thrust::device_vector not working - cuda

I have written a code using Thrust. I am pasting the code and its output below. Strangely, when the device_vector line is reached during exectution the screen just hangs and no more output comes. It was working in the morning. Please help me.
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <iostream>
int main(void)
{
// H has storage for 4 integers
thrust::host_vector<int> H(4);
// initialize individual elements
H[0] = 14;
H[1] = 20;
H[2] = 38;
H[3] = 46;
// H.size() returns the size of vector H
std::cout << "H has size " << H.size() << std::endl;
// print contents of H
for(size_t i = 0; i < H.size(); i++)
std::cout << "H[" << i << "] = " << H[i] << std::endl;
// resize H
H.resize(2);
std::cout << "H now has size " << H.size() << std::endl;
// Copy host_vector H to device_vector D
thrust::device_vector<int> D = H;
// elements of D can be modified
D[0] = 99;
D[1] = 88;
// print contents of D
for(size_t i = 0; i < D.size(); i++)
std::cout << "D[" << i << "] = " << D[i] << std::endl;
// H and D are automatically deleted when the function returns
return 0;
}
The output is :
H has size 4
H[0] = 14
H[1] = 20
H[2] = 38
H[3] = 46
H now has size 2
* After this nothing happens


Run Device Query. I am confident that if the code was working in the morning, the problem is due to the graphics card.

Related

What is the behavior of the vector here?

I am unable to comprehend why in the test shown below, iterator p never reaches the end and therefore the loop breaks only when k = 20? What exactly is the push_back doing to cause undefined behavior? Is it because the vector dynamically allocated a bunch of additional storage for the new elements I want to use, and the amount is not necessarily the amount I will use?
#include <iostream>
#include <vector>
#include <list>
using namespace std;
const int MAGIC = 11223344;
void test()
{
bool allValid = true;
int k = 0;
vector<int> v2(5, MAGIC);
k = 0;
for (vector<int>::iterator p = v2.begin(); p != v2.end(); p++, k++)
{
if (k >= 20) // prevent infinite loop
break;
if (*p != MAGIC)
{
cout << "Item# " << k << " is " << *p << ", not " << MAGIC <<"!" << endl;
allValid = false;
}
if (k == 2)
{
for (int i = 0; i < 5; i++)
v2.push_back(MAGIC);
}
}
if (allValid && k == 10)
cout << "Passed test 3" << endl;
else
cout << "Failed test 3" << "\n" << k << endl;
}
int main()
{
test();
}

Insertion to a vector while iterating over it is really a bad idea. Data insertion may cause memory reallocation that invalidates iterators. In this case, the capacity was not enough to insert additional elements, which caused memory allocation with a different address. You can check it yourself:
void test()
{
bool allValid = true;
int k = 0;
vector<int> v2(5, MAGIC);
k = 0;
for (vector<int>::iterator p = v2.begin(); p != v2.end(); p++, k++)
{
cout << v2.capacity() << endl; // Print the vector capacity
if (k >= 20) // prevent infinite loop
break;
if (*p != MAGIC) {
//cout << "Item# " << k << " is " << *p << ", not " << MAGIC <<"!" << endl;
allValid = false;
}
if (k == 2) {
for (int i = 0; i < 5; i++)
v2.push_back(MAGIC);
}
}
if (allValid && k == 10)
cout << "Passed test 3" << endl;
else
cout << "Failed test 3" << "\n" << k << endl;
}
This code will output something like the following:
5
5
5
10 <-- the capacity has changed
10
... skipped ...
10
10
Failed test 3
20
We can see that where k is equal to 2 (third line), the capacity of the vector doubled (fourth line) because we are adding new elements. The memory is redistributed, and the vector elements are most likely now located elsewhere. You can also check it by printing vector base address with data member function instead of capacity:
Address: 0x136dc20 k: 0
Address: 0x136dc20 k: 1
Address: 0x136dc20 k: 2
Address: 0x136e050 k: 3 <-- the address has changed
Address: 0x136e050 k: 4
... skipped ...
Address: 0x136e050 k: 19
Address: 0x136e050 k: 20
Failed test 3
20
The code is poorly written, you can make it more robust by using indices instead of iterators.

How to cope with "cudaErrorMissingConfiguration" from "cudaMallocPitch" function of CUDA?

I'm making a Mandelbrot set program with CUDA. However I can't step more unless cudaErrorMissingConfiguration from cudaMallocPitch() function of CUDA is to be solved. Could you tell me something about it?
My GPU is GeForce RTX 2060 SUPER.
I'll show you my command lines below.
> nvcc MandelbrotCUDA.cu -o MandelbrotCUDA -O3
I tried cudaDeviceSetLimit( cudaLimitMallocHeapSize, 7*1024*1024*1024 ) to
resize heap size.
cudaDeviceSetLimit was success.
However I cannot step one more. I cannot print "CUDA malloc done!"
#include <iostream>
#include <thrust/complex.h>
#include <fstream>
#include <string>
#include <stdlib.h>
using namespace std;
#define D 0.0000025 // Tick
#define LIMIT_N 255
#define INF_NUM 2
#define PLOT_METHOD 2 // dat file : 0, ppm file : 1, ppm file with C : 2
__global__
void calculation(const int indexTotalX, const int indexTotalY, int ***n, thrust::complex<double> ***c){ // n, c are the pointers of dN, dC.
for(int i = 0; i < indexTotalY ; i++){
for(int j = 0; j < indexTotalX; j++){
thrust::complex<double> z(0.0f, 0.0f);
n[i][j] = 0;
for(int ctr=1; ctr <= LIMIT_N ; ctr++){
z = z*z + (*(c[i][j]));
n[i][j] = n[i][j] + (abs(z) < INF_NUM);
}
}
}
}
int main(){
// Data Path
string filePath = "Y:\\Documents\\Programming\\mandelbrot\\";
string fileName = "mandelbrot4.ppm";
string filename = filePath+fileName;
//complex<double> c[N][M];
double xRange[2] = {-0.76, -0.74};
double yRange[2] = {0.05, 0.1};
const int indexTotalX = (xRange[1]-xRange[0])/D;
const int indexTotalY = (yRange[1]-yRange[0])/D;
thrust::complex<double> **c;
//c = new complex<double> [N];
cout << "debug_n" << endl;
int **n;
n = new int* [indexTotalY];
c = new thrust::complex<double> * [indexTotalY];
for(int i=0;i<indexTotalY;i++){
n[i] = new int [indexTotalX];
c[i] = new thrust::complex<double> [indexTotalX];
}
cout << "debug_n_end" << endl;
for(int i = 0; i < indexTotalY; i++){
for(int j = 0; j < indexTotalX; j++){
thrust::complex<double> tmp( xRange[0]+j*D, yRange[0]+i*D );
c[i][j] = tmp;
//n[i*sqrt(N)+j] = 0;
}
}
// CUDA malloc
cout << "CUDA malloc initializing..." << endl;
int **dN;
thrust::complex<double> **dC;
cudaError_t error;
error = cudaDeviceSetLimit(cudaLimitMallocHeapSize, 7*1024*1024*1024);
if(error != cudaSuccess){
cout << "cudaDeviceSetLimit's ERROR CODE = " << error << endl;
return 0;
}
size_t tmpPitch;
error = cudaMallocPitch((void **)dN, &tmpPitch,(size_t)(indexTotalY*sizeof(int)), (size_t)(indexTotalX*sizeof(int)));
if(error != cudaSuccess){
cout << "CUDA ERROR CODE = " << error << endl;
cout << "indexTotalX = " << indexTotalX << endl;
cout << "indexTotalY = " << indexTotalY << endl;
return 0;
}
cout << "CUDA malloc done!" << endl;
This is console messages below.
debug_n
debug_n_end
CUDA malloc initializing...
CUDA ERROR CODE = 1
indexTotalX = 8000
indexTotalY = 20000

There are several problems here:
int **dN;
...
error = cudaMallocPitch((void **)dN, &tmpPitch,(size_t)(indexTotalY*sizeof(int)), (size_t)(indexTotalX*sizeof(int)));
The correct type of pointer to use in CUDA allocations is a single pointer:
int *dN;
not a double pointer:
int **dN;
(so your kernel where you are trying pass triple-pointers:
void calculation(const int indexTotalX, const int indexTotalY, int ***n, thrust::complex<double> ***c){ // n, c are the pointers of dN, dC.
is almost certainly not going to work, and should not be designed that way, but that is not the question you are asking.)
The pointer is passed to the allocating function by its address:
error = cudaMallocPitch((void **)&dN,
For cudaMallocPitch, only the horizontal requested dimension is scaled by the size of the data element. The allocation height is not scaled this way. Also, I will assume X corresponds to your allocation width, and Y corresponds to your allocation height, so you also have those parameters reversed:
error = cudaMallocPitch((void **)&dN, &tmpPitch,(size_t)(indexTotalX*sizeof(int)), (size_t)(indexTotalY));
The cudaLimitMallocHeapSize should not be necessary to set to make any of this work. It applies only to in-kernel allocations. Reserving 7GB on an 8GB card may also cause problems. Until you are sure you need that (it's not needed for what you have shown) I would simply remove that.
$ cat t1488.cu
#include <iostream>
#include <thrust/complex.h>
#include <fstream>
#include <string>
#include <stdlib.h>
using namespace std;
#define D 0.0000025 // Tick
#define LIMIT_N 255
#define INF_NUM 2
#define PLOT_METHOD 2 // dat file : 0, ppm file : 1, ppm file with C : 2
__global__
void calculation(const int indexTotalX, const int indexTotalY, int ***n, thrust::complex<double> ***c){ // n, c are the pointers of dN, dC.
for(int i = 0; i < indexTotalY ; i++){
for(int j = 0; j < indexTotalX; j++){
thrust::complex<double> z(0.0f, 0.0f);
n[i][j] = 0;
for(int ctr=1; ctr <= LIMIT_N ; ctr++){
z = z*z + (*(c[i][j]));
n[i][j] = n[i][j] + (abs(z) < INF_NUM);
}
}
}
}
int main(){
// Data Path
string filePath = "Y:\\Documents\\Programming\\mandelbrot\\";
string fileName = "mandelbrot4.ppm";
string filename = filePath+fileName;
//complex<double> c[N][M];
double xRange[2] = {-0.76, -0.74};
double yRange[2] = {0.05, 0.1};
const int indexTotalX = (xRange[1]-xRange[0])/D;
const int indexTotalY = (yRange[1]-yRange[0])/D;
thrust::complex<double> **c;
//c = new complex<double> [N];
cout << "debug_n" << endl;
int **n;
n = new int* [indexTotalY];
c = new thrust::complex<double> * [indexTotalY];
for(int i=0;i<indexTotalY;i++){
n[i] = new int [indexTotalX];
c[i] = new thrust::complex<double> [indexTotalX];
}
cout << "debug_n_end" << endl;
for(int i = 0; i < indexTotalY; i++){
for(int j = 0; j < indexTotalX; j++){
thrust::complex<double> tmp( xRange[0]+j*D, yRange[0]+i*D );
c[i][j] = tmp;
//n[i*sqrt(N)+j] = 0;
}
}
// CUDA malloc
cout << "CUDA malloc initializing..." << endl;
int *dN;
thrust::complex<double> **dC;
cudaError_t error;
size_t tmpPitch;
error = cudaMallocPitch((void **)&dN, &tmpPitch,(size_t)(indexTotalX*sizeof(int)), (size_t)(indexTotalY));
if(error != cudaSuccess){
cout << "CUDA ERROR CODE = " << error << endl;
cout << "indexTotalX = " << indexTotalX << endl;
cout << "indexTotalY = " << indexTotalY << endl;
return 0;
}
cout << "CUDA malloc done!" << endl;
}
$ nvcc -o t1488 t1488.cu
t1488.cu(68): warning: variable "dC" was declared but never referenced
$ cuda-memcheck ./t1488
========= CUDA-MEMCHECK
debug_n
debug_n_end
CUDA malloc initializing...
CUDA malloc done!
========= ERROR SUMMARY: 0 errors
$

Modifying zip iterator with eigen::Matrix gives errenous results

I have three set of points X,Y,Z. I intend to apply a transform using Eigen::Matrix4f. I use a zip iterator and a transform operator to do it. The program compiles however the result is only partially correct. this post is inspired by How to modify the contents of a zip iterator
The transformation of
A= [0 1 2;3 4 5;6 7 8; 1 1 1] with M=[1 2 3 4;5 6 7 8;9 10 11 12;13 14 15 16] using M*A should result to: R=[28 34 40; 68 86 104; 108 138 168]
However it gives: R=[28 34 40; 208 251 294; 2410 2905 3400].
The X values are being correctly modified. However the Y & Z values are faulty.
My code and cmakelists is as below:
#include <thrust/iterator/zip_iterator.h>
#include <thrust/execution_policy.h>
#include <thrust/copy.h>
#include <thrust/device_vector.h>
#include <Eigen/Dense>
#include <iostream>
typedef thrust::device_vector<float>::iterator FloatIterator;
typedef thrust::tuple<FloatIterator, FloatIterator, FloatIterator> FloatIteratorTuple;
typedef thrust::zip_iterator<FloatIteratorTuple> Float3Iterator;
typedef thrust::tuple<float,float,float> Float3;
struct modify_tuple
{
Eigen::Matrix4f _Mat4f;
modify_tuple(Eigen::Matrix4f Mat4f) : _Mat4f(Mat4f) { }
__host__ __device__ Float3 operator()(Float3 a) const
{
Eigen::Vector4f V(thrust::get<0>(a), thrust::get<1>(a), thrust::get<2>(a), 1.0);
V=_Mat4f*V;
Float3 res=thrust::make_tuple( V(0,0), V(1,0), V(2,0) );
return res;
}
};
int main(void)
{
thrust::device_vector<float> X(3);
thrust::device_vector<float> Y(3);
thrust::device_vector<float> Z(3);
X[0]=0, X[1]=1, X[2]=2;
Y[0]=4, Y[1]=5, Y[2]=6;
Z[0]=7, Z[1]=8, Z[2]=9;
std::cout << "X,Y,Z before transformation="<< std::endl;
thrust::copy_n(X.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::copy_n(Y.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::copy_n(Z.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
Float3Iterator P_first = thrust::make_zip_iterator(make_tuple(X.begin(), Y.begin(), Z.begin()));
Float3Iterator P_last = thrust::make_zip_iterator(make_tuple(X.end(), Y.end(), Z.end()));
Eigen::Matrix4f M;
M(0,0)= 1; M(0,1)= 2; M(0,2)= 3; M(0,3)= 4;
M(1,0)= 5; M(1,1)= 6; M(1,2)= 7; M(1,3)= 8;
M(2,0)= 9; M(2,1)= 10; M(2,2)= 11; M(2,3)= 12;
M(3,0)= 13; M(3,1)= 14; M(3,2)= 15; M(3,3)= 16;
thrust::transform(thrust::device, P_first,P_last, P_first, modify_tuple(M));
std::cout << "X, Y, Z after transformation="<< std::endl;
thrust::copy_n(X.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::copy_n(Y.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::copy_n(Z.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
return 0;
}
CMakeLists.txt
CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
FIND_PACKAGE(CUDA REQUIRED)
INCLUDE_DIRECTORIES(${CUDA_INCLUDE_DIRS})
INCLUDE_DIRECTORIES (/usr/include/eigen3)
set(
CUDA_NVCC_FLAGS
${CUDA_NVCC_FLAGS};
-O3 -gencode arch=compute_52,code=sm_52;
)
CUDA_ADD_EXECUTABLE(modify_zip_iterator_stackoverflow_ver2 modify_zip_iterator_stackoverflow_ver2.cu)
TARGET_LINK_LIBRARIES(modify_zip_iterator_stackoverflow_ver2 ${CUDA_LIBRARIES})

Probably you just need to get the latest Eigen.
I used CUDA 9.2 on Fedora27, and grabbed the latest eigen from here.
Then I compiled and ran your code as follows:
$ cat t21.cu
#include <thrust/iterator/zip_iterator.h>
#include <thrust/execution_policy.h>
#include <thrust/copy.h>
#include <thrust/device_vector.h>
#include <Eigen/Dense>
#include <iostream>
typedef thrust::device_vector<float>::iterator FloatIterator;
typedef thrust::tuple<FloatIterator, FloatIterator, FloatIterator> FloatIteratorTuple;
typedef thrust::zip_iterator<FloatIteratorTuple> Float3Iterator;
typedef thrust::tuple<float,float,float> Float3;
struct modify_tuple
{
Eigen::Matrix4f _Mat4f;
modify_tuple(Eigen::Matrix4f Mat4f) : _Mat4f(Mat4f) { }
__host__ __device__ Float3 operator()(Float3 a) const
{
Eigen::Vector4f V(thrust::get<0>(a), thrust::get<1>(a), thrust::get<2>(a), 1.0);
V=_Mat4f*V;
Float3 res=thrust::make_tuple( V(0,0), V(1,0), V(2,0) );
return res;
}
};
int main(void)
{
thrust::device_vector<float> X(3);
thrust::device_vector<float> Y(3);
thrust::device_vector<float> Z(3);
X[0]=0, X[1]=1, X[2]=2;
Y[0]=4, Y[1]=5, Y[2]=6;
Z[0]=7, Z[1]=8, Z[2]=9;
std::cout << "X,Y,Z before transformation="<< std::endl;
thrust::copy_n(X.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::copy_n(Y.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::copy_n(Z.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
Float3Iterator P_first = thrust::make_zip_iterator(make_tuple(X.begin(), Y.begin(), Z.begin()));
Float3Iterator P_last = thrust::make_zip_iterator(make_tuple(X.end(), Y.end(), Z.end()));
Eigen::Matrix4f M;
M(0,0)= 1; M(0,1)= 2; M(0,2)= 3; M(0,3)= 4;
M(1,0)= 5; M(1,1)= 6; M(1,2)= 7; M(1,3)= 8;
M(2,0)= 9; M(2,1)= 10; M(2,2)= 11; M(2,3)= 12;
M(3,0)= 13; M(3,1)= 14; M(3,2)= 15; M(3,3)= 16;
thrust::transform(thrust::device, P_first,P_last, P_first, modify_tuple(M));
std::cout << "X, Y, Z after transformation="<< std::endl;
thrust::copy_n(X.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::copy_n(Y.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::copy_n(Z.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
return 0;
}
$ nvcc -std=c++11 -I/path/to/eigen/eigen-eigen-71546f1a9f0c t21.cu -o t21 --expt-relaxed-constexpr
$ ./t21
X,Y,Z before transformation=
0,1,2,
4,5,6,
7,8,9,
X, Y, Z after transformation=
33,39,45,
81,99,117,
129,159,189,
$
The output doesn't match what you are expecting in your question, but what you are expecting is not correct either.
The first tuple provided to your functor as a result of dereferencing your zip iterator will be (X[0],Y[0],Z[0]), which is (0,4,7). Your functor then converts that to (0,4,7,1) and does a matrix-vector multiplication with your M matrix. The first row inner product is given by 0*1+4*2+7*3+1*4, which sum is 33. The second row inner product is given by 0*5+4*6+7*7+1*8, which sum is 81. The third row inner product is given by 0*9+4*10+7*11+1*12, which sum is 129. You can see this sequence 33,81,129 is exactly the first column of the output above.
The second tuple provided to your functor as a result of dereferencing your zip iterator will be (X[1],Y[1],Z[1]), which is (1,5,8). Your functor then converts that to (1,5,8,1) and does a matrix-vector multiplication with your M matrix. The first row inner product is given by 1*1+5*2+8*3+1*4 which sum is 39. The second row innner product is given by 1*5+5*6+8*7+1*8 which sum is 99. The third row inner product is given by 1*9+5*10+8*11+1*12 which sum is 159. You can see this sequence 39,99,159 is exactly the second column of the output above.
I haven't done the corresponding arithmetic for the 3rd column of the output, but I don't think it is wrong.
Here's a modification of your code, demonstrating the correctness of the results, doing the arithmetic in Eigen host code:
$ cat t21.cu
#include <thrust/iterator/zip_iterator.h>
#include <thrust/execution_policy.h>
#include <thrust/copy.h>
#include <thrust/device_vector.h>
#include <Eigen/Dense>
#include <iostream>
typedef thrust::device_vector<float>::iterator FloatIterator;
typedef thrust::tuple<FloatIterator, FloatIterator, FloatIterator> FloatIteratorTuple;
typedef thrust::zip_iterator<FloatIteratorTuple> Float3Iterator;
typedef thrust::tuple<float,float,float> Float3;
struct modify_tuple
{
Eigen::Matrix4f _Mat4f;
modify_tuple(Eigen::Matrix4f Mat4f) : _Mat4f(Mat4f) { }
__host__ __device__ Float3 operator()(Float3 a) const
{
Eigen::Vector4f V(thrust::get<0>(a), thrust::get<1>(a), thrust::get<2>(a), 1.0);
V=_Mat4f*V;
Float3 res=thrust::make_tuple( V(0,0), V(1,0), V(2,0) );
return res;
}
};
int main(void)
{
thrust::device_vector<float> X(3);
thrust::device_vector<float> Y(3);
thrust::device_vector<float> Z(3);
X[0]=0, X[1]=1, X[2]=2;
Y[0]=4, Y[1]=5, Y[2]=6;
Z[0]=7, Z[1]=8, Z[2]=9;
std::cout << "X,Y,Z before transformation="<< std::endl;
thrust::copy_n(X.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::copy_n(Y.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::copy_n(Z.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::host_vector<float> hX = X;
thrust::host_vector<float> hY = Y;
thrust::host_vector<float> hZ = Z;
Float3Iterator P_first = thrust::make_zip_iterator(make_tuple(X.begin(), Y.begin(), Z.begin()));
Float3Iterator P_last = thrust::make_zip_iterator(make_tuple(X.end(), Y.end(), Z.end()));
Eigen::Matrix4f M;
M(0,0)= 1; M(0,1)= 2; M(0,2)= 3; M(0,3)= 4;
M(1,0)= 5; M(1,1)= 6; M(1,2)= 7; M(1,3)= 8;
M(2,0)= 9; M(2,1)= 10; M(2,2)= 11; M(2,3)= 12;
M(3,0)= 13; M(3,1)= 14; M(3,2)= 15; M(3,3)= 16;
thrust::transform(thrust::device, P_first,P_last, P_first, modify_tuple(M));
std::cout << "X, Y, Z after transformation="<< std::endl;
thrust::copy_n(X.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::copy_n(Y.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
thrust::copy_n(Z.begin(), 3, std::ostream_iterator<float>(std::cout, ","));
std::cout << std::endl;
Eigen::Vector4f hV;
hV(0) = hX[0];
hV(1) = hY[0];
hV(2) = hZ[0];
hV(3) = 1;
hV = M*hV;
std::cout << "column 0:" << std::endl;
std::cout << hV;
std::cout << std::endl;
hV(0) = hX[1];
hV(1) = hY[1];
hV(2) = hZ[1];
hV(3) = 1;
hV = M*hV;
std::cout << "column 1:" << std::endl;
std::cout << hV;
std::cout << std::endl;
hV(0) = hX[2];
hV(1) = hY[2];
hV(2) = hZ[2];
hV(3) = 1;
hV = M*hV;
std::cout << "column 2:" << std::endl;
std::cout << hV;
std::cout << std::endl;
return 0;
}
$ nvcc -std=c++11 -I/home/bob/eigen/eigen-eigen-71546f1a9f0c t21.cu -o t21 --expt-relaxed-constexpr
$ ./t21
X,Y,Z before transformation=
0,1,2,
4,5,6,
7,8,9,
X, Y, Z after transformation=
33,39,45,
81,99,117,
129,159,189,
column 0:
33
81
129
177
column 1:
39
99
159
219
column 2:
45
117
189
261
$

namespace::function cannot be used as a function

main.cpp
#include "Primes.h"
#include <iostream>
int main(){
std::string choose;
int num1, num2;
while(1 == 1){
std::cout << "INSTRUCTIONS" << std::endl << "Enter:" << std::endl
<< "'c' to check whether a number is a prime," << std::endl
<< "'u' to view all the prime numbers between two numbers "
<< "that you want," << std::endl << "'x' to exit,"
<< std::endl << "Enter what you would like to do: ";
std::cin >> choose;
std::cout << std::endl;
if(choose == "c"){
std::cout << "Enter number: ";
std::cin >> num1;
Primes::checkPrimeness(num1) == 1 ?
std::cout << num1 << " is a prime." << std::endl << std::endl :
std::cout << num1 << " isn't a prime." << std::endl << std::endl;
}else if(choose == "u"){
std::cout << "Enter the number you want to start seeing primes "
<< "from: ";
std::cin >> num1;
std::cout << "\nEnter the number you want to stop seeing primes "
<< "till: ";
std::cin >> num2;
std::cout << std::endl;
for(num1; num1 <= num2; num1++){
Primes::checkPrimeness(num1) == 1 ?
std::cout << num1 << " is a prime." << std::endl :
std::cout << num1 << " isn't a prime." << std::endl;
}
}else if(choose == "x"){
return 0;
}
std::cout << std::endl;
}
}
Primes.h
#ifndef PRIMES_H
#define PRIMES_H
namespace Primes{
extern int num, count;
extern bool testPrime;
// Returns true if the number is a prime and false if it isn't.
int checkPrimeness(num);
}
#endif
Primes.cpp
#include "Primes.h"
#include <iostream>
int Primes::checkPrimeness(num){
if(num < 2){
return(0);
}else if(num == 2){
return(1);
}else{
for(count = 0; count < num; count++){
for(count = 2; count < num; count++){
if(num % count == 0){
return(0);
}else{
testPrime = true;
if(count == --num && testPrime == true){
return(1);
}
}
}
}
}
}
I get the following 3 errors: Errors from terminal
I've spent hours for days and still can't seem to fix the errors.
I've tried using extern and pretty much everything I can imagine.

Here is an error in function declaration:
int checkPrimeness(num);
defines a global integer variable checkPrimeness initialized with num! To declare a function you just should change it like:
int checkPrimeness(int);
Can't understand why you declare parameters as external variables. To split declarations and realization you should declare all functions and classes inside header file, and define them inside source file.

Error: 2.5e-1 cannot be used as a function

i wrote a simple program and i'm getting this error which i never encountered yet. Can you help me out?
line 13: error: 2.5e-1 cannot be used as a function
#include <iostream>
#include <iomanip>
using namespace std;
int dirac(int);
int main()
{
float y;
for(int k = 0; k <= 4; k++){
y = 2*dirac(k)-0.5*dirac(k-1)*0.25(2*dirac(k-2)-0.5*dirac(k-3));
cout << "k = " << k << ": ";
cout << setw(8) << setfill(' ');
cout << setprecision(3) << fixed << y << endl;
}
return 0;
}
int dirac(int x){
if(x == 0){
x = 1;
return x;
}else{
x = 0;
return x;
}
}

y = 2*dirac(k)-0.5*dirac(k-1)*0.25(2*dirac(k-2)-0.5*dirac(k-3));
^---
You probably forgot a * at the indicated spot.