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hmm.cu
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hmm.cu
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/*
* Copyright (c) 2009, Chuan Liu <[email protected]>
*
* Permission is hereby granted, free of charge, to any person
* obtaining a copy of this software and associated documentation
* files (the "Software"), to deal in the Software without
* restriction, including without limitation the rights to use, copy,
* modify, merge, publish, distribute, sublicense, and/or sell copies
* of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be
* included in all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
* NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
* BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
* ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
* CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*/
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <cuda.h>
#include <helper_cuda.h>
#include <helper_timer.h>
#define handle_error(msg) \
do { perror(msg); exit(EXIT_FAILURE); } while (0)
#define IDX(i,j,d) (((i)*(d))+(j))
enum {
BLOCK_SIZE = 16,
NUM_THREADS = 256,
IN_DEVICE = 1,
IN_HOST = 0,
};
int nstates = 0; /* number of states */
int nobvs = 0; /* number of observations */
int nseq = 0; /* number of data sequences */
int length = 0; /* data sequencel length */
float *prior = NULL; /* initial state probabilities */
float *trans = NULL; /* state transition probabilities */
float *obvs = NULL; /* output probabilities */
int *data = NULL; /* observations */
float *transd = NULL; /* trans in device memory */
float *obvsd = NULL; /* obvs in device memory */
float *gmmd = NULL; /* gamma in device memory */
float *xid = NULL; /* xi in device memory */
float *pid = NULL; /* pi in device memory */
#ifdef PROFILE_GPU
StopWatchInterface *gpu_timer;
double gpu_flop;
float gpu_time;
#endif
#ifdef PROFILE_PG
StopWatchInterface *pg_timer;
#endif
/* function called in main fuction */
void usage();
void freeall();
void init_count();
float forward_backward(int backward);
void viterbi();
void update_prob();
/* utility functions */
float logadd(float, float);
__device__ float logaddd(float, float);
float sumf(float *, int, int);
float logsumf(float *, int, int);
int main(int argc, char *argv[])
{
char *configfile = NULL;
FILE *fin, *bin;
char *linebuf = NULL;
size_t buflen = 0;
int iterations = 1;
int mode = 3;
int c;
float d;
int i, j, k;
opterr = 0;
while ((c = getopt(argc, argv, "c:n:hp:")) != -1) {
switch (c) {
case 'c':
configfile = optarg;
break;
case 'h':
usage();
exit(EXIT_SUCCESS);
case 'n':
iterations = atoi(optarg);
break;
case 'p':
mode = atoi(optarg);
if (mode != 1 && mode != 2 && mode != 3) {
fprintf(stderr, "illegal mode: %d\n", mode);
exit(EXIT_FAILURE);
}
break;
case '?':
fprintf(stderr, "illegal options\n");
exit(EXIT_FAILURE);
default:
abort();
}
}
if (configfile == NULL) {
fin = stdin;
} else {
fin = fopen(configfile, "r");
if (fin == NULL) {
handle_error("fopen");
}
}
i = 0;
while ((c = getline(&linebuf, &buflen, fin)) != -1) {
if (c <= 1 || linebuf[0] == '#')
continue;
if (i == 0) {
if (sscanf(linebuf, "%d", &nstates) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
prior = (float *) malloc(sizeof(float) * nstates);
if (prior == NULL) handle_error("malloc");
trans = (float *) malloc(sizeof(float) * nstates * nstates);
if (trans == NULL) handle_error("malloc");
} else if (i == 1) {
if (sscanf(linebuf, "%d", &nobvs) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
obvs = (float *) malloc(sizeof(float) * nstates * nobvs);
if (obvs == NULL) handle_error("malloc");
} else if (i == 2) {
/* read initial state probabilities */
bin = fmemopen(linebuf, buflen, "r");
if (bin == NULL) handle_error("fmemopen");
for (j = 0; j < nstates; j++) {
if (fscanf(bin, "%f", &d) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
prior[j] = logf(d);
}
fclose(bin);
} else if (i <= 2 + nstates) {
/* read state transition probabilities */
bin = fmemopen(linebuf, buflen, "r");
if (bin == NULL) handle_error("fmemopen");
for (j = 0; j < nstates; j++) {
if (fscanf(bin, "%f", &d) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
trans[IDX((i - 3), j, nstates)] = logf(d);
}
fclose(bin);
} else if (i <= 2 + nstates * 2) {
/* read output probabilities */
bin = fmemopen(linebuf, buflen, "r");
if (bin == NULL) handle_error("fmemopen");
for (j = 0; j < nobvs; j++) {
if (fscanf(bin, "%f", &d) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
obvs[IDX((i - 3 - nstates), j, nobvs)] = logf(d);
}
fclose(bin);
} else if (i == 3 + nstates * 2) {
if (sscanf(linebuf, "%d %d", &nseq, &length) != 2) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
data = (int *) malloc (sizeof(int) * nseq * length);
if (data == NULL) handle_error("malloc");
} else if (i <= 3 + nstates * 2 + nseq) {
/* read data */
bin = fmemopen(linebuf, buflen, "r");
if (bin == NULL) handle_error("fmemopen");
for (j = 0; j < length; j++) {
if (fscanf(bin, "%d", &k) != 1 || k < 0 || k >= nobvs) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
data[j * nseq + (i - 4 - nstates * 2)] = k;
}
fclose(bin);
}
i++;
}
fclose(fin);
if (linebuf) free(linebuf);
if (i < 4 + nstates * 2 + nseq) {
fprintf(stderr, "configuration incomplete.\n");
freeall();
exit(EXIT_FAILURE);
}
/* initial cuda device memory */
c = sizeof(float) * nstates * nstates;
checkCudaErrors( cudaMalloc((void**)&transd, c) );
checkCudaErrors( cudaMemcpy(transd, trans, c, cudaMemcpyHostToDevice) );
c = sizeof(float) * nstates * nobvs;
checkCudaErrors( cudaMalloc((void**)&obvsd, c) );
checkCudaErrors( cudaMemcpy(obvsd, obvs, c, cudaMemcpyHostToDevice) );
#ifdef PROFILE_GPU
gpu_time = 0;
gpu_flop = 0;
#endif
#ifdef PROFILE_PG
sdkCreateTimer( &pg_timer );
sdkStartTimer(&pg_timer);
#endif
if (mode == 3) {
/* estimating parameters using Baum-Welch algorithm */
for (i = 0; i < iterations; i++) {
init_count();
d = forward_backward(1);
update_prob();
#ifdef PROFILE_PG
sdkStopTimer(&pg_timer);
#endif
printf("iteration %d log-likelihood: %.4f\n", i + 1, d);
printf("updated parameters:\n");
printf("# initial state probability\n");
for (j = 0; j < nstates; j++) {
printf(" %.4f", exp(prior[j]));
}
printf("\n");
printf("# state transition probability\n");
for (j = 0; j < nstates; j++) {
for (k = 0; k < nstates; k++) {
printf(" %.4f", exp(trans[IDX(j,k,nstates)]));
}
printf("\n");
}
printf("# state output probility\n");
for (j = 0; j < nstates; j++) {
for (k = 0; k < nobvs; k++) {
printf(" %.4f", exp(obvs[IDX(j,k,nobvs)]));
}
printf("\n");
}
printf("\n");
}
#ifdef PROFILE_PG
sdkStartTimer(&pg_timer);
#endif
} else if (mode == 1) {
/* compute forward probabilities */
forward_backward(0);
} else if (mode == 2) {
/* find most likely path using Viterbi algorithm */
viterbi();
}
freeall();
#ifdef PROFILE_PG
sdkStopTimer(&pg_timer);
printf("Programming running time (in Ms): %f\n", sdkGetTimerValue(&pg_timer));
sdkDeleteTimer( &pg_timer);
#endif
#ifdef PROFILE_GPU
printf("GPU time (in Ms): %f\n", gpu_time);
printf("GFLOPS: %lf\n", gpu_flop / gpu_time);
#endif
return 0;
}
/* kernel function copied from NVIDIA CUDA SDK */
__global__ void
reduce2(float *g_idata, float *g_odata)
{
__shared__ float sdata[NUM_THREADS];
/* load shared mem */
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
sdata[tid] = g_idata[i];
__syncthreads();
/* do reduction in shared mem */
for(unsigned int s = blockDim.x / 2; s > 0; s >>= 1)
{
if (tid < s)
{
sdata[tid] += sdata[tid + s];
}
__syncthreads();
}
/* write result for this block to global mem */
if (tid == 0) g_odata[blockIdx.x] = sdata[0];
}
/* calculate the sum of the array of n floats by calling a reduce
kernel recursively. indevce indicates whether the array points to
memroy in device or not, i.e. indevice = 0 means the array is in
main memory. */
float sumf(float *array, int n, int indevice)
{
int i;
int num_blocks;
int remains;
int size;
float sum = 0.0;
float *gin;
/* NUM_THREADS also serves as CPU threshold */
if (n < NUM_THREADS) {
if (indevice == 0) {
for (i = 0; i < n; i++) {
sum += array[i];
}
} else {
float gout[n];
size = sizeof(float) * n;
checkCudaErrors( cudaMemcpy(gout, array, size, cudaMemcpyDeviceToHost) );
for (i = 0; i < n; i++) {
sum += gout[i];
}
}
} else {
num_blocks = n / NUM_THREADS;
remains = n - num_blocks * NUM_THREADS;
dim3 dimBlock(NUM_THREADS);
dim3 dimGrid(num_blocks);
if (indevice == 0) {
size = sizeof(float) * num_blocks * NUM_THREADS;
checkCudaErrors( cudaMalloc((void**) &gin, size) );
checkCudaErrors( cudaMemcpy(gin, array, size, cudaMemcpyHostToDevice) );
reduce2<<<dimGrid, dimBlock>>>(gin, gin);
sum += sumf(gin, num_blocks, 1);
if (remains > 0)
sum += sumf(array + num_blocks * NUM_THREADS, remains, 0);
checkCudaErrors( cudaFree(gin) );
} else {
reduce2<<<dimGrid, dimBlock>>>(gin, gin);
sum += sumf(gin, num_blocks, 1);
if (remains > 0)
sum += sumf(gin + num_blocks * NUM_THREADS, remains, 1);
}
}
return sum;
}
/* logarithm version of the reduce kernel function */
__global__ void
logreduce2(float *g_idata, float *g_odata)
{
__shared__ float sdata[NUM_THREADS];
/* load shared mem */
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
sdata[tid] = g_idata[i];
__syncthreads();
/* do reduction in shared mem */
for(unsigned int s = blockDim.x / 2; s > 0; s >>= 1) {
if (tid < s) {
sdata[tid] = logaddd(sdata[tid], sdata[tid + s]);
}
__syncthreads();
}
/* write result for this block to global mem */
if (tid == 0) g_odata[blockIdx.x] = sdata[0];
}
/* logarithm version of the sumf function */
float logsumf(float *array, int n, int indevice)
{
int i;
int num_blocks;
int remains;
int size;
float sum = - INFINITY;
float *gin;
/* NUM_THREADS also serves as CPU threshold */
if (n < NUM_THREADS) {
if (indevice == 0) {
for (i = 0; i < n; i++) {
sum = logadd(sum, array[i]);
}
} else {
float gout[n];
size = sizeof(float) * n;
checkCudaErrors( cudaMemcpy(gout, array, size, cudaMemcpyDeviceToHost) );
for (i = 0; i < n; i++) {
sum = logadd(sum, gout[i]);
}
}
} else {
num_blocks = n / NUM_THREADS;
remains = n - num_blocks * NUM_THREADS;
dim3 dimBlock(NUM_THREADS);
dim3 dimGrid(num_blocks);
if (! indevice) {
size = sizeof(float) * num_blocks * NUM_THREADS;
checkCudaErrors( cudaMalloc((void**) &gin, size) );
checkCudaErrors( cudaMemcpy(gin, array, size, cudaMemcpyHostToDevice) );
logreduce2<<<dimGrid, dimBlock>>>(gin, gin);
sum = logadd(sum, logsumf(gin, num_blocks, IN_DEVICE));
if (remains > 0)
sum = logadd(sum, logsumf(array + num_blocks * NUM_THREADS, remains, IN_HOST));
checkCudaErrors( cudaFree(gin) );
} else {
logreduce2<<<dimGrid, dimBlock>>>(gin, gin);
sum = logadd(sum, logsumf(gin, num_blocks, IN_DEVICE));
if (remains > 0)
sum = logadd(sum, logsumf(gin + num_blocks * NUM_THREADS, remains, IN_DEVICE));
}
}
return sum;
}
/* initilize counts */
void init_count() {
int size;
size_t i;
float pi[nstates];
float gmm[nstates * nobvs];
float xi[nstates * nstates];
for (i = 0; i < nstates * nobvs; i++)
gmm[i] = - INFINITY;
for (i = 0; i < nstates * nstates; i++)
xi[i] = - INFINITY;
for (i = 0; i < nstates; i++)
pi[i] = - INFINITY;
size = sizeof(float) * nstates * nstates;
checkCudaErrors( cudaMalloc((void**)&xid, size) );
checkCudaErrors( cudaMemcpy(xid, xi, size, cudaMemcpyHostToDevice) );
size = sizeof(float) * nstates * nobvs;
checkCudaErrors( cudaMalloc((void**)&gmmd, size) );
checkCudaErrors( cudaMemcpy(gmmd, gmm, size, cudaMemcpyHostToDevice) );
size = sizeof(float) * nstates;
checkCudaErrors( cudaMalloc((void**)&pid, size) );
checkCudaErrors( cudaMemcpy(pid, pi, size, cudaMemcpyHostToDevice) );
}
/* add up two logarithm while avoiding overflow */
float logadd(float x, float y) {
if (y <= x)
return x + log1pf(expf(y - x));
else
return y + log1pf(expf(x - y));
}
/* add up two logarithm while avoiding overflow (device version) */
__device__ float logaddd(float x, float y) {
if (y <= x)
return x + log1pf(expf(y - x));
else
return y + log1pf(expf(x - y));
}
/* the kernel function for stepfwd */
__global__ void
stepfwdd(float *pre, float *transd, int *O, float *obvsd,
int nstates, int nobvs, float *A)
{
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int aBegin = nstates * BLOCK_SIZE * by;
int aEnd = aBegin + nstates - 1;
int aStep = BLOCK_SIZE;
int bBegin = BLOCK_SIZE * bx;
int bStep = BLOCK_SIZE * nstates;
float sub = logf(0);
int a, b, k;
for (a = aBegin, b = bBegin;
a <= aEnd;
a += aStep, b += bStep) {
__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
__shared__ float Os[BLOCK_SIZE][BLOCK_SIZE];
As[ty][tx] = pre[a + nstates * ty + tx];
Bs[ty][tx] = transd[b + nstates * ty + tx];
Os[ty][tx] = obvsd[IDX(BLOCK_SIZE * bx + tx, O[BLOCK_SIZE * by + ty], nobvs)];
__syncthreads();
for (k = 0; k < BLOCK_SIZE; ++k) {
sub = logaddd(sub, As[ty][k] + Bs[k][tx] + Os[ty][tx]);
}
__syncthreads();
}
a = nstates * BLOCK_SIZE * by + BLOCK_SIZE * bx;
A[a + nstates * ty + tx] = sub;
}
/* compute one step (n-th) forward probability. the data partition
follows matrix muptiplication A x B, where the previous forward
probabilities are A and state transition probabilities are B. */
void stepfwd(float *alpha, size_t n)
{
int size;
int *Od;
float *A;
float *Ad;
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 dimGrid(nstates / dimBlock.x, nseq / dimBlock.y);
size = sizeof(int) * nseq;
checkCudaErrors( cudaMalloc((void**)&Od, size) );
checkCudaErrors( cudaMemcpy(Od, data + n * nseq, size, cudaMemcpyHostToDevice) );
size = sizeof(float) * nstates * nseq;
checkCudaErrors( cudaMalloc((void**)&A, size) );
checkCudaErrors( cudaMemcpy(A, alpha + (n - 1) * nseq * nstates, size, cudaMemcpyHostToDevice) );
checkCudaErrors( cudaMalloc((void**)&Ad, size) );
#ifdef PROFILE_GPU
sdkCreateTimer( &gpu_timer );
sdkStartTimer(&gpu_timer);
#endif
stepfwdd<<<dimGrid, dimBlock>>>(A, transd, Od, obvsd, nstates, nobvs, Ad);
#ifdef PROFILE_GPU
checkCudaErrors( cudaThreadSynchronize() );
sdkStopTimer(&gpu_timer);
gpu_time += sdkGetTimerValue(&gpu_timer);
sdkDeleteTimer( &gpu_timer);
gpu_flop += 1e-6 * ((double)nstates) * ((double)nseq) * ((double)nstates) * 7;
#endif
checkCudaErrors( cudaMemcpy(alpha + n * nseq * nstates, Ad, size, cudaMemcpyDeviceToHost) );
checkCudaErrors( cudaFree(Od) );
checkCudaErrors( cudaFree(A) );
checkCudaErrors( cudaFree(Ad) );
}
/* init first slice of forwad probability matrix. */
void initfwd0(float *alpha)
{
size_t i, j;
for (i = 0; i < nseq; i++) {
for (j = 0; j < nstates; j++) {
alpha[IDX(i, j, nstates)] = prior[j] + obvs[IDX(j, data[i], nobvs)];
}
}
}
__global__ void
initfwdd(float *prior, int *Od, float *obvs,
int nstates, int nobvs, float *Ad)
{
int y = blockIdx.y * BLOCK_SIZE + threadIdx.y;
int x = blockIdx.x * BLOCK_SIZE + threadIdx.x;
Ad[IDX(y, x, nstates)] = prior[x] + obvs[IDX(x, Od[y], nobvs)];
}
void initfwd(float *alpha)
{
int size;
int *Od;
float *Ad;
float *priord;
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 dimGrid(nstates / dimBlock.x, nseq / dimBlock.y);
size = sizeof(float) * nstates;
checkCudaErrors( cudaMalloc((void**)&priord, size) );
checkCudaErrors( cudaMemcpy(priord, prior, size, cudaMemcpyHostToDevice) );
size = sizeof(int) * nseq;
checkCudaErrors( cudaMalloc((void**)&Od, size) );
checkCudaErrors( cudaMemcpy(Od, data, size, cudaMemcpyHostToDevice) );
size = sizeof(float) * nstates * nseq;
checkCudaErrors( cudaMalloc((void**)&Ad, size) );
#ifdef PROFILE_GPU
sdkCreateTimer( &gpu_timer );
sdkStartTimer(&gpu_timer);
#endif
initfwdd<<<dimGrid, dimBlock>>>(priord, Od, obvsd, nstates, nobvs, Ad);
#ifdef PROFILE_GPU
checkCudaErrors( cudaThreadSynchronize() );
sdkStopTimer(&gpu_timer);
gpu_time += sdkGetTimerValue(&gpu_timer);
sdkDeleteTimer( &gpu_timer);
gpu_flop += 1e-6 * ((double)nstates) * ((double)nseq) * 1.0;
#endif
checkCudaErrors( cudaMemcpy(alpha, Ad, size, cudaMemcpyDeviceToHost) );
checkCudaErrors( cudaFree(Ad) );
checkCudaErrors( cudaFree(priord) );
}
/* kernel function for initbck() */
__global__ void
initbckd(float *B, int nstates)
{
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
size_t i = bx * BLOCK_SIZE + tx;
size_t j = by * BLOCK_SIZE + ty;
B[IDX(j, i, nstates)] = 0;
}
/* initial backward probabilities for the last slice. data partition
is the same as initfwd(). the values are all initilized to be 0. */
void initbck(float *beta)
{
int size;
float *Bd;
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 dimGrid(nstates / dimBlock.x, nseq / dimBlock.y);
size = sizeof(float) * nstates * nseq;
checkCudaErrors( cudaMalloc((void**)&Bd, size) );
initbckd<<<dimGrid, dimBlock>>>(Bd, nstates);
checkCudaErrors( cudaMemcpy(beta, Bd, size, cudaMemcpyDeviceToHost) );
checkCudaErrors( cudaFree(Bd) );
}
/* kernel function for updating xi counts */
__global__ void
updatexid(float *A, float *B, float *transd, int *O, float *obvsd,
int nstates, int nobvs, int nseq, float* loglik, float *xid)
{
int x = blockIdx.x * BLOCK_SIZE + threadIdx.x;
int y = blockIdx.y * BLOCK_SIZE + threadIdx.y;
float e;
int i;
for (i = 0; i < nseq; i++) {
e = A[IDX(i, y, nstates)] + transd[IDX(y, x, nstates)]
+ B[IDX(i, x, nstates)] + obvsd[IDX(x, O[i], nobvs)] - loglik[i];
xid[IDX(y, x, nstates)] = logaddd(xid[IDX(y, x, nstates)], e);
}
}
/* kernel function for updating gamma counts */
__global__ void
updategmmd(float *A, float *B, int *O, int nstates,
int nobvs, int nseq, float *loglik, float *gmmd)
{
int x = blockIdx.x * BLOCK_SIZE + threadIdx.x;
float e;
int i = 0;
for (i = 0; i < nseq; i++) {
e = A[IDX(i, x, nstates)] + B[IDX(i, x, nstates)] - loglik[i];
gmmd[IDX(x, O[i], nobvs)] = logaddd(gmmd[IDX(x, O[i], nobvs)], e);
}
}
/* kernel function for updateing pi counts */
__global__ void
updatepid(float *A, float *B, int *O, int nstates,
int nseq, float *loglik, float *pid)
{
int x = blockIdx.x * BLOCK_SIZE + threadIdx.x;
float e;
int i = 0;
for (i = 0; i < nseq; i++) {
e = A[IDX(i, x, nstates)] + B[IDX(i, x, nstates)] - loglik[i];
pid[x] = logaddd(pid[x], e);
}
}
/* kernel function for stepbck() */
__global__ void
stepbckd(float *pre, float *transd, int *O, float *obvsd,
int nstates, int nobvs, float *B)
{
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int aBegin = nstates * BLOCK_SIZE * by;
int aEnd = aBegin + nstates - 1;
int aStep = BLOCK_SIZE;
int bBegin = nstates * BLOCK_SIZE * bx;
int bStep = BLOCK_SIZE;
float sub = logf(0);
int a, b, k;
for (a = aBegin, b = bBegin;
a <= aEnd;
a += aStep, b+= bStep) {
__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
As[ty][tx] = pre[a + IDX(ty, tx, nstates)] +
obvsd[IDX(a - aBegin + tx, O[by * BLOCK_SIZE + ty], nobvs)];
Bs[ty][tx] = transd[b + IDX(tx, ty, nstates)];
__syncthreads();
for (k = 0; k < BLOCK_SIZE; ++k) {
sub = logaddd(sub, As[ty][k] + Bs[k][tx]);
}
__syncthreads();
}
b = nstates * BLOCK_SIZE * by + BLOCK_SIZE * bx;
B[b + nstates * ty + tx] = sub;
}
/* compute one step backward probability and update counts.
data partition for computing backward probilities follows forward
pass. the result of the backward probabities are stored in the
memory pointed by *beta. */
void stepbck(float *alpha, float *pre, size_t n, float* loglik, float *beta)
{
int size;
int *Od;
float *Bd;
float *pred;
float *Ad;
float *loglikd;
dim3 xiBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 xiGrid(nstates / xiBlock.x, nstates / xiBlock.y);
dim3 gmmBlock(BLOCK_SIZE);
dim3 gmmGrid(nstates / gmmBlock.x);
dim3 sBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 sGrid(nstates / sBlock.x, nseq / sBlock.y);
size = sizeof(int) * nseq;
checkCudaErrors( cudaMalloc((void**)&Od, size) );
checkCudaErrors( cudaMemcpy(Od, data + (n + 1) * nseq,
size, cudaMemcpyHostToDevice) );
checkCudaErrors( cudaMalloc((void**)&loglikd, size) );
checkCudaErrors( cudaMemcpy(loglikd, loglik, size, cudaMemcpyHostToDevice) );
size = sizeof(float) * nstates * nseq;
checkCudaErrors( cudaMalloc((void**)&pred, size) );
checkCudaErrors( cudaMemcpy(pred, pre, size, cudaMemcpyHostToDevice) );
checkCudaErrors( cudaMalloc((void**)&Ad, size) );
checkCudaErrors( cudaMemcpy(Ad, alpha + n * nseq * nstates,
size, cudaMemcpyHostToDevice) );
/* update counts */
#ifdef PROFILE_GPU
sdkCreateTimer( &gpu_timer );
sdkStartTimer(&gpu_timer);
#endif
updatexid<<<xiGrid, xiBlock>>>(Ad, pred, transd, Od, obvsd,
nstates, nobvs, nseq, loglikd, xid);
#ifdef PROFILE_GPU
checkCudaErrors( cudaThreadSynchronize() );
sdkStopTimer(&gpu_timer);
gpu_time += sdkGetTimerValue(&gpu_timer);
sdkDeleteTimer( &gpu_timer);
gpu_flop += 1e-6 * ((double) nstates) * ((double) nstates) * ((double) nseq) * 9.0;
#endif
checkCudaErrors( cudaMemcpy(Ad, alpha + (n + 1) * nseq * nstates,
size, cudaMemcpyHostToDevice) );
#ifdef PROFILE_GPU
sdkCreateTimer( &gpu_timer );
sdkStartTimer(&gpu_timer);
#endif
updategmmd<<<gmmGrid, gmmBlock>>>(Ad, pred, Od, nstates, nobvs, nseq, loglikd, gmmd);
#ifdef PROFILE_GPU
checkCudaErrors( cudaThreadSynchronize() );
sdkStopTimer(&gpu_timer);
gpu_time += sdkGetTimerValue(&gpu_timer);
sdkDeleteTimer( &gpu_timer);
gpu_flop += 1e-6 * ((double) nstates) * ((double) nseq) * 7.0;
#endif
/* compute one step beta probabilities */
checkCudaErrors( cudaMalloc((void**)&Bd, size) );
#ifdef PROFILE_GPU
sdkCreateTimer( &gpu_timer );
sdkStartTimer(&gpu_timer);
#endif
stepbckd<<<sGrid, sBlock>>>(pred, transd, Od, obvsd, nstates, nobvs, Bd);
#ifdef PROFILE_GPU
checkCudaErrors( cudaThreadSynchronize() );
sdkStopTimer(&gpu_timer);
gpu_time += sdkGetTimerValue(&gpu_timer);
sdkDeleteTimer( &gpu_timer);
gpu_flop += 1e-6 * ((double) nstates) * ((double) nseq) * ((double) nstates) * 6.0;
#endif
checkCudaErrors( cudaMemcpy(beta, Bd, size, cudaMemcpyDeviceToHost) );
checkCudaErrors( cudaFree(Ad) );
checkCudaErrors( cudaFree(Od) );
checkCudaErrors( cudaFree(Bd) );
checkCudaErrors( cudaFree(pred) );
checkCudaErrors( cudaFree(loglikd) );
}
void last_update(float *alpha, float *beta, float *loglik)
{
int size;
int *Od;
float *Bd, *Ad, *loglikd;
dim3 gmmBlock(BLOCK_SIZE);
dim3 gmmGrid(nstates / gmmBlock.x);
dim3 piBlock(BLOCK_SIZE);
dim3 piGrid(nstates / piBlock.x);
size = sizeof(int) * nseq;
checkCudaErrors( cudaMalloc((void**)&Od, size) );
checkCudaErrors( cudaMemcpy(Od, data, size, cudaMemcpyHostToDevice) );
checkCudaErrors( cudaMalloc((void**)&loglikd, size) );
checkCudaErrors( cudaMemcpy(loglikd, loglik, size, cudaMemcpyHostToDevice) );
size = sizeof(float) * nstates * nseq;
checkCudaErrors( cudaMalloc((void**)&Bd, size) );
checkCudaErrors( cudaMalloc((void**)&Ad, size) );
checkCudaErrors( cudaMemcpy(Bd, beta, size, cudaMemcpyHostToDevice) );
checkCudaErrors( cudaMemcpy(Ad, alpha, size, cudaMemcpyHostToDevice) );
#ifdef PROFILE_GPU
sdkCreateTimer( &gpu_timer );
sdkStartTimer(&gpu_timer);
#endif
updategmmd<<<gmmGrid, gmmBlock>>>(Ad, Bd, Od, nstates, nobvs, nseq, loglikd, gmmd);
updatepid<<<piGrid, piBlock>>>(Ad, Bd, Od, nstates, nseq, loglikd, pid);
#ifdef PROFILE_GPU
checkCudaErrors( cudaThreadSynchronize() );
sdkStopTimer(&gpu_timer);
gpu_time += sdkGetTimerValue(&gpu_timer);
sdkDeleteTimer( &gpu_timer);
gpu_flop += 1e-6 * ((double)nstates) * ((double)nseq) * 7.0;
gpu_flop += 1e-6 * ((double)nstates) * ((double)nseq) * 7.0;
#endif
checkCudaErrors( cudaFree(Ad) );
checkCudaErrors( cudaFree(Bd) );
checkCudaErrors( cudaFree(Od) );
checkCudaErrors( cudaFree(loglikd) );
}
/* forwad backward algorithm: running on all sequences in parallel */
float forward_backward(int backward)
{
float *alpha = NULL;
float *beta = NULL;
float *prebeta = NULL;
size_t i;
size_t a;