Search - kernel

Search - kernel - List

[MPI] mc8051_core DL : 0: 一个用VHDL写的8051的内核,很方便集成到FPGA里.-a written VHDL 8051 kernel, is a convenient integrated into the FPGA Lane.
Date : 2026-01-10 Size : 385kb User : 武第
[MPI] myFirstKernel DL : 0: 在gpu上实现载入核函数运行并检查是否运行成功-To achieve the gpu load kernel and run a successful check run
Date : 2026-01-10 Size : 2kb User : 布谷
[MPI] test3 DL : 0: 基于MPI的卷积计算,数据矩阵：256*256 –原始矩阵设置：随机50个点设置为255，其余为0  卷积核：5*5 –明确注明所设计的卷积核  迭代次数：100 –每5次迭代保存一次数据矩阵 –通过20个结果矩阵，生成动画-MPI-based convolution calculation, data matrix: 256* 256- the original matrix setting: Random 50 points set to 255, the rest is 0  convolution kernel: 5* 5- clearly indicate the design of convolution kernel  iterations : 100- once every five iterations saved data matrix- matrix through 20 results generated animation
Date : 2026-01-10 Size : 3kb User : 路东英
[MPI] mini3 DL : 0: opencl kernel, you ll be surprised
Date : 2026-01-10 Size : 1.6mb User : John
[MPI] svm-grid_openmp DL : 0: 支持向量机SVM gridr.py 核函数自动寻优多核并行程序 wen7 32位系统-Support Vector Machine SVM gridr.py kernel function automatically optimizing multicore parallel programming wen7 32-bit systems
Date : 2026-01-10 Size : 103kb User : yuan-chen
[MPI] kernel DL : 0: 利用cuda对图像像素进行简单并行运算，以图像像素rgb值反映运行结果-Use a simple image pixel cuda parallel computing, image pixel rgb value to reflect the operating results
Date : 2026-01-10 Size : 1kb User : 蒋代码
[MPI] kernel DL : 0: 这是一段CUDA并行计算的例子，内容是矢量求和-Parallel computing
Date : 2026-01-10 Size : 1kb User : 工大的猫猫
[MPI] parallel_PI DL : 0: 调用核函数，对PI的数值求解进行并行计算，对gpu并行计算的初学者有一定的启发-Call the kernel function, PI values for solving parallel computing, parallel computing gpu beginners have some inspiration
Date : 2026-01-10 Size : 3.74mb User : 刘欧
[MPI] 1.2 DL : 0: CUDA 测试范例使用宏进行GPU端内核报错侦测-CUDA test sample Using macros for GPU kernel error detection
Date : 2026-01-10 Size : 1kb User : 熊风
[MPI] cudamarix-mul DL : 0: 用cuda写的矩阵相乘，包含分块和共享内存的核函数，请大家参照-Written by cuda matrix multiplication, and shared memory block containing the kernel function, please reference
Date : 2026-01-10 Size : 1kb User : 徐晓东
[MPI] Asy_test DL : 0: opencl实现任务并行，流水线操作。3个kernel同时运行-the opencl overlap
Date : 2026-01-10 Size : 12kb User : 王珂
[MPI] myFirstKernel DL : 0: 启动内核--从“myFirstKernel”模板开始。 Part1：使用指针d_a为内核的结果分配设备内存。 Part2：使用1-D的1-D网格来配置和启动内核线程块。 Part3：让每个线程设置一个d_a的元素，如下所示： idx = blockIdx.x * blockDim.x + threadIdx.x d_a [idx] = 1000 * blockIdx.x + threadIdx.x Part4：将d_a中的结果复制回主机指针h_a。 Part5：验证结果是否正确。(Start the kernel -- start with the myFirstKernel template. Part1: use pointer d_a to allocate device memory for the results of the kernel. Part2: use the 1-D 1-D grid to configure and start the kernel Thread block. Part3: let each thread set an element of d_a, as shown below: IDX = blockIdx.x * blockDim.x + threadIdx.x d_a = [idx] = 1000 * blockIdx.x + threadIdx.x Part4: copy the results from d_a back to the host pointer h_a. Part5: is the validation correct?.)
Date : 2026-01-10 Size : 6kb User : p-yang
[MPI] reverseArray_singleblock DL : 0: 反向阵列(单块)-- 给定指针d_a中的输入数组{a0，a1，...，an-1}，将反向数组{an-1，an-2，...，a0}存储在指针d_b中 A: 从“reverseArray_singleblock”模板开始 B: 只有一个线程块启动，以反转一个大小的数组 N = numThreads = 256个元素 C: 第1部分（共1个）：所有你需要做的是实现内核的“reverseArrayBlock（）” D:每个线程将单个元件移动到相反的位置, 从d_a指标读取输入,在d_b指标中将输出存储在相反的位置(Reverse array (single block) - the input array {a0, A1,..., an-1} in the given pointer d_a, the reverse array {an-1, An-2,..., a0}, stored in the pointer d_b A: starts with the "reverseArray_singleblock" template B: has only one thread block to start to reverse an array of sizes N = numThreads = 256 elements C: first parts (1 altogether): all you need to do is implement the kernel's reverseArrayBlock ()" D: each thread moves a single element in the opposite position, reads input from the d_a index, and outputs the output in the opposite position in the d_b index)
Date : 2026-01-10 Size : 6kb User : p-yang
[MPI] reverseArray_multiblock DL : 0: 反向阵列（multiblock）: 给定指针d_a中的输入数组{a0，a1，...，an-1}，将反向数组{an-1，an-2，...，a0}存储在指针d_b中 A: 从“reverseArray_multiblock”模板开始 B:多个256线程块启动,要颠倒大小为N，N / 256块的数组第1部分：计算要启动的块数第2部分：实现内核reverseArrayBlock 请注意，现在您必须同时进行计算块内的相反位置反向偏移到块的开始(Reverse array (multiblock): Given the pointer d_a in the input array {a0, A1,..., an-1}, the reverse array {an-1, An-2,..., a0} stored in the pointer d_b A: starts with the "reverseArray_multiblock" template B: more than 256 thread block start, to reverse the size of N, N / 256 block array The first part: the number of calculation to start The second part: implementation of kernel reverseArrayBlock Attention, now you must calculate at the same time The reverse position in a block Reverse offset to block start)
Date : 2026-01-10 Size : 6kb User : p-yang
[MPI] kernel DL : 0: 1,使用VS创建一个工程 2，复制文件代码 3、使用内核cuda函数使用内核cuda函数调试专用(Use kernel CUDA function to use kernel CUDA function to debug special-purpose)
Date : 2026-01-10 Size : 1kb User : 专研