      E = M_PI * 2 - E; 
     _M0 = E - e * sin(E); 
    } 
    _OMEGA_0 = global.degToRad(raanDeg); 
    _omega_0 = global.degToRad(aopDeg); 
    _p = _a * (1 - e * e); 
    _ReKm = global._ITURadEarth/1000; 
    mu = global._ITUGravCst; 
    _n0 = sqrt(mu/pow(_a, 3)); 
    _n_bar = _n0 * (1.0 + 1.5 * global._ITUJ2 * pow(_ReKm, 2)/pow(_p, 2) * (1.0 - 1.5 * pow(_SinI, 2)) * pow(1.0 - pow(e, 2), 0.5)); 
    _OMEGA_r = -1.5 * global._ITUJ2 * pow(_ReKm, 2)/pow(_p, 2) * _n_bar * _CosI; 
    _omega_r = 1.5 * global._ITUJ2 * pow(_ReKm, 2)/pow(_p, 2) * _n_bar * (2.0 - 2.5 * pow(_SinI, 2)); 
    _sqrt_e = sqrt((1 + e)/(1 - e)); 
    _WdeltaRad = global.degToRad(WdeltaDeg); 
    _precessionRateRad = global.degToRad(precessionRateDeg); 
    if (stationKeeping == false) 
     _orbitCase = 1; 
    else if (precessionMechanismSupplied == false) 
     _orbitCase = 2; 
    else 
     _orbitCase = 3; 
} 

__device__ Cartesian Propagator::rotateOrbitalElements(Cartesian pq0, double omega, double OMEGA, double CosI, double SinI) 
{ 
    double    CosOMEGA, SinOMEGA, CosOmega, SinOmega, R11, R12, R13, R21, R22, R23, R31, R32, R33, x, y, z; 

    CosOMEGA = cos(OMEGA); 
    SinOMEGA = sin(OMEGA); 
    CosOmega = cos(omega); 
    SinOmega = sin(omega); 
    R11 = CosOMEGA * CosOmega - SinOMEGA * SinOmega * CosI; 
    R12 = -CosOMEGA * SinOmega - SinOMEGA * CosOmega * CosI; 
    R13 = SinOMEGA * SinI; 
    R21 = SinOMEGA * CosOmega + CosOMEGA * SinOmega * CosI; 
    R22 = -SinOMEGA * SinOmega + CosOMEGA * CosOmega * CosI; 
    R23 = -CosOMEGA * SinI; 
    R31 = SinOmega * SinI; 
    R32 = CosOmega * SinI; 
    R33 = CosI; 
    x = R11 * pq0._X + R12 * pq0._Y + R13 * pq0._Z; 
    y = R21 * pq0._X + R22 * pq0._Y + R23 * pq0._Z; 
    z = R31 * pq0._X + R32 * pq0._Y + R33 * pq0._Z; 
    Cartesian   cart = Cartesian(x, y, z); 

    return (cart); 
} 
__device__ Cartesian Propagator::evaluate(double timeSec, double simulationDuration, double artificialPrecessionRad, bool ECImode = true) 
{ 
    double    M, E, v, cosV, sinV, rotationAngleECF, omega, OMEGA; 
    Global    global; 

    if (_simulationDuration != simulationDuration || _artificialPrecessionRad != artificialPrecessionRad) 
    { 
     _simulationDuration = simulationDuration; 
     _artificialPrecessionRad = artificialPrecessionRad; 
     _incrementWdeltaRad = (_WdeltaRad * 2)/_simulationDuration; 
    } 
    M = _M0 + ((_orbitCase == 3) ? _n0 : _n_bar) * timeSec; 
    E = E = (_e == 0) ? M : solveKepler(M, _e, 1e-8); 
    v = 2.0 * atan(_sqrt_e * tan(E/2)); 
    cosV = cos(v); 
    sinV = sin(v); 
    _rho = _p/(1 + _e * cosV); 
    rotationAngleECF = (ECImode) ? 0 : -1 * (global._J2000AngleRad + timeSec * global._ITUAngleRateEarthRotRad); 
    omega = _omega_0 + ((_orbitCase == 3) ? 0 : _omega_r * timeSec); 
    OMEGA = _OMEGA_0 + rotationAngleECF + ((_orbitCase == 3) ? 0 : _OMEGA_r * timeSec); 
    if (_orbitCase == 1) 
     OMEGA += artificialPrecessionRad * timeSec; 
    else if (_orbitCase == 2) 
     OMEGA += _WdeltaRad * ((2.0 * timeSec/_simulationDuration) - 1); 
    else if (_orbitCase == 3) 
     OMEGA += _precessionRateRad * timeSec - _WdeltaRad + _incrementWdeltaRad * timeSec; 
    Cartesian pq0 = Cartesian(1000 * _rho * cosV, 1000 * _rho * sinV, 0); 

    Cartesian positionECI = Propagator::rotateOrbitalElements(pq0, omega, OMEGA, _CosI, _SinI); 

    return (positionECI); 
} 
__device__ double Propagator::solveKepler(double M, double e, double epsilon) 
{ 
    double   En, Ens; 

    En = M; 
    Ens = En - (En - e * sin(En) - M)/(1 - e * cos(En)); 
    while (abs(Ens - En) > epsilon) 
    { 
     En = Ens; 
     Ens = En - (En - e * sin(En) - M)/(1 - e * cos(En)); 
    } 
    return (Ens); 
} 

__global__ void kernel(Propagator *CUDA_prop) 
{ 
    size_t  tid; 

    tid = (blockIdx.x + blockIdx.y * gridDim.x + gridDim.x * gridDim.y * blockIdx.z) * blockDim.x + threadIdx.x; 
    //if (tid < NB_IT) 
    Cartesian positionNGSOsatECI = CUDA_prop[0].evaluate(STEP * tid, 615359.772, 0); 
} 

int  main(void) 
{ 
    cudaEvent_t  start, stop; 
    HANDLE_ERROR(cudaEventCreate(&start)); 
    HANDLE_ERROR(cudaEventCreate(&stop)); 
    HANDLE_ERROR(cudaEventRecord(start, 0)); 
    Propagator prop[1], *CUDA_prop; 
    dim3  block(1000, 1, 1); 
    dim3  thread(1024, 1, 1); 

    prop[0].propagator(7847.3, 53, 0, 18, 0, 67.5, true, 5, true, 3.4000000596279278E-05); 
    HANDLE_ERROR(cudaMalloc((void **)&CUDA_prop, sizeof(Propagator))); 
    HANDLE_ERROR(cudaMemcpy(CUDA_prop, prop, sizeof(Propagator), cudaMemcpyHostToDevice)); 
    kernel <<< block, thread >>> (CUDA_prop); 
    gpuErrchk(cudaPeekAtLastError()); 
    gpuErrchk(cudaDeviceSynchronize()); 
    HANDLE_ERROR(cudaFree(CUDA_prop)); 
    HANDLE_ERROR(cudaEventRecord(stop, 0)); 
    HANDLE_ERROR(cudaEventSynchronize(stop)); 
    float   elapsedTime; 
    HANDLE_ERROR(cudaEventElapsedTime(&elapsedTime, start, stop)); 
    printf("time : %f ms\n", elapsedTime); 
    HANDLE_ERROR(cudaEventDestroy(start)); 
    HANDLE_ERROR(cudaEventDestroy(stop)); 
    return (0); 
}

如果我启动这个数量的“线程？”它工作到大约300k块。但有时它的数量不一样。我得到一个错误：“未知错误”从线路：

gpuErrchk(cudaDeviceSynchronize());

或cudaFree，或内核调用后一些功能。在这里输入代码

如果我只用1k块和1k线程启动并使用cuda-memcheck我得到与以前相同的错误但没有cuda-memcheck它运行得很好。

我不知道是什么导致这个问题，以及如何解决它

注：了handle_error宏可以通过gpuErrchk maccro改变，这是从做同样的事情库中的定义

而且我也想知道如何确定我可以使用硬件或任何其他规格启动的最大线程数量。

来源

2016-09-26 Nico Wadel

听起来很像[看门狗定时器]（https://devtalk.nvidia.com/default/topic/459869/cuda-programming-and-performance/-quot-display-driver-stopped-responding-and- has-recovered-quot-wddm-timeout-detection-and-recovery-/）踢进来。内核运行多久才失败？ – tera

您的GPU是专用计算设备还是您也将其用于显示？ – talonmies

我也使用我的GPU进行显示，内核为300k块和1024线程运行约2100毫秒，100k块和1024线程运行约700毫秒。对于看门狗，我看起来更早，它被设置为6或7秒。当它失败时，它不会运行超过3秒 –

在使用WDDM驱动程序的Windows上，可以批量启动多个内核以减少启动开销。由于看门狗定时器适用于整个批处理，即使每个内核本身在选定的超时值内完成，也可能会触发超时。

到目前为止，强制立即执行所有内核的廉价方法是调用cudaStreamQuery(0)。不同于调用cudaDeviceSynchronize()，这将立即返回，而不是等待内核完成。

内核调用之间的散列cudaStreamQuery(0)因此确保WDDM超时只适用于两个cudaStreamQuery(0)调用之间的内核。

如果即使单个内核需要很长时间并触发看门狗，也可以尝试将其分割为多个调用，每个调用的块数较少，然后再次调用cudaStreamQuery(0)。这不仅使监督人员感到高兴，而且也使GUI具有一定的反应性。

来源

2016-09-26 12:36:45 tera

我做了你所说的。在多个内核调用之间以及甚至之后使用cudaStreamQuery，我将内核分割为多个内核来启动较少的块。但是我也遇到了同样的问题。 5分钟前我可以启动<<< 400000，1024 >>>现在我甚至不能启动<<< 200000,1024 >>>，我试过了15秒后我可以启动<<< 200000,1024 >>> kernel ... –

然后再减少块数。我个人会在慢速或0.01s的速度上瞄准像0.1s这样的快速GPU，这样a）保持GUI模糊响应，b）仍然远离看门狗超时，c）即使升级到GPU速度提升10倍。 – tera

或者获得独立的GPU进行显示，并将GPU置于TCC模式（如果支持，则切换到Linux :)）。 – tera

CUDA内核只启动并运行在某些网格大小

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