i1 = ref_ip[0].reshape([dataset, -1])
i2 = ref_ip[1].reshape([dataset, -1])
o_pred = ref_op[0].reshape([dataset, -1])
o_act = i1.dot(i2)
print o_pred
print o_act
return o_pred == o_act
if __name__ == "__main__":
args = parse_arg(sys.argv[1:])
src_name = args.file.split("/")[-1]
s_name = src_name[:-5] #name of Kernel
if args.log:
logging.basicConfig(level=logging.DEBUG)
cmd_qs, ctxs, gpus, cpus = fw.host_initialize(int(args.nGPU),
int(args.nCPU))
info_file = args.file
with open(info_file, "r") as f:
info = json.loads(f.read())
dataset = int(args.dataset_size)
st = time.time()
if args.dump_output_file:
fw.dump_output = True
if args.partition_class != None:
partition = int(args.partition_class)
kernel = fw.Kernel(info, dataset=dataset, partition=partition)
else:
kernel = fw.Kernel(info, dataset=dataset)
partition = info['partition']
return task
if __name__ == '__main__':
args = parse_arg(sys.argv[1:])
if args.recreate_dag:
fw.create_dag("./database/info/","./dag_info/dag_transformer/dag.graph","./dag_info/dag_transformer/t1.json"\
,partition=10)
num_chunks = int(args.num_chunks)
fw.just_for_testing_num_chunks = num_chunks
info_file = args.file
cmd_qs, ctxs, gpus, cpus = fw.host_initialize(int(args.nGPU), int(args.nCPU),use_mul_queues = True)
#Dags_folder = list()
all_dags = [] #list of all the DAGs
finished_task_Dag = dict()
deleted_task_dag = list()
all_dags_jsons = [join(info_file,f) for f in listdir(info_file)] #list of json files - each json file corresponds to a single DAG
gantt_label = [(info_file + f) for f in listdir(info_file)]
gantt = 0
# count = 0
# count1 = 0
# task_dag_id = 0
frontier_Q = fw.frontier_Q
if local_work_size > dataset:
continue
#logging.info("\t\tLocal Work Size :- "+str(local_work_size))
print "dataset size :- ", dataset
for partition in [0, 10]:
with open(info_file, "r") as f:
info = json.loads(f.read())
info['localWorkSize'] = [local_work_size
] * info['workDimension']
if local_work_size >= lims[info['workDimension'] - 1]:
break
elif info['workDimension'] == 3 and dataset > 256:
break
cmd_qs, ctxs, gpus, cpus = fw.host_initialize(1, 1)
if partition == 0:
dev = "CPU"
else:
dev = "GPU"
print dev
kernel = fw.Kernel(info, dataset=dataset, partition=partition)
kernel.build_kernel(gpus, cpus, ctxs, profile=True)
kernel.random_data()
ref_ip = []
ref_op = []
ref_iop = []
for i, ip in enumerate(kernel.data["input"]):
ref_ip.append(weakref.proxy(ip))
def select_main(kernels, select=baseline_select):
cmd_qs, ctxs, gpus, cpus = fw.host_initialize(4, 2)
aCPU, aGPU = fw.nCPU, fw.nGPU
cpu_q, gpu_q, mixed_q, mixedg_q, mixedc_q = [], [], [], [], []
num_dispatched = 0
num_kernels = len(kernels)
rCPU, rGPU = 0, 0
num_global_work_items_max = 0
for i in range(len(kernels)):
kernels[i].build_kernel(gpus, cpus, ctxs)
kernels[i].random_data()
p = kernels[i].partition
num_global_work_items = kernels[i].get_num_global_work_items()
if num_global_work_items > num_global_work_items_max:
num_global_work_items_max = num_global_work_items
if p == 0:
heapq.heappush(cpu_q, (p, -num_global_work_items, i))
rCPU += 1
elif p == 10:
heapq.heappush(gpu_q, (-p, -num_global_work_items, i))
rGPU += 1
elif p >= 5:
heapq.heappush(mixedg_q, (abs(p-5), -num_global_work_items, i))
heapq.heappush(mixed_q, (abs(p-5), -num_global_work_items, i))
rCPU += 1
rGPU += 1
else:
heapq.heappush(mixedc_q, (abs(p-5), -num_global_work_items, i))
heapq.heappush(mixed_q, (abs(p-5), -num_global_work_items, i))
rCPU +=1
rGPU +=1
logging.debug( "Task Queue Stats")
logging.debug(rCPU)
logging.debug(rGPU)
logging.debug( "CPU " + str(len(cpu_q)))
logging.debug( "GPU " + str(len(gpu_q)))
logging.debug( "Mixed " + str(len(mixed_q)))
logging.debug( "Mixed CPU " +str(len(mixedc_q)))
logging.debug( "Mixed GPU " +str(len(mixedg_q)))
events = []
now, soon = None, None
while (len(cpu_q) > 0 or len(gpu_q) > 0 or len(mixed_q) > 0 or num_dispatched != num_kernels) and (len(cpu_q) > 0 or len(gpu_q) > 0 or
len(mixedc_q) > 0 or len(mixedg_q) > 0 or num_dispatched != num_kernels):
logging.debug( "READY DEVICES")
logging.debug( "GPU " + str(len(fw.ready_queue['gpu'])))
logging.debug( "CPU " + str(len(fw.ready_queue['cpu'])))
logging.debug( "Number of tasks left")
logging.debug( "Mixed Queue " + str(len(mixed_q)))
logging.debug( "CPU Queue " + str(len(cpu_q)))
logging.debug( "GPU Queue " + str(len(gpu_q)))
logging.debug( "Mixed CPU " + str(len(mixedc_q)))
logging.debug( "Mixed GPU " + str(len(mixedg_q)))
logging.debug( "Number of available devices (CPU and GPU) " + str(fw.nCPU) + " " + str(fw.nGPU))
if fw.nCPU > 0 or fw.nGPU > 0:
logging.debug( "Entering selection phase")
if soon == None:
if select is baseline_select or select is look_ahead_select:
now, soon = select(kernels, M=mixed_q, G=gpu_q, C=cpu_q)
else:
now, soon = select(kernels, M1=mixedc_q, M2=mixedg_q, G=gpu_q, C=cpu_q, feat_max=num_global_work_items_max, threshold=0.4)
else:
now, soon = soon, None
if now == None:
logging.debug( "Searching for available devices")
continue
i, p = now
if fw.nCPU > rCPU and fw.nGPU > rGPU:
logging.debug( "DISPATCH MULTIPLE")
g_factor = fw.nGPU if rGPU == 0 else fw.nGPU / rGPU
c_factor = fw.nCPU if rCPU == 0 else fw.nCPU / rCPU
free_gpus, free_cpus = [], []
# try:
while fw.test_and_set(0,1):
pass
if p == 0:
for j in range(c_factor):
free_cpus.append(fw.ready_queue['cpu'].popleft())
elif p == 10:
for j in range(g_factor):
free_gpus.append(fw.ready_queue['gpu'].popleft())
else:
for j in range(c_factor):
free_cpus.append(fw.ready_queue['cpu'].popleft())
for j in range(g_factor):
free_gpus.append(fw.ready_queue['gpu'].popleft())
fw.rqlock[0] = 0
# except:
# logging.debug( free_cpus, free_gpus, framework.ready_queue, framework.nCPU, framework.nGPU, c_factor, g_factor
if kernels[i].partition == 0:
rCPU -= 1
elif kernels[i].partition == 10:
rGPU -= 1
else:
rCPU -= 1
rGPU -= 1
kernels[i].partition = p
# kernels[i].build_kernel(gpus, cpus, ctxs)
# kernels[i].random_data()
logging.debug( "Dispatching Multiple " + str(kernels[i].name))
start_time, done_events = kernels[i].dispatch_multiple(free_gpus, free_cpus, ctxs, cmd_qs)
events.extend(done_events)
num_dispatched += 1
# if False:
# pass
else:
logging.debug( "DISPATCH")
cpu, gpu = -1, -1
if p == 0:
while fw.test_and_set(0, 1):
pass
cpu = fw.ready_queue['cpu'].popleft()
fw.rqlock[0] = 0
elif p == 10:
while fw.test_and_set(0, 1):
pass
gpu = fw.ready_queue['gpu'].popleft()
fw.rqlock[0] = 0
else:
while fw.test_and_set(0, 1):
pass
cpu = fw.ready_queue['cpu'].popleft()
gpu = fw.ready_queue['gpu'].popleft()
fw.rqlock[0] = 0
if kernels[i].partition == 0:
rCPU -= 1
elif kernels[i].partition == 10:
rGPU -= 1
else:
rCPU -=1
rGPU -=1
kernels[i].partition = p
logging.debug( "Dispatching " + str(kernels[i].name) + " with partition class " + str(kernels[i].partition))
start_time, done_events = kernels[i].dispatch(gpu, cpu, ctxs, cmd_qs)
events.extend(done_events)
num_dispatched +=1
else:
logging.debug( "Devices unavailable")
fw.host_synchronize(cmd_qs, events)
return fw.dump_device_history()
help='Flag for dumping output file for a kernel',
action="store_true")
parser.add_argument('-lf',
'--layer_forward',
help='Test Layer ID Forward',
default=0)
parser.add_argument('-lb',
'--layer_backward',
help='Test Layer ID Backward',
default=0)
return parser.parse_args(args)
args = parse_arg(sys.argv[1:])
CLHOST = fw.host_initialize(int(args.nGPU), int(args.nCPU))
class LocalController(object):
def __init__(self):
pass
class Transforms(object):
def __init__(self):
self.macros = dict()
self.local_work_size = []
self.global_work_size = []
self.buffer_sizes = {'input': [], 'output': []}
self.buffer_chunks = {'input': [], 'output': []}
self.local_buffer_sizes = []
