def runApproxHeuristic(dataset, seeds_fname, tau_scale, cores, init_samples = -1, iter_samples = -1):
res_fname = "res" + str(randint(1, 1000))
perf_fname = "perf" + str(randint(1, 1000))
init_tau = -1
num_cycles_approx = 0
subprocess.Popen("perf stat -x, -o %s python load_link_server.py -cp %s"%\
(perf_fname, dataset), shell = True, stdout = subprocess.PIPE).stdout.read()
nCycles_link_server = getNumCycles(perf_fname)
removeFile(perf_fname)
if init_samples > 0:
subprocess.Popen("perf stat -x, -o %s python seq_estimation.py -dataset %s -seeds_file %s -results_file %s -output_mode 1 -nSamples %d -cores %d"%\
(perf_fname, dataset, seeds_fname, res_fname, init_samples, cores),\
shell=True,stdout=subprocess.PIPE).stdout.read()
init_tau = float(open(res_fname, 'r').readline().strip()) / (1 - tau_scale)
print "Initial tau value set to: ", init_tau
num_cycles_approx = getNumCycles(perf_fname) - nCycles_link_server
removeFile(perf_fname)
removeFile(res_fname)
subprocess.Popen("perf stat -x, -o %s python ic_bfs_eval.py -tau_scale %.3f -dataset %s -res_fname %s -seeds %s -output_mode 2 -cores %d -init_tau %.3f -iter_samples %d"%\
(perf_fname, tau_scale, dataset, res_fname, seeds_fname, cores, init_tau, iter_samples), \
shell = True, stdout = subprocess.PIPE).stdout.read()
estimate_approx = float(open(res_fname, 'r').readline().strip())
print "Estimated value: ", estimate_approx
num_cycles_approx += getNumCycles(perf_fname) - nCycles_link_server
removeFile(res_fname)
removeFile(perf_fname)
return (estimate_approx, num_cycles_approx)
def runApproxHeuristic(dataset,
seeds_fname,
tau_scale,
cores,
init_samples=-1,
iter_samples=-1):
res_fname = "res" + str(randint(1, 1000))
perf_fname = "perf" + str(randint(1, 1000))
init_tau = -1
num_cycles_approx = 0
subprocess.Popen("perf stat -x, -o %s python load_link_server.py -cp %s"%\
(perf_fname, dataset), shell = True, stdout = subprocess.PIPE).stdout.read()
nCycles_link_server = getNumCycles(perf_fname)
removeFile(perf_fname)
if init_samples > 0:
subprocess.Popen("perf stat -x, -o %s python seq_estimation.py -dataset %s -seeds_file %s -results_file %s -output_mode 1 -nSamples %d -cores %d"%\
(perf_fname, dataset, seeds_fname, res_fname, init_samples, cores),\
shell=True,stdout=subprocess.PIPE).stdout.read()
init_tau = float(open(res_fname,
'r').readline().strip()) / (1 - tau_scale)
print "Initial tau value set to: ", init_tau
num_cycles_approx = getNumCycles(perf_fname) - nCycles_link_server
removeFile(perf_fname)
removeFile(res_fname)
subprocess.Popen("perf stat -x, -o %s python ic_bfs_eval.py -tau_scale %.3f -dataset %s -res_fname %s -seeds %s -output_mode 2 -cores %d -init_tau %.3f -iter_samples %d"%\
(perf_fname, tau_scale, dataset, res_fname, seeds_fname, cores, init_tau, iter_samples), \
shell = True, stdout = subprocess.PIPE).stdout.read()
estimate_approx = float(open(res_fname, 'r').readline().strip())
print "Estimated value: ", estimate_approx
num_cycles_approx += getNumCycles(perf_fname) - nCycles_link_server
removeFile(res_fname)
removeFile(perf_fname)
return (estimate_approx, num_cycles_approx)
def runVanilla(dataset,
seeds,
nSamples=0,
nCores=-1,
min_samples=-1,
min_relative_std_error=-1):
perf_fname = "perf" + str(randint(1, 1000))
res_fname = "res" + str(randint(1, 1000))
subprocess.Popen("perf stat -x, -o %s python load_link_server.py -cp %s"%\
(perf_fname, dataset), shell = True, stdout = subprocess.PIPE).stdout.read()
nCycles_link_server = getNumCycles(perf_fname)
removeFile(perf_fname)
print "running vanilla with max_samples == %d, min_samples == %d, and min_relative_std_error = %.8f" % (
nSamples, min_samples, min_relative_std_error)
subprocess.Popen("perf stat -x, -o %s python seq_estimation.py -dataset %s -seeds_file %s -results_file %s -output_mode 1 -nSamples %d -cores %d -min_samples %d -min_relative_standard_error %.8f"%\
(perf_fname, dataset, seeds, res_fname, nSamples, nCores, min_samples, min_relative_std_error),\
shell=True,stdout=subprocess.PIPE).stdout.read()
num_cycles_seq = getNumCycles(perf_fname)
print "dataset: ", dataset
print "res_fname: ", res_fname
print "dataset: ", dataset
print "seeds file: ", seeds
estimate_seq_capped = float(open(res_fname, 'r').readline().strip())
removeFile(perf_fname)
removeFile(res_fname)
return estimate_seq_capped, num_cycles_seq
def runVanilla(dataset, seeds, nSamples = 0, nCores = -1, min_samples = -1, min_relative_std_error = -1):
perf_fname = "perf" + str(randint(1,1000))
res_fname = "res" + str(randint(1,1000))
subprocess.Popen("perf stat -x, -o %s python load_link_server.py -cp %s"%\
(perf_fname, dataset), shell = True, stdout = subprocess.PIPE).stdout.read()
nCycles_link_server = getNumCycles(perf_fname)
removeFile(perf_fname)
print "running vanilla with max_samples == %d, min_samples == %d, and min_relative_std_error = %.8f"%(nSamples, min_samples, min_relative_std_error)
subprocess.Popen("perf stat -x, -o %s python seq_estimation.py -dataset %s -seeds_file %s -results_file %s -output_mode 1 -nSamples %d -cores %d -min_samples %d -min_relative_standard_error %.8f"%\
(perf_fname, dataset, seeds, res_fname, nSamples, nCores, min_samples, min_relative_std_error),\
shell=True,stdout=subprocess.PIPE).stdout.read()
num_cycles_seq = getNumCycles(perf_fname)
print "dataset: ", dataset
print "res_fname: ", res_fname
print "dataset: ", dataset
print "seeds file: ", seeds
estimate_seq_capped = float(open(res_fname, 'r').readline().strip())
removeFile(perf_fname)
removeFile(res_fname)
return estimate_seq_capped, num_cycles_seq
f_output = open(output_file,'a')
f_output.write(text+'\n')
f_output.close()
if __name__ == "__main__":
parameters = parser.parse_args()
dataset, seeds_file, results_file, output_mode, nSamples = parameters.dataset, parameters.seeds_file, parameters.results_file,\
parameters.output_mode, parameters.nSamples
L = LinkServerCP(dataset)
f = open(seeds_file, 'r')
assert (nSamples <= 0 or parameters.min_relative_standard_error <= 0)
seeds_list = cp.load(f)
f.close()
removeFile(parameters.reached_nodes_file)
if output_mode in [1,2]:
f = open(results_file, 'w')
for i, seed_set in enumerate(seeds_list):
if output_mode in [0,2]:
print "Sample ", i
if parameters.get_n_reached == 1:
try:
avg, total_samples,l_n_reached = sequential_estimation(L, seed_set, max_samples_cap=nSamples, \
nCores=parameters.cores,bReturnValues = True,\
min_samples = parameters.min_samples, \
min_relative_standard_error = parameters.min_relative_standard_error)
except:
r = open('test.txt','w')
r.write('cannot run bfs\n')
r.close()
f_output.write(text + '\n')
f_output.close()
if __name__ == "__main__":
parameters = parser.parse_args()
dataset, seeds_file, results_file, output_mode, nSamples = parameters.dataset, parameters.seeds_file, parameters.results_file,\
parameters.output_mode, parameters.nSamples
L = LinkServerCP(dataset)
f = open(seeds_file, 'r')
assert (nSamples <= 0 or parameters.min_relative_standard_error <= 0)
seeds_list = cp.load(f)
f.close()
removeFile(parameters.reached_nodes_file)
if output_mode in [1, 2]:
f = open(results_file, 'w')
for i, seed_set in enumerate(seeds_list):
if output_mode in [0, 2]:
print "Sample ", i
if parameters.get_n_reached == 1:
try:
avg, total_samples,l_n_reached = sequential_estimation(L, seed_set, max_samples_cap=nSamples, \
nCores=parameters.cores,bReturnValues = True,\
min_samples = parameters.min_samples, \
min_relative_standard_error = parameters.min_relative_standard_error)
except:
r = open('test.txt', 'w')
r.write('cannot run bfs\n')
r.close()
