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// Test for boxmuller.h on CUDA
#include <Random123/philox.h>
#include <Random123/threefry.h>
#include "util.h" // for timer()
#include "util_cuda.h" // for cuda_init, CHECKCALL
#include "boxmuller.hpp"
typedef r123::Philox4x32 CBRNGF;
typedef r123::Threefry2x64 CBRNGD;
int debug = 0;
const char *progname = "time_boxmuller_cuda";
// Sometimes warnings are A LOT more trouble than they're worth.
// if we just write u[6], we get warnings
// so we write u[(csize>n)?6:0].
#define UGLY(n) (csize>n)?n:0
// The timedloop kernel sums N randoms per thread for timing and
// records that sum in out[tid] (mainly to ensure that
// the random generation process does not get optimized away)
template <typename CBRNG, typename F, typename F2>
__global__ void timedloop(F *out, typename CBRNG::ukey_type k, size_t N){
unsigned tid = blockDim.x * blockIdx.x + threadIdx.x;
const size_t klast = sizeof(k)/sizeof(k[0]) - 1;
R123_ASSERT(k[klast] == 0); // uses last element of key to
k[klast] = tid; // ensure unique key per thread
F sum = 0.f;
typename CBRNG::ctr_type ctr = {};
const size_t csize = sizeof(ctr)/sizeof(ctr[0]);
CBRNG rng;
for(size_t i=0; i<N; i+=csize){
ctr.incr();
typename CBRNG::ctr_type u = rng(ctr, k);
F2 f2;
// Using a loop instead of the Duff device here costs 10%,
// at least in CUDA4.2 circa Jan 2013 on a Tesla C2050!
switch(csize) {
case 8: f2 = r123::boxmuller(u[UGLY(6)], u[UGLY(7)]); sum += f2.x + f2.y;
f2 = r123::boxmuller(u[UGLY(4)], u[UGLY(5)]); sum += f2.x + f2.y;
case 4: f2 = r123::boxmuller(u[UGLY(2)], u[UGLY(3)]); sum += f2.x + f2.y;
case 2: f2 = r123::boxmuller(u[0], u[1]); sum += f2.x + f2.y;
break;
default:
R123_ASSERT(0);
}
}
out[tid] = sum;
}
// The dumploop kernel records all the normal randoms individually in out,
// so it produces N randoms per thread. Each thread records
// its randoms in tid, NTHREADS+tid, NTHREAD*2+tid, ..., NTHREADS*(N-1)+tid
// which hopefully results in nicely coalesced writes from each warp.
template <typename CBRNG, typename F, typename F2>
__global__ void dumploop(F *out, typename CBRNG::ukey_type k, size_t N){
unsigned tid = blockDim.x * blockIdx.x + threadIdx.x;
const size_t klast = sizeof(k)/sizeof(k[0]) - 1;
R123_ASSERT(k[klast] == 0); // uses last element of key to
k[klast] = tid; // ensure unique key per thread
typename CBRNG::ctr_type ctr = {};
const size_t csize = sizeof(ctr)/sizeof(ctr[0]);
CBRNG rng;
for(size_t i=0; i<N;){
ctr.incr();
typename CBRNG::ctr_type u = rng(ctr, k);
F2 f2;
// Using a loop instead of the Duff device here costs 10%,
// at least in CUDA4.2 circa Jan 2013 on a Tesla C2050!
switch(csize) {
case 8: f2 = r123::boxmuller(u[UGLY(6)], u[UGLY(7)]);
out[blockDim.x*gridDim.x*i + tid] = f2.x;
i++;
out[blockDim.x*gridDim.x*i + tid] = f2.y;
i++;
f2 = r123::boxmuller(u[UGLY(4)], u[UGLY(5)]);
out[blockDim.x*gridDim.x*i + tid] = f2.x;
i++;
out[blockDim.x*gridDim.x*i + tid] = f2.y;
i++;
case 4: f2 = r123::boxmuller(u[UGLY(2)], u[UGLY(3)]);
#undef UGLY
out[blockDim.x*gridDim.x*i + tid] = f2.x;
i++;
out[blockDim.x*gridDim.x*i + tid] = f2.y;
i++;
case 2: f2 = r123::boxmuller(u[0], u[1]);
out[blockDim.x*gridDim.x*i + tid] = f2.x;
i++;
out[blockDim.x*gridDim.x*i + tid] = f2.y;
i++;
break;
default:
asm("trap;");
}
}
}
template <typename CBRNG, typename F, typename F2>
void timedcall(const char *tname, const char *out_fname, CUDAInfo *infop, typename CBRNG::ukey_type k, size_t N) {
double cur_time, dt;
const int nthreads = infop->blocks_per_grid*infop->threads_per_block;
const size_t nrand = out_fname ? N * nthreads : nthreads;
const size_t out_size = nrand*sizeof(F);
F *d_out, *h_out = (F *) malloc(out_size);
CHECKNOTZERO(h_out);
CHECKCALL(cudaMalloc(&d_out, out_size));
(void) timer(&cur_time);
if (out_fname)
dumploop<CBRNG,F,F2> <<<infop->blocks_per_grid, infop->threads_per_block>>> (d_out, k, N);
else
timedloop<CBRNG,F,F2> <<<infop->blocks_per_grid, infop->threads_per_block>>> (d_out, k, N);
CHECKCALL(cudaThreadSynchronize());
CHECKCALL(cudaMemcpy(h_out, d_out, out_size, cudaMemcpyDeviceToHost));
dt = timer(&cur_time);
printf("%s %zd in %g sec: %gM/sec\n", tname, N*nthreads, dt, N*nthreads*1.e-6/dt);
if (out_fname) {
char *fname = (char *) malloc(strlen(out_fname) + strlen(tname) + 2);
CHECKNOTZERO(fname);
sprintf(fname, "%s-%s", out_fname, tname);
FILE *fp = fopen(fname, "w");
CHECKNOTZERO(fp);
for (size_t i = 0; i < nrand; i++){
fprintf(fp, "%g\n", h_out[i]);
}
fclose(fp);
free(fname);
} else {
int nwoops = 0;
printf("%s h_out[0] = %g\n", tname, h_out[0]);
for (size_t i = 0; i < nrand; i++){
if(h_out[i] == 0.f){
if(nwoops++<10)
printf("Woops %s h_out[%zd] = %g\n", tname, i, h_out[i]);
}
}
if(nwoops>10){
printf("Woops %s %d times\n", tname, nwoops);
}
}
CHECKCALL(cudaFree(d_out));
free(h_out);
}
const size_t DEF_N = 200000;
int main(int argc, char **argv){
CBRNGF::ukey_type keyf = {};
CBRNGD::ukey_type keyd = {};
size_t Ntry = DEF_N;
char *cp = getenv("R123_DEBUG");
if (cp)
debug = atoi(cp);
if ((cp = getenv("BOXMULLER_DUMPFILE")) != NULL) {
Ntry = 8;
} else {
Ntry = DEF_N;
}
if(argc>1) {
if (argv[1][0] == '-') {
fprintf(stderr, "Usage: %s [iterations_per_thread [key0 [key1]]]\n", argv[0]);
exit(1);
}
Ntry = atol(argv[1]);
}
// XXX cannot use keyf.size in host code, only in device code
for (int i = 0; i < (int)(sizeof(keyf)/sizeof(keyf[0])-1) && 2+i < argc; i++) {
keyf.v[i] = atol(argv[2+i]);
}
for (int i = 0; i < (int)(sizeof(keyd)/sizeof(keyd[0])-1) && 2+i < argc; i++) {
keyd.v[i] = atol(argv[2+i]);
}
CUDAInfo *infop = cuda_init(getenv("R123_CUDA_DEVICE"));
timedcall<CBRNGF,float,r123::float2>("float", cp, infop, keyf, Ntry);
timedcall<CBRNGD,double,r123::double2>("double",cp, infop, keyd, Ntry);
cuda_done(infop);
return 0;
}
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