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yr_utils.py
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import sys
import numpy as np
import torch
import torch.nn.functional as F
import pandas as pd
np.set_printoptions(threshold=sys.maxsize)
from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
import copy
import os
def load_edge_index(cGridCell):
if machine == 0:
sample_array = np.loadtxt("/home/yrayhan/works/lpmoss/kb_b/" + str(CPUID[0]) + "/query_view.txt")
else:
sample_array = np.loadtxt("/home/yrayhan/works/lpmoss/kb_icelake_quad/" + str(CPUID[0]) + "/query_view.txt")
edge_indexes = [[] for _ in range(sample_array.shape[0])]
edge_indexes_w = [[] for _ in range(sample_array.shape[0])]
for _ in CPUID:
if machine == 0:
RAW_FILE = "/home/yrayhan/works/lpmoss/kb_b/" + str(_) + "/query_view.txt"
else:
RAW_FILE = "/home/yrayhan/works/lpmoss/kb_icelake_quad/" + str(_) + "/query_view.txt"
raw_array = np.loadtxt(RAW_FILE)
idx_array = raw_array[:, 0:2]
qCorr_array = np.reshape(raw_array[:, 2:], (raw_array.shape[0], cGridCell, cGridCell))
for _ in range(qCorr_array.shape[0]):
qCorr_ts = qCorr_array[_] # ts
for row in range(cGridCell):
indexes = np.where(qCorr_ts[row] > 0)
for n in indexes[0]:
edge_indexes[_].append([row, n])
edge_indexes_w[_].append(qCorr_ts[row, n])
return edge_indexes, edge_indexes_w
def load_hardware_snapshot(exp_config):
cfg_par = exp_config.cfg_par
sample_array = np.loadtxt(exp_config.kb_path + str(exp_config.machine.li_ncore_dumper[0]) + "/data_view.txt")
GRID_FEATURES = np.zeros((sample_array.shape[0], exp_config.cnt_grid_cells, exp_config.num_features))
GRID_QUERIES = np.zeros((sample_array.shape[0], exp_config.cnt_grid_cells))
for _ in exp_config.machine.li_ncore_dumper:
RAW_FILE = exp_config.kb_path+str(_) + "/data_view.txt"
raw_array = np.loadtxt(RAW_FILE)
cfgs_info = raw_array[:, 0:cfg_par]
feature_plus_samples_array = raw_array[:, cfg_par:]
feature_array = feature_plus_samples_array[:, :-exp_config.cnt_grid_cells]
feature_array = np.reshape(feature_array, (feature_array.shape[0], exp_config.cnt_grid_cells, -1))
feature_array = feature_array[:, :, :exp_config.num_features]
GRID_FEATURES = np.add(GRID_FEATURES, feature_array)
samples_array = feature_plus_samples_array[:, -exp_config.cnt_grid_cells:]
GRID_QUERIES = np.add(GRID_QUERIES, samples_array)
# samples_array = np.repeat(samples_array, exp_config.num_features, axis=1)
# samples_array = np.reshape(samples_array, (-1, exp_config.cnt_grid_cells, exp_config.num_features))
#
# feature_array = np.divide(feature_array, samples_array)
# feature_array = np.nan_to_num(feature_array, neginf=0, nan=0)
grid_queries = np.repeat(GRID_QUERIES, exp_config.num_features, axis=1)
grid_queries = np.reshape(grid_queries, (-1, exp_config.cnt_grid_cells, exp_config.num_features))
GRID_FEATURES = np.nan_to_num(GRID_FEATURES, neginf=0, nan=0, posinf=9999999999)
grid_features = np.divide(GRID_FEATURES, grid_queries, out=np.zeros_like(GRID_FEATURES), where=grid_queries!=0)
# grid_features_idx = np.arange(0, exp_config.cnt_grid_cells)
# grid_features_idx = np.reshape(grid_features_idx, (1, grid_features_idx.shape[0]))
# grid_features_idx = np.repeat(grid_features_idx, grid_features.shape[0], axis=0)
# grid_features_idx = np.reshape(grid_features_idx, (-1, exp_config.cnt_grid_cells, 1))
# grid_features = np.concatenate([grid_features, grid_features_idx], axis=2)
return cfgs_info, grid_features
def load_uncore_features_intel(exp_config):
# Only for intel
cfg_par = exp_config.cfg_par
num_numa = exp_config.machine.numa_node
num_mc_per_numa = exp_config.machine.mc_channel_per_numa
num_socket = exp_config.machine.socket
num_upi_per_socket = exp_config.machine.num_upi_per_socket
RAW_FILE = exp_config.kb_path + str(exp_config.machine.li_ncore_dumper[0]) + "/mem-channel_view.txt"
raw_array = np.loadtxt(RAW_FILE)
t1=cfg_par
t2=cfg_par+num_numa*num_mc_per_numa
read_channels = raw_array[:, t1:t2]
t1=t2
t2=cfg_par+2*num_numa*num_mc_per_numa
write_channels = raw_array[:, t1:t2]
t1=t2
t2=cfg_par+2*num_numa*num_mc_per_numa+num_socket*num_upi_per_socket
upi_channels_incoming = raw_array[:, t1:t2]
t1=t2
t2=cfg_par+2*num_numa*num_mc_per_numa+2*num_socket*num_upi_per_socket
upi_channels_outgoing = raw_array[:, t1:t2]
cfg = raw_array[:, :cfg_par]
read_channels = np.reshape(read_channels, (read_channels.shape[0], num_numa, -1))
write_channels = np.reshape(write_channels, (write_channels.shape[0], num_numa, -1))
upi_incoming = np.reshape(upi_channels_incoming, (upi_channels_incoming.shape[0], num_socket, -1))
upi_outgoing = np.reshape(upi_channels_outgoing, (upi_channels_outgoing.shape[0], num_socket, -1))
read_channels_throughput_ts = np.sum(read_channels, axis=2)
write_channels_throughput_ts = np.sum(write_channels, axis=2)
upi_incoming_throughput_ts = np.sum(upi_incoming, axis=2)
upi_outgoing_throughput_ts = np.sum(upi_outgoing, axis=2)
read_channels_throughput_ts = np.reshape(read_channels_throughput_ts,
(read_channels_throughput_ts.shape[0], num_numa, -1))
write_channels_throughput_ts = np.reshape(write_channels_throughput_ts,
(write_channels_throughput_ts.shape[0], num_numa, -1))
upi_incoming_throughput_ts = np.reshape(upi_incoming_throughput_ts,
(upi_incoming_throughput_ts.shape[0], num_socket, -1))
upi_outgoing_throughput_ts = np.reshape(upi_outgoing_throughput_ts,
(upi_outgoing_throughput_ts.shape[0], num_socket, -1))
return cfg, read_channels_throughput_ts, write_channels_throughput_ts, upi_incoming_throughput_ts, upi_outgoing_throughput_ts
def load_qtput_cum(exp_config):
cfg_par = exp_config.cfg_par
grp_len = exp_config.per_cfg_sample
raw_tL = []
idx = []
for _ in exp_config.machine.li_ncore_dumper:
RAW_FILE = exp_config.kb_path + str(_) + "/query-exec_view.txt"
raw_array = np.loadtxt(RAW_FILE, dtype=int)
bw = np.sum(raw_array[:, cfg_par:], axis=1)
raw_tL.append(bw)
cfg = np.reshape(raw_array[:, 0:cfg_par], (raw_array[:, 0:1].shape[0], cfg_par))
idx.append(cfg)
query_throughput = np.asarray(raw_tL)
query_throughput = np.moveaxis(query_throughput, 0, 1)
query_throughput = query_throughput.sum(axis=1)
query_throughput_shadow = np.zeros_like(query_throughput)
query_throughput_shadow[1:] = query_throughput[:query_throughput.shape[0]-1]
for _ in range(0, query_throughput_shadow.shape[0]):
query_throughput_shadow[_] = query_throughput_shadow[0]
_ = _ + grp_len
query_throughput = query_throughput - query_throughput_shadow
idx_array = np.asarray(idx)
return idx_array, query_throughput
def find_correct_max_tput_for_wl(exp_config):
cfg_q2, query_throughput_numa = load_qtput_cum(exp_config) # (tr, )
valid_tputs = []
valid_idxs = []
valid_cfgs = []
for _ in range(query_throughput_numa.shape[0]): # tr
cfg_ = cfg_q2[0][_][0]
wl_ = cfg_q2[0][_][2]
if wl_ == exp_config.workload:
valid_tputs.append(query_throughput_numa[_])
valid_cfgs.append(cfg_)
valid_idxs.append(_)
tputs_to_consider = np.asarray(valid_tputs)
max_tput = np.max(tputs_to_consider)
mIdx = np.argmax(tputs_to_consider)
maxCfg = valid_cfgs[mIdx]
maxIdx = valid_idxs[mIdx]
print("MAXCONFIG =", maxCfg, "MAXIDX=", maxIdx)
print(max_tput)
return max_tput
def find_correct_max_tput(exp_config):
cfg_q2, query_throughput_numa = load_qtput_cum(exp_config) # (tr, )
valid_tputs = []
valid_idxs = []
valid_cfgs = []
for _ in range(query_throughput_numa.shape[0]): # tr
cfg_ = cfg_q2[0][_][0]
# if cfg_ >= 1000:
# continue
# comment for quadtree: aug 2
# if _ <= 996 or 1677 <= _ <= 1707 or 1846 <= _ <= 1856 or 1917 <= _ <= 1931:
# continue
valid_tputs.append(query_throughput_numa[_])
valid_cfgs.append(cfg_)
valid_idxs.append(_)
tputs_to_consider = np.asarray(valid_tputs)
max_tput = np.max(tputs_to_consider)
mIdx = np.argmax(tputs_to_consider)
maxCfg = valid_cfgs[mIdx]
maxIdx = valid_idxs[mIdx]
print("MAXCONFIG =", maxCfg, "MAXIDX=", maxIdx)
print(max_tput)
return max_tput
def get_wkload_range(dataset, id, wkload, machine):
ranges = []
cfgs_to_choose= []
if dataset == 'glife' and id == 0 and wkload == 0 and machine == '4S8N':
ranges = [(1, 264), (841, 996), (1857, 1866), (1932, 1936), (1947, 1951)]
# cfgs_to_choose = [900, 901, 30, 40, 1, 100601]
cfgs_to_choose = [900, 901, 30, 40, 1, 200001]
# --------------------------------------------------------------------------------------------
elif dataset == 'glife' and id == 1 and wkload == 0 and machine == '4S8N':
ranges = [(81, 155), (276, 285), (506, 590)]
cfgs_to_choose = [900, 901, 30, 40, 20, 100601] # [Uni-Scan] [QTree] [GLife] [BigData]
# --------------------------------------------------------------------------------------------
# --------------------------------------------------------------------------------------------
elif dataset == 'bmod02' and id == 0 and wkload == 0 and machine == '4S8N':
ranges = [(997, 1312), (1867, 1876), (1937, 1946), (1982, 1986)]
# cfgs_to_choose = [900, 901, 30, 40, 1, 200001]
cfgs_to_choose = [900, 901, 30, 40, 1, 300001]
# --------------------------------------------------------------------------------------------
elif dataset == 'bmod02' and id == 1 and wkload == 0 and machine == '4S8N':
ranges = [(156, 220), (261, 275), (356, 360), (261, 275), (341, 355), (361, 370), (376, 505),
]
cfgs_to_choose = [900, 901, 30, 40, 1, 100601]
cfgs_to_choose = [900, 901, 30, 40, 1, 400001] # [Uni-Scan] [QTree] [BMod02] [BigData]
# --------------------------------------------------------------------------------------------
# -----------------------------[2 SOCKET SERVER][BMOD 02]---------------------------------------------------------------
elif dataset == 'bmod02' and id == 0 and wkload == 9 and machine == '2S':
ranges = [(141, 144)]
cfgs_to_choose = [900, 901, 2000, 2010, 2020, 400001]
elif dataset == 'bmod02' and id == 1 and wkload == 11 and machine == '2S':
ranges = [(61, 80), (130, 139)]
cfgs_to_choose = [1000, 1001, 2000, 2010, 2020, -1] # [Uni-Scan] [QTree] [BMod02] [BigData]
elif dataset == 'bmod02' and id == 1 and wkload == 12 and machine == '2S':
ranges = [(81, 92), (140, 149)]
cfgs_to_choose = [1000, 1001, 2000, 2010, 2020, -1] # [Uniform] [Quad] [OSM] [DBServer]
elif dataset == 'bmod02' and id == 1 and wkload == 13 and machine == '2S':
ranges = [(93, 104), (150, 159)]
cfgs_to_choose = [1000, 1001, 2000, 2010, 2020, -1] # [Uniform] [Quad] [OSM] [DBServer]
elif dataset == 'bmod02' and id == 1 and wkload == 14 and machine == '2S':
ranges = [(105, 116), (160, 169)]
cfgs_to_choose = [1000, 1001, 2000, 2010, 2020, -1] # [Uniform] [Quad] [OSM] [DBServer]
elif dataset == 'bmod02' and id == 1 and wkload == 15 and machine == '2S':
ranges = [(117, 129), (170, 179)]
cfgs_to_choose = [1000, 1001, 2000, 2010, 2020, -1] # [Uniform] [Quad] [OSM] [DBServer]
# --------------------------------------------------------------------------------------------
# -----------------------------[2 SOCKET SERVER][OSM]---------------------------------------------------------------
elif dataset == 'osm' and id == 1 and wkload == 0 and machine == '2S':
ranges = [(1, 80)]
cfgs_to_choose = [1000, 1001, 2000, 2010, 2020, -1] # [Uniform] [Quad] [OSM] [DBServer]
# --------------------------------------------------------------------------------------------
# --------------------------------------------------------------------------------------------
elif dataset == 'osm' and id == 0 and wkload == 0 and machine == '4S8N':
ranges = [(1313, 1432), (1812, 1816),(1827, 1836)]
cfgs_to_choose = [900, 901, 30, 40, 1, 200001]
elif dataset == 'osm' and id == 0 and wkload == 0 and machine == '2S':
ranges = [(1, 135)]
cfgs_to_choose = [1000, 1001, 2001, 2015, 2021, -1]
elif dataset == 'osm' and id == 0 and wkload == 0 and machine == '1S':
ranges = [(1, 25)]
cfgs_to_choose = [50, 51, 3000, 3020, 3027, -1]
# --------------------------------------------------------------------------------------------
# --------------------------------------------------------------------------------------------
elif dataset == 'osm' and id == 0 and wkload == 1 and machine == '4S8N':
ranges = [(1433, 1507), (1837, 1846), (1952, 1956)]
cfgs_to_choose = [900, 901, 30, 40, 10, 300002] # [Normal] [RTree] [OSM] [BigData]
# cfgs_to_choose = [900, 901, 30, 40, 10, 100600] # [Normal] [RTree] [OSM] [BigData]
elif dataset == 'osm' and id == 0 and wkload == 2 and machine == '4S8N':
ranges = [(1508, 1587)]
cfgs_to_choose = [-1, -1, 30, 40, 1, 100600]
elif dataset == 'osm' and id == 0 and wkload == 3 and machine == '4S8N':
ranges = [(1587, 1637)]
cfgs_to_choose = [-1, -1, 30, 40, 1, 100600]
elif dataset == 'osm' and id == 0 and wkload == 10 and machine == '4S8N':
ranges = [(1678, 1707), (1847, 1851), (1917, 1931), (1962, 1966)]
# cfgs_to_choose = [900, 32, 30, 40, 1, 100600] # [Log-Normal] [RTree] [OSM] [BigData]
cfgs_to_choose = [900, 32, 30, 40, 2, 300003] # [Log-Normal] [RTree] [OSM] [BigData]
elif dataset == 'osm' and id == 0 and wkload == 5 and machine == '4S8N':
ranges = [(1708, 1722), (1907, 1916), (1957, 1961)]
cfgs_to_choose = [900, 901, 30, 40, 1, 300004] # [Zipfian: skew = 40] [RTree] [OSM] [BigData]
# cfgs_to_choose = [900, 901, 30, 40, 1, 200001] # [Zipfian: skew = 40] [RTree] [OSM] [BigData]
elif dataset == 'osm' and id == 0 and wkload == 6 and machine == '4S8N':
ranges = [(1737, 1756), (1817, 1821), (1877, 1886), (1967, 1971)]
# cfgs_to_choose = [900, 901, 30, 40, 1, 200001]
cfgs_to_choose = [900, 901, 30, 40, 1, 300005] # [Hot=3] [RTree] [OSM] [BigData]
elif dataset == 'osm' and id == 0 and wkload == 7 and machine == '4S8N':
ranges = [(1757, 1776), (1887, 1896), (1972, 1976)]
cfgs_to_choose = [900, 901, 30, 40, 1, 100601]
cfgs_to_choose = [900, 901, 30, 40, 1, 300006] # [Hot=5] [RTree] [OSM] [BigData]
elif dataset == 'osm' and id == 0 and wkload == 8 and machine == '4S8N':
ranges = [(1777, 1796), (1897, 1906), (1977, 1981)]
cfgs_to_choose = [900, 901, 30, 40, 1, 100600]
cfgs_to_choose = [900, 901, 30, 40, 1, 300007] # [Hot=7] [RTree] [OSM] [BigData]
elif dataset == 'osm' and id == 0 and wkload == 9 and machine == '4S8N':
ranges = [(1797, 1811), (1822, 1826)]
cfgs_to_choose = [-1, -1, 30, 40, 1, 200001]
cfgs_to_choose = [-1, -1, 30, 40, 1, 100600]
# --------------------------------------------------------------------------------------------
elif dataset == 'osm' and id == 1 and wkload == 0 and machine == '4S8N':
ranges = [(1, 80), (286, 295), (296, 340), (371, 374), (591, 625)]
cfgs_to_choose = [900, 901, 30, 40, 1, 200100] # [Uniform-Scan] [Quad] [OSM] [BigData]
# cfgs_to_choose = [900, 901, 30, 40, 1, 300999]
elif dataset == 'osm' and id == 1 and wkload == 10 and machine == '4S8N':
ranges = [(256, 260)]
cfgs_to_choose = [900, 901, 30, 40, 1, 200100] # [Uniform-Scan] [Quad] [OSM] [BigData]
elif dataset == 'osm' and id == 1 and wkload == 0 and machine == '2S':
ranges = [(1, 60)]
cfgs_to_choose = [1000, 1001, 2000, 2010, 2020, -1] # [Uniform] [Quad] [OSM] [DBServer]
elif dataset == 'osm' and id == 1 and wkload == 0 and machine == '1S':
ranges = [(1, 70)]
cfgs_to_choose = [50, 51, 3000, -1, 3020, -1] # [Uniform] [Quad] [OSM] [DBServer----1SS]
elif dataset == 'ycsb' and id == 3 and wkload == 6 and machine == '4S8N':
ranges = [(306, 555)]
cfgs_to_choose = [50, 51, 3000, -1, 3020, -1] # [Uniform] [Quad] [OSM] [DBServer----1SS]
elif dataset == 'ycsb' and id == 3 and wkload == 7 and machine == '4S8N':
ranges = [(556, 662)]
cfgs_to_choose = [50, 51, 3000, -1, 3020, -1] # [Uniform] [Quad] [OSM] [DBServer----1SS]
return ranges
def load_qtput_per_kscell(exp_config):
cfg_par = exp_config.cfg_par
grp_len = exp_config.per_cfg_sample
raw_tL = []
idx = []
for _ in exp_config.machine.li_ncore_dumper:
RAW_FILE = exp_config.kb_path + str(_) + "/query-exec_view.txt"
raw_array = np.loadtxt(RAW_FILE, dtype=int)
bw = raw_array[:, cfg_par:]
raw_tL.append(bw)
cfg = np.reshape(raw_array[:, 0:cfg_par], (raw_array[:, 0:1].shape[0], cfg_par))
idx.append(cfg)
query_throughput = np.asarray(raw_tL)
query_throughput = np.moveaxis(query_throughput, 0, 1)
query_throughput = query_throughput.sum(axis=1)
query_throughput_shadow = np.zeros_like(query_throughput)
query_throughput_shadow[1:, :] = query_throughput[:query_throughput.shape[0]-1, :]
for _ in range(0, query_throughput_shadow.shape[0]):
query_throughput_shadow[_] = query_throughput_shadow[0]
_ = _ + grp_len
query_throughput = query_throughput - query_throughput_shadow
idx_array = np.asarray(idx)
return idx_array, query_throughput
def load_machine_adjacency():
edge_indexes = []
edge_indexes_w = []
RAW_FILE = "/home/yrayhan/works/lpmoss/machine_configs/raw_config.txt"
raw_array = np.loadtxt(RAW_FILE)
for _ in range(raw_array.shape[0]):
indexes = np.where(raw_array[_] > 0)
for n in indexes[0]:
edge_indexes.append([_, n])
edge_indexes_w.append(raw_array[_, n])
return edge_indexes, edge_indexes_w
def load_frozen_cell_embeddings():
emb = np.loadtxt("predicted_in.features")
return emb
def load_actions(exp_config, cfg, wl, onlyNUMA=False):
# 1. this should come from the machine, which are the worker threads and how they should be converted to 0 - num of workers
# 2. at the end there is this 10 * 10 division, you can change it to a square root division.
gen_core_dict = {}
coreIdx = 0
for _ in exp_config.machine.li_worker:
gen_core_dict[_] = coreIdx
coreIdx += 1
refined_configs = []
cIdx = int(cfg)
# TODO: Have the machine name and access name posssibly then add the config stuff
if exp_config.index==0:
if cIdx >= 200:
if exp_config.cnt_grid_cells == 100:
machine_config_path = "./pmoss_machine_configs/" + exp_config.processor + "/" + str(int(wl)) + "/c_" + str(cIdx) + ".txt"
else:
machine_config_path = "./pmoss_machine_configs/" + exp_config.processor + "/" + str(int(wl)) + "/c_" + str(cIdx) + "_" + str(exp_config.cnt_grid_cells) + ".txt"
else:
if exp_config.cnt_grid_cells == 100:
machine_config_path = "./machine_configs/" + exp_config.processor + "/" + "c_" + str(cIdx) + ".txt"
else:
machine_config_path = "./machine_configs/" + exp_config.processor + "/" + "c_" + str(cIdx) + "_" + str(exp_config.cnt_grid_cells) + ".txt"
elif exp_config.index==1:
if cIdx >= 200:
if exp_config.cnt_grid_cells == 100:
machine_config_path = "./pmoss_machine_configs/" + exp_config.processor + "/" + str(int(wl)) + "/c_" + str(cIdx) + "_r.txt"
else:
machine_config_path = "./pmoss_machine_configs/" + exp_config.processor + "/" + str(int(wl)) + "/c_" + str(cIdx) + "_" + str(exp_config.cnt_grid_cells) + "_r.txt"
else:
if exp_config.cnt_grid_cells == 100:
machine_config_path = "./machine_configs/" + exp_config.processor + "/" + "c_" + str(cIdx) + "_r.txt"
else:
machine_config_path = "./machine_configs/" + exp_config.processor + "/" + "c_" + str(cIdx) + "_" + str(exp_config.cnt_grid_cells) + "_r.txt"
config = np.loadtxt(machine_config_path)
numa_core_div = exp_config.policy_dim[0]
config_numa = config[:numa_core_div, :]
config_core = config[numa_core_div:, :]
config_numa = np.reshape(config_numa, (config_numa.shape[0] * config_numa.shape[1],))
config_core = np.reshape(config_core, (config_core.shape[0] * config_core.shape[1],))
refined_config = np.empty_like(config_core)
for _ in range(config_core.shape[0]):
refined_config[_] = gen_core_dict[config_core[_]]
if onlyNUMA:
refined_configs.append(config_numa)
else:
refined_configs.append(refined_config)
refined_configs = np.asarray(refined_configs)
refined_configs = np.reshape(refined_config, (-1, ))
# refined_configs_li = refined_config.tolist()
return refined_configs
def load_actions_hw_pos(exp_config, cfg, wl, onlyNUMA=False):
# 1. this should come from the machine, which are the worker threads and how they should be converted to 0 - num of workers
# 2. at the end there is this 10 * 10 division, you can change it to a square root division.
cores_position = exp_config.machine.worker_to_chassis_pos_mapping
gen_core_dict = {}
coreIdx = 0
for _ in exp_config.machine.li_worker:
gen_core_dict[_] = coreIdx
coreIdx += 1
refined_configs = []
cIdx = int(cfg)
# TODO: Have the machine name and access name posssibly then add the config stuff
if exp_config.index==0:
if cIdx >= 200:
if exp_config.cnt_grid_cells == 100:
machine_config_path = "./pmoss_machine_configs/" + exp_config.processor + "/" + str(int(wl)) + "/c_" + str(cIdx) + ".txt"
else:
machine_config_path = "./pmoss_machine_configs/" + exp_config.processor + "/" + str(int(wl)) + "/c_" + str(cIdx) + "_" + str(exp_config.cnt_grid_cells) + ".txt"
else:
if exp_config.cnt_grid_cells == 100:
machine_config_path = "./machine_configs/" + exp_config.processor + "/" + "c_" + str(cIdx) + ".txt"
else:
machine_config_path = "./machine_configs/" + exp_config.processor + "/" + "c_" + str(cIdx) + "_" + str(exp_config.cnt_grid_cells) + ".txt"
elif exp_config.index==1:
if cIdx >= 200:
if exp_config.cnt_grid_cells == 100:
machine_config_path = "./pmoss_machine_configs/" + exp_config.processor + "/" + str(int(wl)) + "/c_" + str(cIdx) + "_r.txt"
else:
machine_config_path = "./pmoss_machine_configs/" + exp_config.processor + "/" + str(int(wl)) + "/c_" + str(cIdx) + "_" + str(exp_config.cnt_grid_cells) + "_r.txt"
else:
if exp_config.cnt_grid_cells == 100:
machine_config_path = "./machine_configs/" + exp_config.processor + "/" + "c_" + str(cIdx) + "_r.txt"
else:
machine_config_path = "./machine_configs/" + exp_config.processor + "/" + "c_" + str(cIdx) + "_" + str(exp_config.cnt_grid_cells) + "_r.txt"
config = np.loadtxt(machine_config_path)
numa_core_div = exp_config.policy_dim[0]
config_numa = config[:numa_core_div, :]
config_core = config[numa_core_div:, :]
config_numa = np.reshape(config_numa, (config_numa.shape[0] * config_numa.shape[1],))
config_core = np.reshape(config_core, (config_core.shape[0] * config_core.shape[1],))
refined_config = np.empty_like(config_core)
for _ in range(config_core.shape[0]):
refined_config[_] = cores_position[int(gen_core_dict[config_core[_]])]
if onlyNUMA:
refined_configs.append(config_numa)
else:
refined_configs.append(refined_config)
refined_configs = np.asarray(refined_configs)
refined_configs = np.reshape(refined_config, (-1, ))
# refined_configs_li = refined_config.tolist()
return refined_configs
def retrieve_config(exp_config, out_actions, cfg_idx):
hw_chassis = exp_config.machine.worker_to_chassis_pos_mapping
hw_workers = exp_config.machine.li_worker
num_numa = exp_config.machine.numa_node
numa_nodes = []
workers = []
for a_ in out_actions:
worker_index = hw_chassis.index(a_)
workers.append(hw_workers[worker_index])
numa_nodes.append(worker_index%num_numa)
numa_nodes = np.asarray(numa_nodes)
workers = np.asarray(workers)
retrieved_configs_numa = np.reshape(numa_nodes, (exp_config.policy_dim[0], exp_config.policy_dim[1]))
retrieved_configs_core = np.reshape(workers, (exp_config.policy_dim[0], exp_config.policy_dim[1]))
retrieved_configs = np.vstack((retrieved_configs_numa, retrieved_configs_core))
retrieved_configs = retrieved_configs.astype(int)
save_cfg_dir = "./pmoss_machine_configs/" + exp_config.processor + "/" + str(exp_config.workload)
os.makedirs(save_cfg_dir, exist_ok=True)
if exp_config.index == 0:
cfg_file = save_cfg_dir + "/c_" + str(cfg_idx) + "_" + str(exp_config.cnt_grid_cells) + ".txt"
elif exp_config.index == 1:
cfg_file = save_cfg_dir + "/c_" + str(cfg_idx) + "_" + str(exp_config.cnt_grid_cells) + "_r.txt"
np.savetxt(cfg_file, retrieved_configs, fmt='%i')
return retrieved_configs
def sth(cfg_idx):
# cfg = np.loadtxt("machine_configs/config_" + str(cfg_idx) + ".txt")[10:]
cfg = np.loadtxt("/home/yrayhan/works/lpmoss/machine_configs/config_" + str(cfg_idx) + ".txt")[10:]
cfg = np.reshape(cfg, (-1, ))
unique, counts = np.unique(cfg, return_counts=True)
d = dict(zip(unique, counts))
print(len(d))
print(d)
# sth(221)
# sth(3000)
# =======================================================================================
# Commented it oct 13
# =======================================================================================
# if machine == 0:
# cGridCell = 100
# nFeatures = 5 + 1 # Changed it to 23 + 1, previous: 5 + 1 # change here 10 + 1
# num_numa = 8
# cCorePerNuma = 8
# cMCChannel = 3
# else:
# cGridCell = 100
# nFeatures = 5 + 1 # Changed it to 23 + 1, previous: 5 + 1 # change here 10 + 1
# num_numa = 2
# cCorePerNuma = 40
# cMCChannel = 12 # this is not sure
# =======================================================================================
# =======================================================================================
# =======================================================================================
# idx_array, grid_features, grid_queries = load_hardware_snapshot(cGridCell, nFeatures-1)
# # (tr, 2) (tr, nGridCells, nFeatures) (tr, 100, 5)
# edge_indexes, edge_indexes_w = load_edge_index(cGridCell) # list(tr)
# cfg_s, mc_read_tput, mc_write_tput = load_uncore_features_intel(num_numa, cMCChannel)
# # (tr, 2) (tr, 8, 1) (tr, 8, 1)
# cfg_q, query_throughput = load_qtput_cum() # (tr, )
# mc_tput = np.concatenate([mc_read_tput, mc_write_tput], axis=2)
# m_adj, m_adj_weight = load_machine_adjacency()
def gen_token_for_eval(exp_config):
# (tr, 2) (tr, nGridCells, nFeatures) (tr, 100, 5)
idx_array, grid_features = load_hardware_snapshot(exp_config)
cfg_q, query_throughput = load_qtput_per_kscell(exp_config) # (tr, cGridCell)
cfg_q2, query_throughput_numa = load_qtput_cum(exp_config) # (tr, )
if not(exp_config.num_meta_features == 0):
cfg_q3, read_channels_throughput_ts, write_channels_throughput_ts, upi_incoming_throughput_ts, upi_outgoing_throughput_ts = load_uncore_features_intel(exp_config)
upi_tput = np.concatenate([upi_incoming_throughput_ts, upi_outgoing_throughput_ts], axis=2)
mc_tput = np.concatenate([read_channels_throughput_ts, write_channels_throughput_ts], axis=2)
upi_tput = np.reshape(upi_tput, (upi_tput.shape[0], -1))
mc_tput = np.reshape(mc_tput, (mc_tput.shape[0], -1))
mc_tput = np.concatenate([mc_tput, upi_tput], axis=1)
grid_features = np.reshape(grid_features, (grid_features.shape[0], -1))
"""
For cleaning the data in amd processors, it's a bad practice but well
"""
if exp_config.processor == "amd_epyc7543_2s_2n" or exp_config.processor == "amd_epyc7543_2s_8n":
grid_features = np.reshape(grid_features, (grid_features.shape[0], -1, exp_config.num_features))
refine = [244, 245, 246, 251]
for _ in refine:
grid_features[:, _, :] = 0.00001
grid_features = np.reshape(grid_features, (grid_features.shape[0], -1))
""""""
scaler = StandardScaler()
grid_features = scaler.fit_transform(grid_features)
grid_features = np.reshape(grid_features, (grid_features.shape[0], -1, exp_config.num_features))
grid_features_idx = np.arange(0, exp_config.cnt_grid_cells)
grid_features_idx = np.reshape(grid_features_idx, (1, grid_features_idx.shape[0]))
grid_features_idx = np.repeat(grid_features_idx, grid_features.shape[0], axis=0)
grid_features_idx = np.reshape(grid_features_idx, (-1, exp_config.cnt_grid_cells, 1))
grid_features = np.concatenate([grid_features, grid_features_idx], axis=2)
if not(exp_config.num_meta_features == 0):
mc_tput = np.reshape(mc_tput, (mc_tput.shape[0], -1))
scaler_mc = StandardScaler()
mc_tput = scaler_mc.fit_transform(mc_tput)
mc_tput = np.reshape(mc_tput, (mc_tput.shape[0], 1, -1))
mc_tput = np.repeat(mc_tput, exp_config.cnt_grid_cells, axis=1)
orginal_max_tput_dset = find_correct_max_tput(exp_config) * exp_config.rtg_scale
orginal_max_tput_dset = find_correct_max_tput_for_wl(exp_config) * exp_config.rtg_scale
obss = []
obss_s = []
obss_mask = []
actions = []
stepwise_returns = []
rtgs = []
done_idxs = []
timesteps = []
metas = []
actions_ = []
cores_position = exp_config.machine.worker_to_chassis_pos_mapping
num_numa = exp_config.machine.numa_node
num_worker_per_numa = exp_config.machine.worker_per_numa
lbound_core = 2 # 100 / 56
lbound_numa = 13 # 100/8
chassis_dimx = exp_config.chassis_dim[0]
chassis_dimy = exp_config.chassis_dim[1]
for _ in range(idx_array.shape[0]): # tr
cfg_ = idx_array[_][0]
wl_ = idx_array[_][2]
if cfg_!= exp_config.eval_start_cfg and wl_ != exp_config.workload:
continue
# Load the actions (how many for each complete row? = no of grid cells)
act_ = load_actions(exp_config, cfg_, wl_)
actions.append(act_)
if not(exp_config.num_meta_features == 0):
# Load the meta mc_data
metas.append(mc_tput[_])
"""Update the obss mask: mask should be where you should not put
Which one to off?
"""
numa_machine_obs = np.full((chassis_dimx * chassis_dimy, ), False)
numa_machine_obs_s = np.full((chassis_dimx * chassis_dimy, exp_config.num_features+1), 0, dtype=np.float64)
# numa_machine_obss_mask = np.full((chassis_dimx * chassis_dimy,), True)
numa_machine_obss_mask = np.full((chassis_dimx * chassis_dimy,), False)
obs_mask_core = np.full((chassis_dimx * chassis_dimy, ), 0)
chassis_act_=[int(cores_position[int(z)]) for z in range(exp_config.machine.num_worker)]
obs_mask_core[np.array(chassis_act_).astype(int)] = lbound_core
mask_already_full = np.where(obs_mask_core==0)
numa_machine_obss_mask[mask_already_full] = True
st_return = np.full((act_.shape[0], ), 0)
tg_return = np.full((act_.shape[0]+1, ), 0) # => made it +1: april 16
max_tput_dset = orginal_max_tput_dset
tg_return[0] = max_tput_dset
obss.append(copy.deepcopy(numa_machine_obs)) # empty obs
obss_s.append(copy.deepcopy(numa_machine_obs_s))
obss_mask.append(copy.deepcopy(numa_machine_obss_mask))
# print(torch.tensor(obss).view(-1, 8, 8))
# print(query_throughput[_])
# print(query_throughput_numa[_])
flag = False
actions_individual = np.zeros_like(act_)
for i in range(act_.shape[0]): # each timestep
# map_idx=exp_config.machine.li_worker.index(int(act_[i]))
# a = exp_config.machine.worker_to_chassis_pos_mapping[map_idx]
a = int(cores_position[int(act_[i])])
actions_individual[i] = a
# => Add this if condition so that i can place stuff again
# if i > 32:
# obs_mask_core = np.full((num_numa * num_worker_per_numa, ), 2)
# if i > 60 and not(flag): # For obs_mask_core = [2+1]
# obs_mask_core = np.full((num_numa * num_worker_per_numa, ), 1)
# flag = True
# numa_machine_obss_mask = np.full((chassis_dimx * chassis_dimy,), False)
# mask_already_full = obs_mask_core.nonzero()
# numa_machine_obss_mask[mask_already_full] = True
"""Update the obss mask: mask should be where you should not put
Which one to off?
"""
numa_machine_obss_mask = np.full((chassis_dimx * chassis_dimy,), False)
obs_mask_core = np.full((chassis_dimx * chassis_dimy, ), 0)
chassis_act_=[int(cores_position[int(z)]) for z in range(exp_config.machine.num_worker)]
obs_mask_core[np.array(chassis_act_).astype(int)] = lbound_core
mask_already_full = np.where(obs_mask_core==0)
numa_machine_obss_mask[mask_already_full] = True
st_return[i] = query_throughput[_][i]
# tg_return[0] should be the max value and equal across
if i != act_.shape[0]-1:
tg_return[i+1] = max_tput_dset - query_throughput[_][i]
max_tput_dset = tg_return[i+1]
# By placing the [ith] grid cell, at the [a]th place in the machine
# you take the grid feature of the ith cell currently,
# i am just replacing the values, what happens if they have diff value or whether this is not possible at all
# actions.append(a)
numa_machine_obs[int(a)] = True
numa_machine_obs_s[int(a)] += grid_features[_][i]
if i != act_.shape[0]-1:
obss.append(copy.deepcopy(numa_machine_obs))
obss_s.append(copy.deepcopy(numa_machine_obs_s))
obss_mask.append(copy.deepcopy(numa_machine_obss_mask))
# print(torch.tensor(obss).view(-1, 8, 8))
# zz = input()
# print(torch.tensor(numa_machine_obs).view(8, 8))
# print(torch.tensor(numa_machine_obss_mask).view(8, 8))
# print("=======================")
# if i == 10:
# exit()
# print(tg_return)
# print(st_return)
# print("=======================")
# actions.append(-1)
actions_.append(actions_individual)
done_idxs.append((_+1) * i)
rtgs.append(copy.deepcopy(tg_return[:exp_config.cnt_grid_cells])) # parameterize it
stepwise_returns.append(copy.deepcopy(st_return))
timesteps.append(np.reshape(np.arange(act_.shape[0]), (-1, )))
# print(torch.tensor(rtgs).view(-1, 100))
# print(torch.tensor(stepwise_returns).view(-1, 100))
# actions = np.asarray(actions) # (nsamples * ngridcells, 1)
# # (nSamples * (nGridcells+1 = initial observation = 0s), num_numa,num_worker_per_numa)
# obss = np.asarray(obss)
# # (nSamples * (nGridcells+1 = initial observation = 0s), num_numa, num_worker_per_numa, nFeatures)
# obss_s = np.asarray(obss_s)
# obss_mask = np.asarray(obss_mask)
# stepwise_returns = np.asarray(stepwise_returns) # (nSamples * nGridcells, 1)
# rtgs = np.asarray(rtgs) # (nSamples * nGridcells, 1)
# done_idxs = np.asarray(done_idxs) # (nSamples, 1)
# timesteps = np.asarray(timesteps) # (nsamples * ngridcells, 1)
# print(actions.shape)
# print(obss.shape)
# print(obss_s.shape)
# print(obss_mask.shape)
# print(stepwise_returns.shape)
# print(rtgs.shape)
# print(done_idxs.shape)
# print(timesteps.shape)
"""Send the positions in the hardware, because this is what we actually do the prediction on"""
actions_ = np.reshape(np.asarray(actions_), (-1, )) # (nsamples * ngridcells, 1)
actions = np.reshape(np.asarray(actions), (-1, )) # (nsamples * ngridcells, 1)
# (nSamples * (nGridcells+1 = initial observation = 0s), num_numa,num_worker_per_numa)
obss = np.reshape(np.asarray(obss), (-1, 1, chassis_dimx, chassis_dimy))
# (nSamples * (nGridcells+1 = initial observation = 0s), num_numa, num_worker_per_numa, nFeatures)
obss_s = np.reshape(np.asarray(obss_s), (-1, 1, chassis_dimx, chassis_dimy, exp_config.num_features+1))
obss_mask = np.reshape(np.asarray(obss_mask), (-1, 1, chassis_dimx, chassis_dimy))
stepwise_returns = np.reshape(np.asarray(stepwise_returns), (-1, 1)) # (nSamples * nGridcells, 1)
rtgs = np.reshape(np.asarray(rtgs), (-1, 1)) # (nSamples * nGridcells, 1)
done_idxs = np.reshape(np.asarray(done_idxs), (-1, )) # (nSamples, 1)
timesteps = np.reshape(np.asarray(timesteps), (-1, )) # (nsamples * ngridcells, 1)
rtgs = rtgs / exp_config.rtg_div
print(actions.shape)
print(obss.shape)
print(obss_s.shape)
print(obss_mask.shape)
print(stepwise_returns.shape)
print(rtgs.shape)
print(done_idxs.shape)
print(timesteps.shape)
if not(exp_config.num_meta_features == 0):
metas = np.reshape(np.asarray(metas), (obss_s.shape[0], -1)) # (nSamples * nGridcells, 1)
# metas = np.zeros((obss_s.shape[0], 6))
# This definitely needs work
lengths = np.full((obss_s.shape[0], 1), exp_config.cnt_grid_cells) # it should be actually the number of valid actions until which timestep
benchmarks = np.zeros((obss_s.shape[0], 1))
'''Sedning actions_ instead of actions'''
return obss, obss_s, obss_mask, actions_, stepwise_returns, rtgs, done_idxs, timesteps, metas, lengths, benchmarks
def gen_token(exp_config):
# (tr, 2) (tr, nGridCells, nFeatures) (tr, 100, 5)
idx_array, grid_features = load_hardware_snapshot(exp_config) # hardware snapshot + grid index
cfg_q, query_throughput = load_qtput_per_kscell(exp_config) # (tr, cGridCell)
cfg_q2, query_throughput_numa = load_qtput_cum(exp_config) # (tr, )
if not(exp_config.num_meta_features == 0):
cfg_q3, read_channels_throughput_ts, write_channels_throughput_ts, upi_incoming_throughput_ts, upi_outgoing_throughput_ts = load_uncore_features_intel(exp_config)
upi_tput = np.concatenate([upi_incoming_throughput_ts, upi_outgoing_throughput_ts], axis=2)
mc_tput = np.concatenate([read_channels_throughput_ts, write_channels_throughput_ts], axis=2)
upi_tput = np.reshape(upi_tput, (upi_tput.shape[0], -1))
mc_tput = np.reshape(mc_tput, (mc_tput.shape[0], -1))
mc_tput = np.concatenate([mc_tput, upi_tput], axis=1)
# print(idx_array.shape, grid_features.shape)
# print(cfg_q.shape, query_throughput.shape)
# print(cfg_q2.shape, query_throughput_numa.shape)
# print(cfg_q3.shape, read_channels_throughput_ts.shape, write_channels_throughput_ts.shape)
# print(mc_tput.shape)
# (1931, 2) (1931, 100, 6) (1931, 100, 5)
# (8, 1931, 2) (1931, 100)
# (8, 1931, 2) (1931,)
# (1931, 2) (1931, 8, 1) (1931, 8, 1)
# (1931, 8, 2)
grid_features = np.reshape(grid_features, (grid_features.shape[0], -1))
"""
For cleaning the data in amd processors, it's a bad practice but well
"""
if exp_config.processor == "amd_epyc7543_2s_2n" or exp_config.processor == "amd_epyc7543_2s_8n":
grid_features = np.reshape(grid_features, (grid_features.shape[0], -1, exp_config.num_features))
refine = [244, 245, 246, 251]
for _ in refine:
grid_features[:, _, :] = 0.00001
grid_features = np.reshape(grid_features, (grid_features.shape[0], -1))
""""""
scaler = StandardScaler()
grid_features = scaler.fit_transform(grid_features)
grid_features = np.reshape(grid_features, (grid_features.shape[0], -1, exp_config.num_features))
grid_features_idx = np.arange(0, exp_config.cnt_grid_cells)
grid_features_idx = np.reshape(grid_features_idx, (1, grid_features_idx.shape[0]))
grid_features_idx = np.repeat(grid_features_idx, grid_features.shape[0], axis=0)
grid_features_idx = np.reshape(grid_features_idx, (-1, exp_config.cnt_grid_cells, 1))
grid_features = np.concatenate([grid_features, grid_features_idx], axis=2)
# for _ in range(grid_features.shape[1]):
# print(grid_features[0, _, :])
# zz=input()
if not(exp_config.num_meta_features == 0):
mc_tput = np.reshape(mc_tput, (mc_tput.shape[0], -1))
scaler_mc = StandardScaler()
mc_tput = scaler_mc.fit_transform(mc_tput)
mc_tput = np.reshape(mc_tput, (mc_tput.shape[0], 1, -1))
mc_tput = np.repeat(mc_tput, exp_config.cnt_grid_cells, axis=1)
orginal_max_tput_dset = find_correct_max_tput(exp_config) * 1.0
obss = []
obss_s = []
obss_mask = []
actions = []
stepwise_returns = []
rtgs = []
done_idxs = []
timesteps = []
metas = []
actions_ = []
num_numa = exp_config.machine.numa_node
num_worker_per_numa = exp_config.machine.worker_per_numa
lbound_core = 6 # 100 / 56
lbound_numa = 13 # 100/8
chassis_dimx = exp_config.chassis_dim[0]
chassis_dimy = exp_config.chassis_dim[1]
# TODO: Return timesteps, resolve the last state thingy
# _ = each full sample: we are breaking it into timesteps
cores_position = exp_config.machine.worker_to_chassis_pos_mapping
for _ in range(idx_array.shape[0]): # tr
cfg_ = idx_array[_][0]
wl_ = idx_array[_][2]
# print(cfg_)
# Load the actions (how many for each complete row? = no of grid cells)
act_ = load_actions(exp_config, cfg_, wl_)
actions.append(act_)
if not(exp_config.num_meta_features == 0):
# Load the meta mc_data
metas.append(mc_tput[_])
# TODO:
# obs_mask_core = np.full((num_numa * num_worker_per_numa, ), lbound_core)
# obs_mask_numa = np.full((num_numa,), lbound_numa)
numa_machine_obs = np.full((chassis_dimx * chassis_dimy, ), False)
numa_machine_obs_s = np.full((chassis_dimx * chassis_dimy, exp_config.num_features+1), 0, dtype=np.float64)
"""Update the obss mask: mask should be where you should not put
Which one to off?
"""
# numa_machine_obss_mask = np.full((chassis_dimx * chassis_dimy,), True) # it should be false
# obs_mask_core = np.full((chassis_dimx * chassis_dimy, ), 3)
# obs_mask_core = np.full((chassis_dimx * chassis_dimy, ), 0)
# obs_mask_core[np.array(act_).astype(int)] = 1
numa_machine_obss_mask = np.full((chassis_dimx * chassis_dimy,), False)
obs_mask_core = np.full((chassis_dimx * chassis_dimy, ), 0)
chassis_act_=[int(cores_position[int(z)]) for z in range(exp_config.machine.num_worker)]
obs_mask_core[np.array(chassis_act_).astype(int)] = lbound_core
mask_already_full = np.where(obs_mask_core==0)
numa_machine_obss_mask[mask_already_full] = True
st_return = np.full((act_.shape[0], ), 0)
tg_return = np.full((act_.shape[0]+1, ), 0)
# max_tput_dset = orginal_max_tput_dset
max_tput_dset = query_throughput_numa[_]
tg_return[0] = max_tput_dset
obss.append(copy.deepcopy(numa_machine_obs)) # empty obs
obss_s.append(copy.deepcopy(numa_machine_obs_s))
obss_mask.append(copy.deepcopy(numa_machine_obss_mask))
flag = False
i=0
actions_individual = np.zeros_like(act_)
for i in range(act_.shape[0]): # each timestep
# since we already refine it to 0 - 63, we do not need it
# map_idx=exp_config.machine.li_worker.index(int(act_[i]))
# a = exp_config.machine.worker_to_chassis_pos_mapping[map_idx]
a = int(cores_position[int(act_[i])])
actions_individual[i] = a
"""Update the obss mask: mask should be where you should not put
Which one to off?
"""
numa_machine_obss_mask = np.full((chassis_dimx * chassis_dimy,), False)
obs_mask_core = np.full((chassis_dimx * chassis_dimy, ), 0)
chassis_act_=[int(cores_position[int(z)]) for z in range(exp_config.machine.num_worker)]
obs_mask_core[np.array(chassis_act_).astype(int)] = lbound_core
mask_already_full = np.where(obs_mask_core==0)
numa_machine_obss_mask[mask_already_full] = True
st_return[i] = query_throughput[_][i]
# tg_return[0] should be the max value and equal across
if i != act_.shape[0]-1:
tg_return[i+1] = max_tput_dset - query_throughput[_][i]