Greg Lindahl wrote:
On Wed, Jun 18, 2008 at 10:51:04AM -0400, Prentice Bisbal wrote:
Someone made the inaccurate statement that CUDA programming is difficult
and time-consuming.
One data point cannot prove that CUDA is easy. There are people out
there claiming that FPGAs are easy to program, because they're one of
the 7 people on the planet for whom programming an FPGA is easy.
*chuckle*.
I've looked over CUDA and some examples, and while it's better looking
than some of the other GPU programming languages out there, it's clear
that it is more difficult and time-consuming than using traditional
languages on traditional cpus.
Agreed. Traditional languages are easier, but don't express parallelism well.
One approach is of course openMP, a few pragmas, and parallel friendly (loop
index independent) loops and you can get reasonable speedups on SMP machines.
Cuda seems to take a different approach, instead of trying to auto-parallelize
a loop, it requires a function pointer to the code, and the function must
declare it's exit condition.
CUDA seems rather similar to openMP. Massimiliano Fatica of nvidia did a
stream port, and I'll quote pieces of his code below.
So instead of:
for (j=0; j<N; j++)
b[j] = scalar*c[j];
You get:
__global__ void STREAM_Scale(float *a, float *b, float scale, int len)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < len) b[idx] = scale* a[idx];
}
Then to actually time and launch:
times[1][k]= mysecond();
STREAM_Scale<<<dimGrid,dimBlock>>>(d_b, d_c, scalar, N);
cudaThreadSynchronize();
times[1][k]= mysecond() - times[1][k];
Instead of:
static double a[N+OFFSET]
You get:
cudaMalloc((void**)&d_a, sizeof(float)*N);
Instead of:
for (j=0; j<N; j++) {
a[j] = 1.0;
b[j] = 2.0;
c[j] = 0.0;
}
You get:
set_array<<<dimGrid,dimBlock>>>(d_a, 2.f, N);
set_array<<<dimGrid,dimBlock>>>(d_b, .5f, N);
set_array<<<dimGrid,dimBlock>>>(d_c, .5f, N);
So yes, it's a change, but it does seem pretty reasonable.
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