def generate_matrix(experiments, mem, baseline, replace, directory, fast):
assert(mem == 'best')
db = database.get_instance()
for mem_model in experiments:
mod = model.parse_model_file(mem_model)
model_ml = get_best(db, mod)
for experiment in experiments:
mod = model.parse_model_file(experiment)
time = db.get_result(mod, model_ml)
if not time:
time, cost = evaluate(mod, model_ml, directory, fast)
db.add_result(mod, model_ml, time, cost)
print(get_experiment_name(experiment) + ',' +
get_experiment_name(mem_model) + ',' + str(time))
def main():
options, args = parser.parse_args()
if len(args) < 1:
print('ERROR: no model file specified')
sys.exit(-1)
elif len(args) > 1:
print('ERROR: too many model files specified')
sys.exit(-1)
m = model.parse_model_file(args[0])
if options.best:
db = database.get_instance(options.url)
best, value, cost = db.get_best(m)
if best is None:
print('ERROR: Model not found')
sys.exit(-1)
m.memory = memlist.parse_memory_list(lex.Lexer(StringIO(best)))
print('-- Cost: {}, value: {}'.format(cost, value))
name = '-- ' + str(m).replace(') (', ')\n -- (')
name = name.replace(')(benchmarks ', ')\n-- (benchmarks ')
print(name)
for b in m.benchmarks:
mem = m.memory.get_subsystem(b.index)
if mem.depth < 0:
word_size = mem.get_word_size()
total_size = b.get_size(options.directory)
mem.depth = total_size // word_size
assert(mem.depth >= 0)
gen = vhdl.VHDLGenerator(m.machine)
result = gen.generate(m.memory)
print(result)
def main():
global directory, mach
options, args = parser.parse_args()
experiments = args if args else None
if not database.get_instance(options.url):
print("ERROR: could not connect to the database")
sys.exit(-1)
directory = options.directory
if len(args) > 0:
m = model.parse_model_file(args[0])
mach = m.machine
else:
mach = machine.MachineType()
max_size = get_max_size()
line_count = util.round_power2(max_size // (mach.word_size * 8))
while line_count >= 128:
line_size = util.round_power2(max_size // 8)
while line_size >= mach.word_size:
associativity = min(line_count, 8)
while associativity >= 1:
for policy in get_policies(associativity):
generate_cache(line_count, line_size, associativity, policy, True, experiments)
generate_cache(line_count, line_size, associativity, policy, False, experiments)
associativity //= 2
line_size //= 2
line_count //= 2
print("Total:", total)
print("Best Cost: ", best_cost)
print("Best Memory:", best_name)
def simulate(experiment, mem, baseline, replace, directory, fast):
mod = model.parse_model_file(experiment)
db = database.get_instance()
ml = get_memory_list(db, mem, mod, baseline, replace)
time = db.get_result(mod, ml)
if time is None:
time, cost = evaluate(mod, ml, directory, fast)
db.add_result(mod, ml, time, cost)
print(get_experiment_name(experiment) + ',' + str(time))
def get_stats(experiments, mem, baseline, replace, directory):
db = database.get_instance()
for experiment in experiments:
mod = model.parse_model_file(experiment)
ml = get_memory_list(db, mem, mod, baseline, replace)
ml.reset(mod.machine)
pl = ml.get_max_path_length()
name = get_experiment_name(experiment)
cost = ml.get_cost()
print('{},{},{}'.format(name, pl, cost))
def get_frequency(experiment, mem, baseline, keep):
m = model.parse_model_file(experiment)
db = database.get_instance()
if mem == 'model':
subsystem = m.memory
elif mem == 'baseline':
with open(baseline, 'r') as f:
subsystem = memory.parse_memory_list(lex.Lexer(f))
elif mem == 'best':
best_name, _, _ = db.get_best(m)
best_file = StringIO.StringIO(best_name)
subsystem = memory.parse_memory_list(lex.Lexer(best_file))
else:
print('ERROR: invalid memory selected:', mem)
sys.exit(-1)
m.machine.frequency = 1 << 31
result = xilinx.run_xilinx(m.machine, subsystem, keep)
print(get_experiment_name(experiment) + ',' +
str(result.frequency) + ',' +
str(result.bram_count))
def start_experiment(context):
seed = context.seed
server = context.server
iterations = context.iterations
directory = context.directory
experiments = context.experiments
# Determine the experiment to run and update the seed.
context.seed = (seed + 1) % (2 << 31)
experiment_count = len(experiments)
experiment = experiments[seed % experiment_count]
# Load the model and its current state.
m = model.parse_model_file(experiment)
if not m:
print('ERROR: could not read model:', experiment)
return False
# Only start the thread if there is work to do.
if server.db.get_result_count(m) >= iterations:
return False
# Create a shared database instance.
name = os.path.basename(experiment)
db = server.add_client(name)
print('Starting {}'.format(name))
# Start the thread.
args = {
'db': db,
'iterations': iterations,
'mod': m,
'directory': directory,
'seed': seed,
}
pool = main_context.pool
main_context.thread_count += 1
pool.apply_async(run_experiment, kwds=args, callback=experiment_done)
return True
def run_simulation(mem, experiment):
print(" Running", experiment)
m = model.parse_model_file(experiment)
if m.machine.target != mach.target:
print("ERROR: wrong target for", experiment)
sys.exit(-1)
if m.machine.frequency != mach.frequency:
print("ERROR: wrong frequency for", experiment)
sys.exit(-1)
if m.machine.technology != mach.technology:
print("ERROR: wrong technology for", experiment)
sys.exit(-1)
if m.machine.max_path_length != mach.max_path_length:
print("ERROR: wrong max path length for", experiment)
sys.exit(-1)
if m.machine.part != mach.part:
print("ERROR: wrong part for", experiment)
sys.exit(-1)
if m.machine.word_size != mach.word_size:
print("ERROR: wrong word size for", experiment)
sys.exit(-1)
if m.machine.addr_bits != mach.addr_bits:
print("ERROR: wrong addr bits for", experiment)
sys.exit(-1)
if m.machine.max_cost != mach.max_cost:
print("ERROR: wrong max cost for", experiment)
sys.exit(-1)
mem.set_main(m.memory)
db = database.get_instance()
result = db.get_result(m, mem)
if result is None:
ml = memory.MemoryList(m.memory)
ml.add_memory(mem)
result, cost = evaluate(m, ml, directory)
db.add_result(m, mem, result, cost)
return result
