def check_time_do_mstep(N,K,D):
random_state = np.random.RandomState(123)
weights = random_state.rand(K)
weights /= weights.sum()
means = random_state.randn(K,D)
covars = random_state.rand(K,D)
X = random_state.randn(N,D)
gmm_cpu = cgmm.GMM(K,D)
gmm_cpu.set_weights(weights)
gmm_cpu.set_means(means)
gmm_cpu.set_covars(covars)
gmm_gpu = ggmm.GMM(K,D)
gmm_gpu.set_weights(weights)
gmm_gpu.set_means(means)
gmm_gpu.set_covars(covars)
X_gpu = ggmm.return_CUDAMatrix(X)
temp_gpu_mem = ggmm.TempGPUMem()
logprob_cpu, posterior_cpu = gmm_cpu.score_samples(X)
logprob_gpu, posterior_gpu = gmm_gpu.score_samples(X_gpu,temp_gpu_mem)
update_params = 'wmc'
min_covar = 1e-3
MAX_TRIALS = 10
MAX_TIME = 1.0
gpu_trials = 0
t0 = time.time()
for i in range(MAX_TRIALS):
gmm_gpu._do_mstep(X_gpu,posterior_gpu, update_params, min_covar)
gpu_time = time.time() - t0
gpu_trials += 1
if gpu_time > MAX_TIME:
break
avg_gpu_time = gpu_time/gpu_trials
t0 = time.time()
cpu_trials = 0
for i in range(MAX_TRIALS):
gmm_cpu._do_mstep(X,posterior_cpu, update_params, min_covar)
cpu_time = time.time() - t0
cpu_trials += 1
if cpu_time > MAX_TIME:
break
avg_cpu_time = cpu_time/cpu_trials
speedup = avg_cpu_time/avg_gpu_time
print('')
print('------------------------------------')
print('N=%u, K=%u, D=%u' % (N,K,D))
print('avg_cpu_time (%u trials):' % cpu_trials, avg_cpu_time)
print('avg_gpu_time (%u trials):' % gpu_trials, avg_gpu_time)
print('speedup:', speedup)
print('------------------------------------')
print('')
assert_greater(speedup, 1.0)
def check_time_score_samples(N, K, D):
random_state = np.random.RandomState(123)
weights = random_state.rand(K)
weights /= weights.sum()
means = random_state.randn(K, D)
covars = random_state.rand(K, D)
X = random_state.randn(N, D)
gmm_cpu = cgmm.GMM(K, D)
gmm_cpu.set_weights(weights)
gmm_cpu.set_means(means)
gmm_cpu.set_covars(covars)
gmm_gpu = ggmm.GMM(K, D)
gmm_gpu.set_weights(weights)
gmm_gpu.set_means(means)
gmm_gpu.set_covars(covars)
MAX_TRIALS = 10
MAX_TIME = 1.0
temp_gpu_mem = ggmm.TempGPUMem()
X_gpu = ggmm.return_CUDAMatrix(X)
gpu_trials = 0
t0 = time.time()
for i in xrange(MAX_TRIALS):
logprob_gpu, posterior_gpu = gmm_gpu.score_samples(X_gpu, temp_gpu_mem)
gpu_time = time.time() - t0
gpu_trials += 1
if gpu_time > MAX_TIME:
break
avg_gpu_time = gpu_time / gpu_trials
t0 = time.time()
cpu_trials = 0
for i in xrange(MAX_TRIALS):
logprob_cpu, posterior_cpu = gmm_cpu.score_samples(X)
cpu_time = time.time() - t0
cpu_trials += 1
if cpu_time > MAX_TIME:
break
avg_cpu_time = cpu_time / cpu_trials
speedup = avg_cpu_time / avg_gpu_time
print ''
print '------------------------------------'
print 'N=%u, K=%u, D=%u' % (N, K, D)
print 'avg_cpu_time (%u trials):' % cpu_trials, avg_cpu_time
print 'avg_gpu_time (%u trials):' % gpu_trials, avg_gpu_time
print 'speedup:', speedup
print '------------------------------------'
print ''
assert_greater(speedup, 1.0)