Writing a simple thrust functor operating on some zipped arrays - cuda

I am trying to perform a thrust::reduce_by_key using zip and permutation iterators.
i.e. doing this on a zipped array of several 'virtual' permuted arrays.
I am having trouble in writing the syntax for the functor density_update.
But first the setup of the problem.
Here is my function call:
thrust::reduce_by_key( dflagt,
dflagtend,
thrust::make_zip_iterator(
thrust::make_tuple(
thrust::make_permutation_iterator(dmasst, dmapt),
thrust::make_permutation_iterator(dvelt, dmapt),
thrust::make_permutation_iterator(dmasst, dflagt),
thrust::make_permutation_iterator(dvelt, dflagt)
)
),
thrust::make_discard_iterator(),
danswert,
thrust::equal_to<int>(),
density_update()
)
dmapt, dflagt are of type thrust::device_ptr<int> and dvelt , dmasst and danst are of type
thrust::device_ptr<double>.
(They are thrust wrappers to my raw cuda arrays)
The arrays mapt and flagt are both index vectors from which I need to perform a gather operation from the arrays dmasst and dvelt.
After the reduction step I intend to write my data to the danswert array. Since multiple arrays are being used in the reduction, obviously I am using zip iterators.
My problem lies in writing the functor density_update which is binary operation.
struct density_update
{
typedef thrust::device_ptr<double> ElementIterator;
typedef thrust::device_ptr<int> IndexIterator;
typedef thrust::permutation_iterator<ElementIterator,IndexIterator> PIt;
typedef thrust::tuple< PIt , PIt , PIt, PIt> Tuple;
__host__ __device__
double operator()(const Tuple& x , const Tuple& y)
{
return thrust::get<0>(*x) * (thrust::get<1>(*x) - thrust::get<3>(*x)) + \
thrust::get<0>(*y) * (thrust::get<1>(*y) - thrust::get<3>(*y));
}
};
The value being returned is a double . Why the binary operation looks like the above functor is
not important. I just want to know how I would go about correcting the above syntactically.
As shown above the code is throwing a number of compilation errors. I am not sure where I have gone wrong.
I am using CUDA 4.0 on GTX 570 on Ubuntu 10.10

density_update should not receive tuples of iterators as parameters -- it needs tuples of the iterators' references.
In principle you could write density_update::operator() in terms of the particular reference type of the various iterators, but it's simpler to have the compiler infer the type of the parameters:
struct density_update
{
template<typename Tuple>
__host__ __device__
double operator()(const Tuple& x, const Tuple& y)
{
return thrust::get<0>(x) * (thrust::get<1>(x) - thrust::get<3>(x)) + \
thrust::get<0>(y) * (thrust::get<1>(y) - thrust::get<3>(y));
}
};

Related

How to change the default code generated by SWIG for the allocation of memory for a C structure?

I am using a flexible array in the structure. So I want to change the memory allocated for that structure with some of my own code. Basically I want to change the new_structname() and structname_variable_set() functions.
typedef struct vector{
int x;
char y;
int arr[0];
} vector;
here, SWIG generated new_vector() function to allocate memory by calling calloc(1,sizeof(struct vector)) where swig will not handle these type of structure in a special manner. So we need to modify the swig generated new_vector() in order to allocate memory for the flexible array. So is there any way to handle this?
There are a few ways you can do this. What you're looking for though is %extend. That lets us define new constructors and implement them as we see fit. (It even works with a C compiler, they're only constructors from the perspective of the target language).
Using your vector as a starting point we can illustrate this:
%module test
%include <stdint.i>
%inline %{
typedef struct vector{ int x; char y; int arr[0]; }vector;
%}
%extend vector {
vector(const size_t len) {
vector *v = calloc(1, sizeof *v + len);
v->x = len;
return v;
}
}
With this SWIG synthesises a new_vector function in the generated module code as you'd hoped.
I also assumed that you want to record the length inside the struct as one of its members. If that's not the case you can simply delete the assignment I made.

Reduce by key on device array

I am using reduce_by_key to find the number of elements in an array of type int2 which has same first values .
For example
Array: <1,2> <1,3> <1,4> <2,5> <2,7>
so no. elements with 1 as first element are 3 and with 2 are 2.
CODE:
struct compare_int2 : public thrust::binary_function<int2, int2, bool> {
__host__ __device__ bool operator()(const int2 &a,const int2 &b) const{
return (a.x == b.x);}
};
compare_int2 cmp;
int main()
{
int n,i;
scanf("%d",&n);
int2 *h_list = (int2 *) malloc(sizeof(int2)*n);
int *h_ones = (int *) malloc(sizeof(int)*n);
int2 *d_list,*C;
int *d_ones,*D;
cudaMalloc((void **)&d_list,sizeof(int2)*n);
cudaMalloc((void **)&d_ones,sizeof(int)*n);
cudaMalloc((void **)&C,sizeof(int2)*n);
cudaMalloc((void **)&D,sizeof(int)*n);
for(i=0;i<n;i++)
{
int2 p;
printf("Value ? ");
scanf("%d %d",&p.x,&p.y);
h_list[i] = p;
h_ones[i] = 1;
}
cudaMemcpy(d_list,h_list,sizeof(int2)*n,cudaMemcpyHostToDevice);
cudaMemcpy(d_ones,h_ones,sizeof(int)*n,cudaMemcpyHostToDevice);
thrust::reduce_by_key(d_list, d_list+n, d_ones, C, D,cmp);
return 0;
}
The above code is showing Segmentation Fault . I ran the above code using gdb and it reported the segfault at this location.
thrust::system::detail::internal::scalar::reduce_by_key >
(keys_first=0x1304740000,keys_last=0x1304740010,values_first=0x1304740200,keys_output=0x1304740400, values_output=0x1304740600,binary_pred=...,binary_op=...)
at /usr/local/cuda-6.5/bin/../targets/x86_64-linux/include/thrust/system/detail/internal/scalar/reduce_by_key.h:61 61
InputKeyType temp_key = *keys_first
How to use reduce_by_key on device arrays ?
Thrust interprets ordinary pointers as pointing to data on the host:
thrust::reduce_by_key(d_list, d_list+n, d_ones, C, D,cmp);
Therefore thrust will call the host path for the above algorithm, and it will seg fault when it attempts to dereference those pointers in host code. This is covered in the thrust getting started guide:
You may wonder what happens when a "raw" pointer is used as an argument to a Thrust function. Like the STL, Thrust permits this usage and it will dispatch the host path of the algorithm. If the pointer in question is in fact a pointer to device memory then you'll need to wrap it with thrust::device_ptr before calling the function.
Thrust has a variety of mechanisms (e.g. device_ptr, device_vector, and execution policy) to identify to the algorithm that the data is device-resident and the device path should be used.
The simplest modification for your existing code might be to use device_ptr:
#include <thrust/device_ptr.h>
...
thrust::device_ptr<int2> dlistptr(d_list);
thrust::device_ptr<int> donesptr(d_ones);
thrust::device_ptr<int2> Cptr(C);
thrust::device_ptr<int> Dptr(D);
thrust::reduce_by_key(dlistptr, dlistptr+n, donesptr, Cptr, Dptr,cmp);
The issue described above is similar to another issue you asked about.

thrust transform defining custom binary function

I am trying to write a custom function to carry out sum. I followed this question Cuda Thrust Custom function to take reference.Here is how I have defined my functor
struct hashElem
{
int freq;
int error;
};
//basically this function adds some value to to the error field of each element
struct hashErrorAdd{
const int error;
hashErrorAdd(int _error): error(_error){}
__host__ __device__
struct hashElem operator()(const hashElem& o1,const int& o2)
{
struct hashElem o3;
o3.freq = o1.freq;
o3.error = o1.error + (NUM_OF_HASH_TABLE-o2)*error; //NUM_OF_HASH_TABLE is a constant
return o3;
}
};
struct hashElem freqError[SIZE_OF_HASH_TABLE*NUM_OF_HASH_TABLE];
int count[SIZE_OF_HASH_TABLE*NUM_OF_HASH_TABLE];
thrust::device_ptr<struct hashElem> d_freqError(freqError);
thrust::device_ptr<int> d_count(count);
thrust::transform(thrust::device,d_freqError,d_freqError+new_length,d_count,hashErrorAdd(perThreadLoad)); //new_length is a constant
This code on compilation gives the following error:
error: function "hashErrorAdd::operator()" cannot be called with the given argument list
argument types are: (hashElem)
object type is: hashErrorAdd
Please can anybody explain to me why I am getting this error? and how I can resolve it. Please comment in case I am not able to explain the problem clearly. Thankyou.
It appears that you want to pass two input vectors to thrust::transform and then do an in-place transform (i.e. no output vector is specified).
There is no such incarnation of thrust::transform
Since you have passed:
thrust::transform(vector_first, vector_last, vector_first, operator);
The closest matching prototype is a version of transform that takes one input vector and creates one output vector. In that case, you would need to pass a unary op that takes the input vector type (hashElem) only as an argument, and returns a type appropriate for the output vector, which is int in this case, i.e. as you have written it (not as your intent). Your operator() does not do that, and it cannot be called with the arguments that thrust is expecting to pass to it.
As I see it, you have a couple options:
You could switch to the version of transform that takes two input vectors and produces one output vector, and create a binary op as functor.
You could zip together your two input vectors, and do an in-place transform if that is what you want. Your functor would then be a unary op, but it would take as argument whatever tuple was created from dereferencing the input vector, and it would have to return or modify the same kind of tuple.
As an aside, your method of creating device pointers directly from host arrays looks broken to me. You may wish to review the thrust quick start guide.

CUDA: How to apply __restrict__ on array of pointers to arrays?

This kernel using two __restrict__ int arrays compiles fine:
__global__ void kerFoo( int* __restrict__ arr0, int* __restrict__ arr1, int num )
{
for ( /* Iterate over array */ )
arr1[i] = arr0[i]; // Copy one to other
}
However, the same two int arrays composed into a pointer array fails compilation:
__global__ void kerFoo( int* __restrict__ arr[2], int num )
{
for ( /* Iterate over array */ )
arr[1][i] = arr[0][i]; // Copy one to other
}
The error given by the compiler is:
error: invalid use of `restrict'
I have certain structures that are composed as an array of pointers to arrays. (For example, a struct passed to the kernel that has int* arr[16].) How do I pass them to kernels and be able to apply __restrict__ on them?
The CUDA C manual only refers to the C99 definition of __restrict__, no special CUDA-specific circumstances.
Since the indicated parameter is an array containing two pointers, this use of __restrict__ looks perfectly valid to me, no reason for the compiler to complain IMHO. I would ask the compiler author to verify and possibly/probably correct the issue. I'd be interested in different opinions, though.
One remark to #talonmies:
The whole point of restrict is to tell the compiler that two or more pointer arguments will never overlap in memory.
This is not strictly true. restrict tells the compiler that the pointer in question, for the duration of its lifetime, is the only pointer through which the pointed-to object can be accessed. Be aware that the object pointed to is only assumed to be an array of int. (In truth it's only one int in this case.) Since the compiler cannot know the size of the array, it is up to the programmer to guard the array's boundaries..
Filling in the comment in your code with some arbitrary iteration, we get the following program:
__global__ void kerFoo( int* __restrict__ arr[2], int num )
{
for ( int i = 0; i < 1024; i ++)
arr[1][i] = arr[0][i]; // Copy one to other
}
and this compiles fine with CUDA 10.1 (Godbolt.org).

Cannot overload make_uint4 function

I'm trying to overload make_uint4 in the following manner:
namespace A {
namespace B {
inline __host__ __device__ uint4 make_uint4(uint2 a, uint2 b) {
return make_uint4(a.x, a.y, b.x, b.y);
}
}
}
But when I try to compile it, nvcc returns an error:
error: no suitable constructor exists to convert from "unsigned int" to "uint2"
error: no suitable constructor exists to convert from "unsigned int" to "uint2"
error: too many arguments in function call
All these errors point to the "return…" line.
I was able to get a partial repro on VS 2010 and CUDA 4.0 (the compiler built the code OK but Intellisense flagged the error you are seeing). Try the following:
#include "vector_functions.h"
inline __host__ __device__ uint4 make_uint4(uint2 a, uint2 b)
{
return ::make_uint4(a.x, a.y, b.x, b.y);
}
This fixed it for me.
I have no problem compiling it in Visual Studio+nvcc. What compiler are you using?
If that would be of any help: make_uint4 is defined in vector_functions.h, line 170 as
static __inline__ __host__ __device__ uint4 make_uint4(unsigned int x, unsigned int y, unsigned int z, unsigned int w)
{
uint4 t; t.x = x; t.y = y; t.z = z; t.w = w; return t;
}
Update:
I get similar error when I try to overload the function while being inside my custom namespace. Are you certain you are not inside one? If so, try putting :: in front of function call to refer to global scope, i.e:
return ::make_uint4(a.x, a.y, b.x, b.y);
I don't have the library code, but it seems like the compiler doesn't like overloaded device functions (as they are treated just like really fancy inline macros). What is does is shadow (hide) the old make_uint4(a,b,c,d) with your new make_uint4(va, vb) and try to call the latter with 4 uint parameters. That doesn't work because there is no conversion from uint to uint2 (as indicated by the first two error messages) and there are 4 instead of 2 arguments (the last error message).
Use a slightly different function name like make_uint4_from_uint2s and you'll be fine.