在下面的代码中,G2 = G .* G和G2 = G * G有什么区别?为什么我得到的第一个代码GPU负载100%,第二个我得到GPU负载和内存控制器负载传感器,两者都在100%上的gpu?
X = rand(5000, 'double');
G = gpuArray(X);
classUnderlying(G) % Returns 'single'
for m = 1:5000
G2 = G .* G .* G .* G; % Performed on GPU
end
whos G2
我有以下全局内核:
__global__ void pdegpu(PDE_ParabolicD1_Num_GPU **pdes)
{
PDE_ParabolicD1_Num_GPU *loc;
loc = new PDE_ParabolicD1_Num_GPU();
loc->Setup();
delete loc;
//above code was just an example to show that new and delete work fine
*pdes = new PDE_ParabolicD1_Num_GPU()
我需要计算GPU运行时代码,以及总运行代码(主机和设备)。在我的代码中,我有两个gpu内核在运行,在主机for循环之间复制数据,下面的示例可以显示我的代码是什么样子的。
cuda event start
//FIRST kernel code call <<...>>
// cuda memory copy result back from device to host
CudadeviceSyncronize()
// copy host data to host array (CPU funtion loop)
// cuda memory copy fr
我已经写了一个简单的Halide代码来计算从0到n的数字的平方,然而它在GPU上比在CPU上多花22倍的时间。
#include"stdafx.h"
#include "Halide.h"
#include <stdio.h>
using namespace Halide;
#include "HalideRuntimeOpenCL.h"
#define GPU_TILE 16
#define COMPUTE_SIZE 1024
Target find_gpu_target();
// Define some Vars to u
在tensorflow关于的指南中,有一段代码如下:
import time
def measure(x, steps):
# TensorFlow initializes a GPU the first time it's used, exclude from timing.
tf.matmul(x, x)
start = time.time()
for i in range(steps):
x = tf.matmul(x, x)
_ = x.numpy() # Make sure to execute op and not just enq
我正在尝试获取tensorflow模型的内存使用情况,正在从冻结的pb文件中加载模型:
import tensorflow as tf
def load_graph_def(model_filepath):
# Expects frozen graph in .pb format
with tf.gfile.GFile(model_filepath, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
return graph
我得到以下错误,建立在与kubespray。
helm安装--等等--生成名为nvidia/gpu的操作员
Error: INSTALLATION FAILED: rendered manifests contain a resource that already exists. Unable to continue with install: CustomResourceDefinition "nodefeaturerules.nfd.k8s-sigs.io" in namespace "" exists and cannot be imported into
我有个结构
struct packet
{
int src_ip;
int dest_ip;
int src_port;
int dest_port;
int protocol;
};
cuda内核如下所示:
__global__
void GPU(struct packet * packets,int * gpu_action)
{
int i;
i = (int) packets[6]->src_ip;
}
主要职能如下:
int main ()
{
int * gpu_action;
struct pack