I'm writing a CUDA application that has a step where the variance of some complex-valued input data is computed, and then that variance is used to threshold the data. I've got a reduction kernel that computes the variance for me, but I'm not sure if I have to pull the value back to the host to pass it to the thresholding kernel or not.
Is there a way to pass the value directly from device memory?
You can use a __device__ variable to hold the variance value in-between kernel calls.
Put this before the definition of the kernels that use it:
__device__ float my_variance = 0.0f;
Variables defined this way can be used by any kernel executing on the device (without requiring that they be explicitly passed as a kernel function parameter) and persist for the lifetime of the context, i.e. beyond the lifetime of any single kernel call.
It's not entirely clear from your question, but you can also define an array of data this way.
__device__ float my_variance[32] = {0.0f};
Likewise, allocations created by cudaMalloc live for the duration of the application/context (or until an appropriate cudaFree is encountered) and so there is no need to "pull back the data" to the host if you want to use it in a successive kernel:
float *d_variance;
cudaMalloc((void **)&d_variance), sizeof(float));
my_reduction_kernel<<<...>>>(..., d_variance, ...);
my_thresholding_kernel<<<...>>>(..., d_variance, ...);
Any value set in *d_variance by the reduction kernel above will be properly observed by the thresholding kernel.
Related
On the host side, I can save the amount of dynamic shared memory I intend to launch a kernel with, and use it. I can even pass that as an argument to the kernel. But - is there a way to get it directly from device code, without help from the host side? That is, have the code for a kernel determine, as it runs, how much dynamic shared memory it has available?
Yes, there's a special register holding that value. named %dynamic_smem_size. You can obtain this register's value in your CUDA C/C++ code by wrapping some inline PTX with a getter function:
__device__ unsigned dynamic_smem_size()
{
unsigned ret;
asm volatile ("mov.u32 %0, %dynamic_smem_size;" : "=r"(ret));
return ret;
}
You can similarly obtain the total size of allocated shared memory (static + dynamic) from the register %total_smem_size.
I need to find the index of the maximum element in an array of floats. I am using the function "cublasIsamax", but this returns the index to the CPU, and this is slowing down the running time of the application.
Is there a way to compute this index efficiently and store it in the GPU?
Thanks!
Since the CUBLAS V2 API was introduced (with CUDA 4.0, IIRC), it is possible to have routines which return a scalar or index to store those directly into a variable in device memory, rather than into a host variable (which entails a device to host transfer and might leave the result in the wrong memory space).
To use this, you need to use the cublasSetPointerMode call to tell the CUBLAS context to expect pointers for scalar arguments to be device pointers by using the CUBLAS_POINTER_MODE_DEVICE mode. This then implies that in a call like
cublasStatus_t cublasIsamax(cublasHandle_t handle, int n,
const float *x, int incx, int *result)
that result must be a device pointer.
If you want to use CUBLAS and you have a GPU with compute capability 3.5 (K20, Titan) than you can use CUBLAS with dynamic parallelism. Than you can call CUBLAS from within a kernel on the GPU and no data will be returned to the CPU.
If you have no device with cc 3.5 you will probably have to implement a find max function by yourself or look for an aditional library.
I'm confused about copying arrays to constant memory.
According to programming guide there's at least one way to allocate constant memory and use it in order to store an array of values. And this is called static memory allocation:
__constant__ float constData[256];
float data[256];
cudaMemcpyToSymbol(constData, data, sizeof(data));
cudaMemcpyFromSymbol(data, constData, sizeof(data));
According to programming guide again we can use:
__device__ float* devPointer;
float* ptr;
cudaMalloc(&ptr, 256 * sizeof(float));
cudaMemcpyToSymbol(devPointer, &ptr, sizeof(ptr));
It looks like dynamic constant memory allocation is used, but I'm not sure about it. And also no qualifier __constant__ is used here.
So here are some questions:
Is this pointer stored in constant memory?
Is assigned (by this pointer) memory stored in constant memory too?
Is this pointer constant? And it's not allowed to change that pointer using device or host function. But is changing values of array prohibited or not? If changing values of array is allowed, then does it mean that constant memory is not used to store this values?
The developer can declare up to 64K of constant memory at file scope. In SM 1.0, the constant memory used by the toolchain (e.g. to hold compile-time constants) was separate and distinct from the constant memory available to developers, and I don't think this has changed since. The driver dynamically manages switching between different views of constant memory as it launches kernels that reside in different compilation units. Although you cannot allocate constant memory dynamically, this pattern suffices because the 64K limit is not system-wide, it only applies to compilation units.
Use the first pattern cited in your question: statically declare the constant data and update it with cudaMemcpyToSymbol before launching kernels that reference it. In the second pattern, only reads of the pointer itself will go through constant memory. Reads using the pointer will be serviced by the normal L1/L2 cache hierarchy.
I am new with CUDA, and I am confuse with the kernel calls.
When you call a Kernel method you specify the number of blocks and the thread per block, like this kernelMethod<<< block, Threads >>>(parameters);"
So why it is possible to use a 3rd parameter?
kernelMethod<<< block, Threads, ???>>>(parameters);
Using cudaDeviceProp you can read the number of thread per block in the variable maxThreadsPerBlock. But how can I know the maximum number of blocks?
Thanks!!
The third parameter specifies the amount of shared memory per block to be dynamically allocated. The programming guide provides additional detail about shared memory, as well as a description and example.
Shared memory can be allocated statically in a kernel:
__shared__ int myints[256];
or dynamically:
extern __shared__ int myints[];
In the latter case, it's necessary to pass as an additional kernel config parameter (the 3rd parameter you mention) the size of the shared memory to be allocated in bytes.
In that event, the pointer myints then points to the beginning of that dynamically allocated region.
The maximum number of blocks is specified per grid dimension (x, y, z) and can also be obtained through the device properties query. It is specified in the maxGridSize parameter. You may want to refer to the deviceQuery sample for a worked example.
Is there any way to declare an array such as:
int arraySize = 10;
int array[arraySize];
inside a CUDA kernel/function? I read in another post that I could declare the size of the shared memory in the kernel call and then I would be able to do:
int array[];
But I cannot do this. I get a compile error: "incomplete type is not allowed". On a side note, I've also read that printf() can be called from within a thread and this also throws an error: "Cannot call host function from inside device/global function".
Is there anything I can do to make a variable sized array or equivalent inside CUDA? I am at compute capability 1.1, does this have anything to do with it? Can I get around the variable size array declarations from within a thread by defining a typedef struct which has a size variable I can set? Solutions for compute capabilities besides 1.1 are welcome. This is for a class team project and if there is at least some way to do it I can at least present that information.
About the printf, the problem is it only works for compute capability 2.x. There is an alternative cuPrintf that you might try.
For the allocation of variable size arrays in CUDA you do it like this:
Inside the kernel you write extern __shared__ int[];
On the kernel call you pass as the third launch parameter the shared memory size in bytes like mykernel<<<gridsize, blocksize, sharedmemsize>>>();
This is explained in the CUDA C programming guide in section B.2.3 about the __shared__ qualifier.
If your arrays can be large, one solution would be to have one kernel that computes the required array sizes, stores them in an array, then after that invocation, the host allocates the necessary arrays and passes an array of pointers to the threads, and then you run your computation as a second kernel.
Whether this helps depends on what you have to do, because it would be arrays allocated in global memory. If the total size (per block) of your arrays is less than the size of the available shared memory, you could have a sufficiently-large shared memory array and let your threads negociate splitting it amongst themselves.