def softmax(x): # x[ M, K] e_x = exp(x - max(x, axis=1)) # 这里首先对K个元素进行 return e_x / e_x.sum(axis=1) 这里的减法和除法都是元素操作(elementwise operation),对于多线程并行计算的时候,让每一个元素都执行相同的操作就可以。 此外这里还有求取最大值和求和的规约操作(reductio...
Max threads per block: 1024 Max thread dimensions: (1024, 1024, 64) Max grid dimensions: (2147483647, 65535, 65535) 可以看到 Global Mem 是 25430786048 bytes 约等于 24GB,计算能力是 8.6,符合 RTX 3090 的规格。 3.X Inline Device Function 最后是一些个人补充部分,关于 nvcc 的 inline,我们这里可...
) = cuInit(0)# Get attributeserr, DEVICE_NAME = cuDeviceGetName(128, 0)DEVICE_NAME = DEVICE_NAME.decode("ascii").replace("\x00", "")err, MAX_THREADS_PER_BLOCK = cuDeviceGetAttribute( CUdevice_attribute.
__device__ float GetElement(const Matrix A, int row, int col) { return A.elements[row * A.stride + col]; } // Set a matrix element __device__ void SetElement(Matrix A, int row, int col, float value) { A.elements[row * A.stride + col] = value; } // Get the BLOCK_SIZE...
37.4 Conclusion Modern GPU hardware is highly capable of use in financial simulation. In this chapter, we have discussed approaches for generating random numbers for these kinds of simulation. Wallace's method provides good performance while maintaining a high quality of random numbers...
T* pElement = (T*)((char*)BaseAddress + Row * pitch) + Column; For allocations of 2D arrays, it is recommended that programmers consider performing pitch allocations using cudaMallocPitch(). Due to pitch alignment restrictions in the hardware, this is especially true if the application...
GPU加速应用程序与CPU应用程序对比:在CPU应用程序中,数据在CPU上分配,并且所有工作均在CPU上执行,而在加速应用程序中,则可使用cudaMallocManaged()分配数据,其数据可由CPU进行访问和处理,并能自动迁移至可执行并行工作的GPU,GPU异步执行工作,与此同时CPU可执行它的工作,通过cudaDeviceSynchronize(), CPU代码可与异步GP...
性能够高,应用这套 Elementwise 模板的算子都能打满机器的带宽,速度也够快。 开发效率高,开发人员可以不用过分关注 CUDA 逻辑及相关优化手段,只需要编写计算逻辑即可。 可扩展性强,目前这套模板支持了一元,二元,三元操作。若今后有需求拓展,支持更多输入时,只需要仿照编写对应的工厂即可。
CUDA Runtime 27 CUDA C++ Programming Guide, Release 12.8 size_t pitch, int width, int height) { for (int r = 0; r < height; ++r) { float* row = (float*)((char*)devPtr + r * pitch); for (int c = 0; c < width; ++c) { float element = row[c]; } } } (continued ...
} }; class Sub { public: __device__ float operator() (float a, float b) const { return a - b; } }; // Device code template<class O> __global__ void VectorOperation(const float * A, const float * B, float * C, unsigned int N, O op) { unsigned in...