I'm trying to pass some POD to a kernel which has as parameters some non-POD, and has non explicit constructors. Idea behind that is: allocate some memory on the host, pass the memory to the kernel, and it encapsulate the memory in the objects without the user to explicitly do that step.
The constructors are marked as __device__ code, but they are not called when passing the parameters, and I can't figure out why.
My question is not really related about how should I do the thing, but trying to understand what's happening behind the scenes.
Here an example (I'm using CUDA 5 with a GPU of capability 2.1, hence the printf).
#include <stdio.h>
struct Test {
__device__ Test() {
printf("Default\n"),
_n = 0;
}
__device__ Test(int n) {
printf("Construct %d\n", n);
_n = n;
}
__device__ Test(const Test &t) {
printf("Copy constr %d\n", t._n);
_n = t._n;
}
__device__ Test &operator=(const Test &t) {
printf("Assignment %d\n", t._n);
_n = t._n;
return *this;
}
__device__ int calc() const {
printf("Calculating %d\n", threadIdx.x + 10 * _n);
return threadIdx.x + 10 * _n;
}
int _n;
};
__global__ void dosome(Test a, Test b) {
printf("Kernel data %d %d\n", a._n, b._n);
a.calc();
b.calc();
}
int main(int argc, char **argv) {
dosome<<<1, 2>>>(2, 3);
cudaError_t cudaerr = cudaDeviceSynchronize();
if (cudaerr != cudaSuccess)
printf("kernel launch failed with error:\n\t%s\n",cudaGetErrorString(cudaerr));
return 0;
}
EDIT: Forgot to say that, none of the constructor message is printed, but the calc and kernel message are.
EDIT2: Is it guaranteed that CUDA will initialize a Test object before copying it on the device?
You have to see a constructor just like a normal method. If you qualify it with __host__, then you'll be able to call it host-side. If you qualify it with __device__, you'll be able to call it device-side. If you qualify it with both, you'll be able to call it on both sides.
What happens when you do dosome<<<1, 2>>>(2, 3); is that the two objects are implictly constructed (because your constructor is not explicit, so maybe that's confusing you too) host side and then memcpy'd to the device. There is no copy-constructor involved in the process.
Let's illustrate this:
__global__ void dosome(Test a, Test b) {
a.calc();
b.calc();
}
int main(int argc, char **argv) {
dosome<<<1, 2>>>(2, 3); // Constructors must be at least __host__
return 0;
}
// Outputs:
Construct 2 (from the host side)
Construct 3 (from the host side)
Now if you change your kernel to take ints instead of Test:
__global__ void dosome(int arga, int argb) {
// Constructors must be at least __device__
Test a(arga);
Test b(argb);
a.calc();
b.calc();
}
int main(int argc, char **argv) {
dosome<<<1, 2>>>(2, 3);
return 0;
}
// Outputs:
Construct 2 (from the device side)
Construct 3 (from the device side)
Ok, I found it works (constructors are called) if I add both __host__ and __device__ qualifiers to the constructors. The constructor of the objects happened at host side, and then they were copied to device (stack?). This is why the constructors weren't called: they were device code (but what was called on the host side?!?)
Using both __host__ and __device__ in the constructors allowed to use the class without problems.
EDIT: Still, I'm not sure if the construction always happens before the copy to device.
Related
This is my first question on Stack Overflow, and it's quite a long question. The tl;dr version is: How do I work with a thrust::device_vector<BaseClass> if I want it to store objects of different types DerivedClass1, DerivedClass2, etc, simultaneously?
I want to take advantage of polymorphism with CUDA Thrust. I'm compiling for an -arch=sm_30 GPU (GeForce GTX 670).
Let us take a look at the following problem: Suppose there are 80 families in town. 60 of them are married couples, 20 of them are single-parent households. Each family has, therefore, a different number of members. It's census time and households have to state the parents' ages and the number of children they have. Therefore, an array of Family objects is constructed by the government, namely thrust::device_vector<Family> familiesInTown(80), such that information of families familiesInTown[0] to familiesInTown[59] corresponds to married couples, the rest (familiesInTown[60] to familiesInTown[79]) being single-parent households.
Family is the base class - the number of parents in the household (1 for single parents and 2 for couples) and the number of children they have are stored here as members.
SingleParent, derived from Family, includes a new member - the single parent's age, unsigned int ageOfParent.
MarriedCouple, also derived from Family, however, introduces two new members - both parents' ages, unsigned int ageOfParent1 and unsigned int ageOfParent2.
#include <iostream>
#include <stdio.h>
#include <thrust/device_vector.h>
class Family
{
protected:
unsigned int numParents;
unsigned int numChildren;
public:
__host__ __device__ Family() {};
__host__ __device__ Family(const unsigned int& nPars, const unsigned int& nChil) : numParents(nPars), numChildren(nChil) {};
__host__ __device__ virtual ~Family() {};
__host__ __device__ unsigned int showNumOfParents() {return numParents;}
__host__ __device__ unsigned int showNumOfChildren() {return numChildren;}
};
class SingleParent : public Family
{
protected:
unsigned int ageOfParent;
public:
__host__ __device__ SingleParent() {};
__host__ __device__ SingleParent(const unsigned int& nChil, const unsigned int& age) : Family(1, nChil), ageOfParent(age) {};
__host__ __device__ unsigned int showAgeOfParent() {return ageOfParent;}
};
class MarriedCouple : public Family
{
protected:
unsigned int ageOfParent1;
unsigned int ageOfParent2;
public:
__host__ __device__ MarriedCouple() {};
__host__ __device__ MarriedCouple(const unsigned int& nChil, const unsigned int& age1, const unsigned int& age2) : Family(2, nChil), ageOfParent1(age1), ageOfParent2(age2) {};
__host__ __device__ unsigned int showAgeOfParent1() {return ageOfParent1;}
__host__ __device__ unsigned int showAgeOfParent2() {return ageOfParent2;}
};
If I were to naïvely initiate the objects in my thrust::device_vector<Family> with the following functors:
struct initSlicedCouples : public thrust::unary_function<unsigned int, MarriedCouple>
{
__device__ MarriedCouple operator()(const unsigned int& idx) const
// I use a thrust::counting_iterator to get idx
{
return MarriedCouple(idx % 3, 20 + idx, 19 + idx);
// Couple 0: Ages 20 and 19, no children
// Couple 1: Ages 21 and 20, 1 child
// Couple 2: Ages 22 and 21, 2 children
// Couple 3: Ages 23 and 22, no children
// etc
}
};
struct initSlicedSingles : public thrust::unary_function<unsigned int, SingleParent>
{
__device__ SingleParent operator()(const unsigned int& idx) const
{
return SingleParent(idx % 3, 25 + idx);
}
};
int main()
{
unsigned int Num_couples = 60;
unsigned int Num_single_parents = 20;
thrust::device_vector<Family> familiesInTown(Num_couples + Num_single_parents);
// Families [0] to [59] are couples. Families [60] to [79] are single-parent households.
thrust::transform(thrust::counting_iterator<unsigned int>(0),
thrust::counting_iterator<unsigned int>(Num_couples),
familiesInTown.begin(),
initSlicedCouples());
thrust::transform(thrust::counting_iterator<unsigned int>(Num_couples),
thrust::counting_iterator<unsigned int>(Num_couples + Num_single_parents),
familiesInTown.begin() + Num_couples,
initSlicedSingles());
return 0;
}
I would definitely be guilty of some classic object slicing...
So, I asked myself, what about a vector of pointers that may give me some sweet polymorphism? Smart pointers in C++ are a thing, and thrust iterators can do some really impressive things, so let's give it a shot, I figured. The following code compiles.
struct initCouples : public thrust::unary_function<unsigned int, MarriedCouple*>
{
__device__ MarriedCouple* operator()(const unsigned int& idx) const
{
return new MarriedCouple(idx % 3, 20 + idx, 19 + idx); // Memory issues?
}
};
struct initSingles : public thrust::unary_function<unsigned int, SingleParent*>
{
__device__ SingleParent* operator()(const unsigned int& idx) const
{
return new SingleParent(idx % 3, 25 + idx);
}
};
int main()
{
unsigned int Num_couples = 60;
unsigned int Num_single_parents = 20;
thrust::device_vector<Family*> familiesInTown(Num_couples + Num_single_parents);
// Families [0] to [59] are couples. Families [60] to [79] are single-parent households.
thrust::transform(thrust::counting_iterator<unsigned int>(0),
thrust::counting_iterator<unsigned int>(Num_couples),
familiesInTown.begin(),
initCouples());
thrust::transform(thrust::counting_iterator<unsigned int>(Num_couples),
thrust::counting_iterator<unsigned int>(Num_couples + Num_single_parents),
familiesInTown.begin() + Num_couples,
initSingles());
Family A = *(familiesInTown[2]); // Compiles, but object slicing takes place (in theory)
std::cout << A.showNumOfParents() << "\n"; // Segmentation fault
return 0;
}
Seems like I've hit a wall here. Am I understanding memory management correctly? (VTables, etc). Are my objects being instantiated and populated on the device? Am I leaking memory like there is no tomorrow?
For what it's worth, in order to avoid object slicing, I tried with a dynamic_cast<DerivedPointer*>(basePointer). That's why I made my Family destructor virtual.
Family *pA = familiesInTown[2];
MarriedCouple *pB = dynamic_cast<MarriedCouple*>(pA);
The following lines compile, but, unfortunately, a segfault is thrown again. CUDA-Memcheck won't tell me why.
std::cout << "Ages " << (pB -> showAgeOfParent1()) << ", " << (pB -> showAgeOfParent2()) << "\n";
and
MarriedCouple B = *pB;
std::cout << "Ages " << B.showAgeOfParent1() << ", " << B.showAgeOfParent2() << "\n";
In short, what I need is a class interface for objects that will have different properties, with different numbers of members among each other, but that I can store in one common vector (that's why I want a base class) that I can manipulate on the GPU. My intention is to work with them both in thrust transformations and in CUDA kernels via thrust::raw_pointer_casting, which has worked flawlessly for me until I've needed to branch out my classes into a base one and several derived ones. What is the standard procedure for that?
Thanks in advance!
I am not going to attempt to answer everything in this question, it is just too large. Having said that here are some observations about the code you posted which might help:
The GPU side new operator allocates memory from a private runtime heap. As of CUDA 6, that memory cannot be accessed by the host side CUDA APIs. You can access the memory from within kernels and device functions, but that memory cannot be accessed by the host. So using new inside a thrust device functor is a broken design that can never work. That is why your "vector of pointers" model fails.
Thrust is fundamentally intended to allow data parallel versions of typical STL algorithms to be applied to POD types. Building a codebase using complex polymorphic objects and trying to cram those through Thrust containers and algorithms might be made to work, but it isn't what Thrust was designed for, and I wouldn't recommend it. Don't be surprised if you break thrust in unexpected ways if you do.
CUDA supports a lot of C++ features, but the compilation and object models are much simpler than even the C++98 standard upon which they are based. CUDA lacks several key features (RTTI for example) which make complex polymorphic object designs workable in C++. My suggestion is use C++ features sparingly. Just because you can do something in CUDA doesn't mean you should. The GPU is a simple architecture and simple data structures and code are almost always more performant than functionally similar complex objects.
Having skim read the code you posted, my overall recommendation is to go back to the drawing board. If you want to look at some very elegant CUDA/C++ designs, spend some time reading the code bases of CUB and CUSP. They are both very different, but there is a lot to learn from both (and CUSP is built on top of Thrust, which makes it even more relevant to your usage case, I suspect).
I completely agree with #talonmies answer. (e.g. I don't know that thrust has been extensively tested with polymorphism.) Furthermore, I have not fully parsed your code. I post this answer to add additional info, in particular that I believe some level of polymorphism can be made to work with thrust.
A key observation I would make is that it is not allowed to pass as an argument to a __global__ function an object of a class with virtual functions. This means that polymorphic objects created on the host cannot be passed to the device (via thrust, or in ordinary CUDA C++). (One basis for this limitation is the requirement for virtual function tables in the objects, which will necessarily be different between host and device, coupled with the fact that it is illegal to directly take the address of a device function in host code).
However, polymorphism can work in device code, including thrust device functions.
The following example demonstrates this idea, restricting ourselves to objects created on the device although we can certainly initialize them with host data. I have created two classes, Triangle and Rectangle, derived from a base class Polygon which includes a virtual function area. Triangle and Rectangle inherit the function set_values from the base class but replace the virtual area function.
We can then manipulate objects of those classes polymorphically as demonstrated here:
#include <iostream>
#include <thrust/device_vector.h>
#include <thrust/for_each.h>
#include <thrust/sequence.h>
#include <thrust/iterator/zip_iterator.h>
#include <thrust/copy.h>
#define N 4
class Polygon {
protected:
int width, height;
public:
__host__ __device__ void set_values (int a, int b)
{ width=a; height=b; }
__host__ __device__ virtual int area ()
{ return 0; }
};
class Rectangle: public Polygon {
public:
__host__ __device__ int area ()
{ return width * height; }
};
class Triangle: public Polygon {
public:
__host__ __device__ int area ()
{ return (width * height / 2); }
};
struct init_f {
template <typename Tuple>
__host__ __device__ void operator()(const Tuple &arg) {
(thrust::get<0>(arg)).set_values(thrust::get<1>(arg), thrust::get<2>(arg));}
};
struct setup_f {
template <typename Tuple>
__host__ __device__ void operator()(const Tuple &arg) {
if (thrust::get<0>(arg) == 0)
thrust::get<1>(arg) = &(thrust::get<2>(arg));
else
thrust::get<1>(arg) = &(thrust::get<3>(arg));}
};
struct area_f {
template <typename Tuple>
__host__ __device__ void operator()(const Tuple &arg) {
thrust::get<1>(arg) = (thrust::get<0>(arg))->area();}
};
int main () {
thrust::device_vector<int> widths(N);
thrust::device_vector<int> heights(N);
thrust::sequence( widths.begin(), widths.end(), 2);
thrust::sequence(heights.begin(), heights.end(), 3);
thrust::device_vector<Rectangle> rects(N);
thrust::device_vector<Triangle> trgls(N);
thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(rects.begin(), widths.begin(), heights.begin())), thrust::make_zip_iterator(thrust::make_tuple(rects.end(), widths.end(), heights.end())), init_f());
thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(trgls.begin(), widths.begin(), heights.begin())), thrust::make_zip_iterator(thrust::make_tuple(trgls.end(), widths.end(), heights.end())), init_f());
thrust::device_vector<Polygon *> polys(N);
thrust::device_vector<int> selector(N);
for (int i = 0; i<N; i++) selector[i] = i%2;
thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(selector.begin(), polys.begin(), rects.begin(), trgls.begin())), thrust::make_zip_iterator(thrust::make_tuple(selector.end(), polys.end(), rects.end(), trgls.end())), setup_f());
thrust::device_vector<int> areas(N);
thrust::for_each(thrust::make_zip_iterator(thrust::make_tuple(polys.begin(), areas.begin())), thrust::make_zip_iterator(thrust::make_tuple(polys.end(), areas.end())), area_f());
thrust::copy(areas.begin(), areas.end(), std::ostream_iterator<int>(std::cout, "\n"));
return 0;
}
I suggest compiling the above code for a cc2.0 or newer architecture. I tested with CUDA 6 on RHEL 5.5.
(The polymorphic example idea, and some of the code, was taken from here.)
I thought I knew how to write some clean cuda code. Until I tried to make a simple template class and use it in a simple kernel.
I've been trouble shooting for days. Every single thread I've visited made me feel a little more stupid.
For error checking I used this
Here is my class.h:
#pragma once
template <typename T>
class MyArray
{
public:
const int size;
T *data;
__host__ MyArray(int size); //gpuErrchk(cudaMalloc(&data, size * sizeof(T)));
__device__ __host__ T GetValue(int); //return data[i]
__device__ __host__ void SetValue(T, int); //data[i] = val;
__device__ __host__ T& operator()(int); //return data[i];
~MyArray(); //gpuErrchk(cudaFree(data));
};
template class MyArray<double>;
The relevant content of class.cu is in the comments. If you think the whole thing is relevant Id be happy to add it.
Now for the main class:
__global__ void test(MyArray<double> array, double *data, int size)
{
int j = threadIdx.x;
//array.SetValue(1, j); //doesn't work
//array(j) = 1; //doesn't work
//array.data[j] = 1; //doesn't work
data[j] = 1; //This does work !
printf("Reach this code\n");
}
}
int main(int argc, char **argv)
{
MyArray x(20);
test<<<1, 20>>>(x, x.data, 20);
gpuErrchk(cudaPeekAtLastError());
gpuErrchk(cudaDeviceSynchronize());
}
When I say "doesn't work", I mean that the program stops there (before reaching the printf) without outputting any error. Plus I get the following error both from cudaDeviceSynchronize and from cudaFree:
an illegal memory access was encountered
What I can't understand is that there should be no issue with memory management since sending the array directly to the kernel works fine. So why doesn't it work when I send a class and try to access the classes data? And why do I receive no warning or error message when clearly my code bumped into some error?
Here is the output of nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Nov__3_21:07:56_CDT_2017
Cuda compilation tools, release 9.1, V9.1.85
(Editorial note: there is quite a bit of disinformation in the comments on this question, so I have assembled an answer as a community wiki entry.)
There is no particular reason why a template class cannot be passed as an argument to a kernel. There are some limitations which need to be clearly understood before doing so:
CUDA kernel arguments, for all intent and purpose, always passed by value. Pass by reference is supported under extremely limited set of circumstances (the argument in question must be stored in managed memory). This does not apply here.
As a result of (1), POD arguments just work, because they are trivially copyable and rely on no special behaviour
Classes are different, in that when you pass a class by value, you are implicitly invoking copy construction or move construction semantics. That means that classes passed by value as kernel arguments must be trivially copy constructable. There is no way to run non trivial copy constructors on the device as part of a kernel launch.
CUDA further requires that classes don't contain virtual members
Although the <<< >>> kernel launch syntax looks like a simple function call, it isn't. There is several layers of abstraction boilerplate and a API call between what you write in host code and what actually is emitted by the toolchain on the host side. This means that there are several copy construction operations between your code and the GPU. If you do something like put a cudaFree call in your destructor, you should assume that it will get called as part of the function call sequence which launches a kernel when one of those copies falls out of scope. You do not want that.
You did not show how the class member functions were actually implemented in this case, so saying why one of the many permutations your code comments hinted at did or did not work is impossible, beyond passing the raw pointer to the kernel, which works because it is a trivially copyable POD value, when the class was almost certainly not.
Here is a simple, complete example showing how to make this work:
$cat classy.cu
#include <vector>
#include <iostream>
#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);
}
}
template <typename T>
class MyArray
{
public:
int len;
T *data;
__device__ __host__ void SetValue(T val, int i) { data[i] = val; };
__device__ __host__ int size() { return sizeof(T) * len; };
__host__ void DevAlloc(int N) {
len = N;
gpuErrchk(cudaMalloc(&data, size()));
};
__host__ void DevFree() {
gpuErrchk(cudaFree(data));
len = -1;
};
};
__global__ void test(MyArray<double> array, double val)
{
int j = threadIdx.x;
if (j < array.len)
array.SetValue(val, j);
}
int main(int argc, char **argv)
{
const int N = 20;
const double val = 5432.1;
gpuErrchk(cudaSetDevice(0));
gpuErrchk(cudaFree(0));
MyArray<double> x;
x.DevAlloc(N);
test<<<1, 32>>>(x, val);
gpuErrchk(cudaPeekAtLastError());
gpuErrchk(cudaDeviceSynchronize());
std::vector<double> y(N);
gpuErrchk(cudaMemcpy(&y[0], x.data, x.size(), cudaMemcpyDeviceToHost));
x.DevFree();
for(int i=0; i<N; ++i) std::cout << i << " = " << y[i] << std::endl;
return 0;
}
which compiles and runs like so:
$ nvcc -std=c++11 -arch=sm_53 -o classy classy.cu
$ cuda-memcheck ./classy
========= CUDA-MEMCHECK
0 = 5432.1
1 = 5432.1
2 = 5432.1
3 = 5432.1
4 = 5432.1
5 = 5432.1
6 = 5432.1
7 = 5432.1
8 = 5432.1
9 = 5432.1
10 = 5432.1
11 = 5432.1
12 = 5432.1
13 = 5432.1
14 = 5432.1
15 = 5432.1
16 = 5432.1
17 = 5432.1
18 = 5432.1
19 = 5432.1
========= ERROR SUMMARY: 0 errors
(CUDA 10.2/gcc 7.5 on a Jetson Nano)
Note that I have included host side functions for allocation and deallocation which do not interact with the constructor and destructor. Otherwise the class is extremely similar to your design and has the same properties.
I have the following code:
int main(int argc, char **argv)
{
App app(800, 600);
app.add_event_scene(Scene("Event Plot", event_plot));
Image x("sample.png");
struct foo { static void visual_plot() { x.draw(); } }; // Error.
app.add_visual_scene(Scene("Visual Plot", foo::visual_plot));
app.run();
return 0;
}
And I get the following error:
||=== Build: Debug in Joy-Plus-Plus (compiler: GNU GCC Compiler) ===|
G:\Development\Game-Development\CB\Joy-Plus-Plus\main.cpp|54|error: use of local variable with automatic storage from containing function|
G:\Development\Game-Development\CB\Joy-Plus-Plus\main.cpp|53|error: 'Image x' declared here|
||=== Build failed: 2 error(s), 0 warning(s) (0 minute(s), 0 second(s)) ===|
I'm writing a multimedia/game engine for the Allegro 5 library, and I've abstracted the drawing part of the main-loop (As well as the event parts) into "scene" objects with plots (Functions). Each procedure is passed to the App, so that it gets "run" inside the main-loop. The problem is, the "C++ approach" does not work:
Image x("sample.png");
void visual_plot()
{
x.draw(); // Problem.
}
int main(int argc, char **argv)
{
App app(800, 600);
app.add_event_scene(Scene("Event Plot", event_plot));
app.add_visual_scene(Scene("Visual Plot", visual_plot));
app.run();
return 0;
}
Although the code runs, this happens:
And if I put the x inside the visual_plot, the image is loaded normally:
But now I have a huge performance problem, since a new Image object is being created at each main-loop (And it's not long until the whole thing freezes).
The image is not found when I put it outside the scope of the function because it must come after the initialization of the App, but since I have a typedef function pointer in Scene that takes that function as an argument, I also must give it a void function. The problem is that I can't create local / nested functions in C++ (After the initialization of the App). So, in order to avoid the problem, I've tried the obvious (Lambda expression / closure):
int main(int argc, char **argv)
{
App app(800, 600);
app.add_event_scene(Scene("Event Plot", event_plot));
Image x("sample.png");
app.add_visual_scene(Scene("Visual Plot", [&x]()->void{x.draw();}));
app.run();
return 0;
}
The problem is that the second argument of the constructor of Scene takes a function pointer:
typedef void(*plot)();
typedef map<string, plot> act;
class Scene
{
private:
string name;
plot p;
public:
Scene(string name, plot p);
~Scene() {};
string get_name();
plot get_plot();
void set_name(string value);
void set_plot(plot value);
};
And since functions cannot be passed as parameters, and get decayed to pointers, the same also applies to the lambda expression (Which is not a function), so I get the following error:
G:\Development\Game-Development\CB\Joy-Plus-Plus\main.cpp|52|error: no matching function for call to 'Scene::Scene(const char [12], main(int, char**)::__lambda0)'|
Facing such a tragedy, how can I simulate a nested function in C++11? Since simulating like this answer does not work.
OBS: I agree that it could be a design problem, but I pretty much don't see it that way. For me, C++ just don't want me to pass that bloody function as a parameter by any means (So, I ask for the help of you long C++ Wizards).
Simply put the image inside the visual_plot function and make it static:
void visual_plot()
{
static Image img("sample.png");
x.draw(); // Problem.
}
This will initialize img the first time visual_plot is called, and only then. This will solve both the performance problem and the "it must be initialized after app.run()" issue.
It is a design problem. In order to accomplish what you are trying to do you need two pieces of information: the code to execute and the data to execute it against.
A lambda isn't magic, it simply encapsulates both of these into an object, that's why it doesn't decay nicely to a single function pointer. A lambda with captures is syntactic sugar for a function object:
int x, y;
float f;
// ...
auto l = [x, &y, f] () { return static_cast<int>((x + y) * f); };
int r = l();
is saving you from writing
struct Values {
int x;
int& y;
float f;
int operator() () {
return static_cast<int>((x + y) * f);
}
Capture(int x_, int& y_, float f_) : x(x_), y(y_), f(f_) {}
};
//...
Capture c(x, y, f);
int r = c();
That's a member function call at the end there, so two pointers are involved: a pointer to the member function 'operator()' and a pointer to the instance to call it on.
int r = Capture::operator=(&c); // pseudo
Using a static or global variable you could make the address of the data known at compile time and so allow yourself to only need a function pointer.
But your design is that of a strcpy that only takes one argument or a print function that takes none: how do you know what to copy or print?
Better designs would be either to let you pass a function object to the plot functions, see how STL predicates work, which would allow both function pointers and lambdas, or use virtual functions and subclassing.
struct Scene { virtual void plot(); };
struct MyScene : public Scene {
Image x;
MyScene() : x("image") {}
void plot() override { x.draw(); }
};
The pitfall of this approach is "slicing", you need to pass Scene by reference rather than by value if you are allowing derived types:
void foo(Scene& s) {
s.plot();
}
foo(MyScene(...)); // not going to go well
I have 3 classes.
class piesa_a{
protected:
int id;
char *tip;
int pret;
public:
[custructor with/without param, display function - works well each one of it]
class piesa_b:public piesa_a
{
private:
float lungime;
bool bw;
public:
[custructor with/without param, display function - works well each one of it]
class piesa_c:public piesa_a
{
private:
int nr;
piesa_b *buf;
public:
piesa_c():piesa_a(){nr=0; buf = new piesa_b[nr];}
piesa_c(int n, piesa_b *bu,int aid, char *tipi, int pretzz):piesa_a(aid,tipi,pretzz)
{
buf = new piesa_b[nr];
for(int i=0;i<nr;i++)
buf[i]= bu[i];
}
void afisare()
{
cout<<nr;
}
In main i have this:
piesa_c C(2, H,14,"TIPC",20);
C.afisare();
But this doesn't work.
I don't know if the "buf" was declared properly because the problem seems to be in last class.
Why?
Later Edit:
The entire code is here: http://pastebin.com/nx2FGSfe.
Now, i have this in main
int main(int argc, char** argv) {
piesa_b *H;
H = new piesa_b[2];
piesa_a A(4,"TIPA",120);
piesa_b B(100,1,3,"TIPA",120);
H[0]=B;
H[1]=B;
piesa_c C(2, H,14,"TIPC",20);
piesa_a** v = new piesa_a*[3];
v[0] = &A;
v[1] = &B;
v[2] = &C;
for(int i=0;i<3;i++)
v[i].afisare();
return 0;
}
The display function return this error
main.cpp:143:14: error: request for member ‘afisare’ in ‘*(v + ((unsigned int)(((unsigned int)i) * 4u)))’, which is of non-class type ‘piesa_a*’
nr is not initialized in the piesa_c() constructor, meaning it will have an undefined value.
Instead of using a dynamically allocated array used a std::vector<piesa_b> instead. It will handle dynamic memory allocation and do the right thing when instances of piesa_c is copied. Using std::vector also means the nr member variable can omitted as that information can be obtained from vector::size() and the std::vector can be populated in the initializer list instead of in the constructor body:
std::vector<piesa_b> buf;
piesa_c(int n,
piesa_b *bu,
int aid,
char* tipi,
int pretzz) : piesa_a(aid,tipi,pretzz),
buf(bu, bu + nr) {}
And to invoke a member function on each element in buf:
// C++11 lambda, otherwise use
// std::vector<piesa_b>::const_iterator.
//
std::for_each(buf.begin(), buf.end(), [](piesa_b& pb) { pb.afisare(); });
If afisare() does not modify then make it const:
void afisare() const
{
}
Additonally, use std::string instead of char*. If you insist on having dynamically allocated members in the classes you need to obey the rule of three.
I am not sure what "not work" means in this context, but when you call this constructor:
piesa_c C(2, H,14,"TIPC",20);
the data member nr is not set. It can have any value that fits into an int, so when you use it to initialize an array you will get variable and weird results.
Note that you could save yourself a lot of trouble by using std::vector and std::string instead of dynamically allocated arrays and char*.
I have a re-useable function in some CUDA code that needs to be called from both the device and the host. Is there an appropriate qualifier for this?
e.g. what's the correct definition for func1 in this case:
int func1 (int a, int b) {
return a+b;
}
__global__ devicecode (float *A) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
A[i] = func1(i,i);
}
void main() {
// Normal cuda memory set-up
// Call func1 from inside main:
int j = func1(2,4)
// Normal cuda memory copy / program run / retrieve data
}
So far I can only get this to work by having the function twice: once explicitly for the device and once for the host. Is there a better way?
From the CUDA Programming Guide:
The __device__ and __host__ qualifiers can be used together however, in
which case the function is compiled for both the host and the device.