CUDA function pointers

To get rid of your compile error, you'll have to use -gencode arch=compute_20,code=sm_20 as a compiler argument when compiling your code. But then you'll likely have some runtime problems:

Taken from the CUDA Programming Guide

Function pointers to __global__ functions are supported in host code, but not in device code. Function pointers to __device__ functions are only supported in device code compiled for devices of compute capability 2.x and higher.

It is not allowed to take the address of a __device__ function in host code.

so you can have something like this (adapted from the "FunctionPointers" sample):

//your function pointer type - returns unsigned char, takes parameters of type unsigned char and float
typedef unsigned char(*pointFunction_t)(unsigned char, float);

//some device function to be pointed to
__device__ unsigned char
Threshold(unsigned char in, float thresh)

//pComputeThreshold is a device-side function pointer to your __device__ function
__device__ pointFunction_t pComputeThreshold = Threshold;
//the host-side function pointer to your __device__ function
pointFunction_t h_pointFunction;

//in host code: copy the function pointers to their host equivalent
cudaMemcpyFromSymbol(&h_pointFunction, pComputeThreshold, sizeof(pointFunction_t))

You can then pass the h_pointFunction as a parameter to your kernel, which can use it to call your __device__ function.

//your kernel taking your __device__ function pointer as a parameter
__global__ void kernel(pointFunction_t pPointOperation)
    unsigned char tmp;
    tmp = (*pPointOperation)(tmp, 150.0)

//invoke the kernel in host code, passing in your host-side __device__ function pointer

Hopefully that made some sense. In all, it looks like you would have to change your f1 function to be a __device__ function and follow a similar procedure (the typedefs aren't necessary, but they do make the code nicer) to get it as a valid function pointer on the host-side to pass to your kernel. I'd also advise giving the FunctionPointers CUDA sample a look over