allocating shared memory

i am trying to allocate shared memory by using a constant parameter but getting an error. my kernel looks like this:

__global__ void Kernel(const int count)
{
    __shared__ int a[count];
}

and i am getting an error saying

error: expression must have a constant value

count is const! Why am I getting this error? And how can I get around开发者_StackOverflow社区 this?

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CUDA supports dynamic shared memory allocation. If you define the kernel like this:

__global__ void Kernel(const int count)
{
    extern __shared__ int a[];
}

and then pass the number of bytes required as the the third argument of the kernel launch

Kernel<<< gridDim, blockDim, a_size >>>(count)

then it can be sized at run time. Be aware that the runtime only supports a single dynamically declared allocation per block. If you need more, you will need to use pointers to offsets within that single allocation. Also be aware when using pointers that shared memory uses 32 bit words, and all allocations must be 32 bit word aligned, irrespective of the type of the shared memory allocation.

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const doesn't mean "constant", it means "read-only".

A constant expression is something whose value is known to the compiler at compile-time.

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option one: declare shared memory with constant value (not the same as const)

__global__ void Kernel(int count_a, int count_b)
{
    __shared__ int a[100];
    __shared__ int b[4];
}

option two: declare shared memory dynamically in the kernel launch configuration:

__global__ void Kernel(int count_a, int count_b)
{
    extern __shared__ int *shared;
    int *a = &shared[0]; //a is manually set at the beginning of shared
    int *b = &shared[count_a]; //b is manually set at the end of a
}

sharedMemory = count_a*size(int) + size_b*size(int);
Kernel <<<numBlocks, threadsPerBlock, sharedMemory>>> (count_a, count_b);

note: Pointers to dynamically shared memory are all given the same address. I use two shared memory arrays to illustrate how to manually set up two arrays in shared memory.

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From the "CUDA C Programming Guide":

The execution configuration is specified by inserting an expression of the form:

<<<Dg, Db, Ns, S>>>

where:

• Dg is of type dim3 and specifies the dimensioin and size of the grid ...
• Db is of type dim3 and specifies the dimension and size of each block ...
• Ns is of type size_t and specifies the number of bytes in shared memory that is dynamically allocated per block for this call in addition to the statically allocated memory. This dynamically allocated memory is used by any of the variables declared as an external array as mentioned in __shared__; Ns is optional argument which defaults to 0;
• S is of type cudaStream_t and specifies the associated stream ...

So by using the dynamical parameter Ns, the user can specify the total size of shared memory one kernel function can use, no matter how many shared variables there are in this kernel.

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You cannot declare shared variable like this..

__shared__ int a[count];

although if you are sure enough about the max size of array a then you can directly declare like

__shared__ int a[100];

but in this case you should be worried about how many blocks are there in your program , since fixing shared memory to a block ( and not getting utilized fully), will lead you to context switching with global memory( high latency) , thus poor performance...

There is a nice solution to this problem to declare

extern __shared__ int a[];

and allocating the memory while calling kernel from memory like

Kernel<<< gridDim, blockDim, a_size >>>(count)

but you should also be bothered here because if you are using more memory in blocks than you are assigning in kernel , you are going to getting unexpected results.