def in_test_single_pgm(N=1):
test_len = [12]
fout = open("report_in_12.txt", "w")
fout.write("Benchmark | Cycle Counts | Algorithm Runtime (s)| Sample Sizes | Passes \n")
from gym_hls.envs.chstone_bm import get_all9
bms = get_all9()
for bm in bms:
for length in test_len:
envs = []
i = 0
pgm, path = bm
print("Program: {}".format(pgm))
env_config = {
'pgm':pgm,
'pgm_dir':path,
'run_dir':'run_'+pgm.replace(".c",""),
'normalize':False,
'orig_and_normalize':False,
'log_obs_reward':False,
'verbose':False,
}
envs.append(Env(env_config))
i = i+1
begin = time.time()
(passes, timings, sample_size) = runInsertionN(envs, N, length=length, sort=True)
end = time.time()
print("Best individuals are: {}".format(passes[0]))
print("Cycles: {}".format(timings[0]))
compile_time =end - begin
print("Compile Time: %d"%(int(compile_time)))
fout.write("{}|{}|{}|{}|{}".format(pgm, timings[0], compile_time, sample_size, passes[0]))
def parse_log(algo="a3c"):
from gym_hls.envs.chstone_bm import get_chstone, get_others, get_all9
bms = get_all9()
fout = open("report_" + algo + ".txt", "w")
for i, bm in enumerate(bms):
pgm, _ = bm
pgm = pgm.replace(".c", "")
#pgm = str(i)
min_cycle, sample_size, actual_sample_size, threads = get_min(
"run_{}_{}_".format(algo, pgm))
fout.write("{}|{}|{}|{}|{}\n".format(pgm, min_cycle, sample_size,
actual_sample_size, threads))
fout.close()
from __future__ import print_function
#import adddeps # fix sys.path
from gym_hls.envs.hls_env import HLSEnv
import pickle
if __name__ == '__main__':
from gym_hls.envs.chstone_bm import get_chstone, get_others, get_all9
bms = get_all9()
algo_pass = {}
#algo_pass['autotuner'] = [18, 7, 37, 31, 38, 17, 2, 6, 23, 21, 24, 41, 19, 20, 33, 11, 44, 26, 4, 34, 27, 32, 8, 22, 36]
#algo_pass['ga'] = [0, 1, 43, 1, 1, 0, 25, 1, 23, 16, 0, 0, 0, 33, 0, 18, 16, 0, 37, 0, 35, 0, 1, 0, 1, 14, 14, 0, 23, 1, 44, 0, 1, 33, 1, 1, 28, 0, 1, 0, 0, 0, 0, 1, 2]
#algo_pass['greedy'] = [23, 20, 34, 11, 33, 10, 32]
#algo_pass['greedy'] = [23, 20, 34, 11, 33, 10, 32, 9, 31, 8, 30, 7, 29, 6, 28, 5, 27]
# Train on random
#algo_pass['greedy'] = [10, 8, 15, 11, 6, 13, 10, 7, 11, 3, 13, 7, 7, 7, 7, 7]
#algo_pass['opentuner'] = [15, 14, 10, 8, 6, 5, 13, 7, 0]
#algo_pass['ga'] = [4, 1, 0, 8, 11, 1, 2, 15, 13, 11, 11, 2, 1, 4, 13, 1]
algo_pass['ga'] = [13, 9, 15, 10, 14, 7, 5, 14, 15, 6, 0, 11, 13, 4, 15, 4]
for algo, passes in algo_pass.items():
fout = open(algo + ".csv", "w")
for i, bm in enumerate(bms):
pgm, files = bm
env_configs = {}
env_configs['pgm'] = pgm
env_configs['pgm_dir'] = files