Skip to content

GitLab

Commit fd7ac130 authored by iker_martin's avatar iker_martin
Browse files

WIP - Refactoring output file reading

parent 626eaa40
Show whitespace changes
Inline Side-by-side
Analysis/MallTimes.py
View file @ fd7ac130
......@@ -12,33 +12,47 @@ class G_enum(Enum):
SDR = 4
ADR = 5
DR = 6
ASYNCH_REDISTRIBUTION_TYPE = 7
SPAWN_METHOD = 8
SPAWN_STRATEGY = 9
GROUPS = 10
FACTOR_S = 11
DIST = 12
STAGE_TYPES = 13
STAGE_TIMES = 14
STAGE_BYTES = 15
ITERS = 16
ASYNCH_ITERS = 17
T_ITER = 18
T_STAGES = 19
T_SPAWN = 20
T_SPAWN_REAL = 21
T_SR = 22
T_AR = 23
T_TOTAL = 24
columnsG = ["Total_Resizes", "Total_Groups", "Total_Stages", "Granularity", "SDR", "ADR", "DR", "Asynch_Redistribution_Type", \
"Spawn_Method", "Spawn_Strategy", "Groups", "Factor_S", "Dist", "Stage_Types", "Stage_Times", "Stage_Bytes", \
"Iters", "Asynch_Iters", "T_iter", "T_stages", "T_spawn", "T_spawn_real", "T_SR", "T_AR", "T_total"] #25
RED_METHOD = 7
RED_STRATEGY = 8
SPAWN_METHOD = 9
SPAWN_STRATEGY = 10
GROUPS = 11
FACTOR_S = 12
DIST = 13
STAGE_TYPES = 14
STAGE_TIMES = 15
STAGE_BYTES = 16
ITERS = 17
ASYNCH_ITERS = 18
T_ITER = 19
T_STAGES = 20
T_SPAWN = 21
T_SPAWN_REAL = 22
T_SR = 23
T_AR = 24
T_TOTAL = 25
#Malleability specific
NP = 0
NC = 1
BAR = 11 # Extract 1 from index
columnsG = ["Total_Resizes", "Total_Groups", "Total_Stages", "Granularity", "SDR", "ADR", "DR", "Redistribution_Method", \
"Redistribution_Strategy", "Spawn_Method", "Spawn_Strategy", "Groups", "FactorS", "Dist", "Stage_Types", "Stage_Times", \
"Stage_Bytes", "Iters", "Asynch_Iters", "T_iter", "T_stages", "T_spawn", "T_spawn_real", "T_SR", "T_AR", "T_total"] #26
columnsM = ["NP", "NC", "Total_Stages", "Granularity", "SDR", "ADR", "DR", "Redistribution_Method", \
"Redistribution_Strategy", "Spawn_Method", "Spawn_Strategy", "FactorS", "Dist", "Stage_Types", "Stage_Times", \
"Stage_Bytes", "Iters", "Asynch_Iters", "T_iter", "T_stages", "T_spawn", "T_spawn_real", "T_SR", "T_AR"] #24
# Obtains the value of a given index in a splited line
# and returns it as a float values
# and returns it as a float values if possible, string otherwise
def get_value(line, index):
return float(line[index].split('=')[1].split(',')[0])
value = line[index].split('=')[1].split(',')[0]
try:
return float(value)
except ValueError:
return value
# Obtains the general parameters of an execution and
# stores them for creating a global dataframe
......@@ -53,8 +67,7 @@ def record_config_line(lineS, dataG_it):
index = ordered_indexes[i]
dataG_it[index] = value
dataG_it[G_enum.TOTAL_GROUPS.value] = dataG_it[G_enum.TOTAL_RESIZES.value]
dataG_it[G_enum.TOTAL_RESIZES.value] -=1 #FIXME Modificar en App sintetica
dataG_it[G_enum.TOTAL_GROUPS.value] = dataG_it[G_enum.TOTAL_RESIZES.value]+1
#FIXME Modificar cuando ADR ya no sea un porcentaje
dataG_it[G_enum.DR.value] = dataG_it[G_enum.SDR.value] + dataG_it[G_enum.ADR.value]
......@@ -62,9 +75,9 @@ def record_config_line(lineS, dataG_it):
# Init lists for each column
array_groups = [G_enum.GROUPS.value, G_enum.FACTOR_S.value, G_enum.DIST.value, G_enum.ITERS.value, \
G_enum.ASYNCH_ITERS.value, G_enum.T_ITER.value, G_enum.T_STAGES.value]
array_resizes = [G_enum.ASYNCH_REDISTRIBUTION_TYPE.value, G_enum.SPAWN_METHOD.value, \
G_enum.SPAWN_STRATEGY.value, G_enum.T_SPAWN.value, G_enum.T_SPAWN_REAL.value, \
G_enum.T_SR.value, G_enum.T_AR.value]
array_resizes = [G_enum.REDISTRIBUTION_METHOD.value, G_enum.REDISTRIBUTION_METHOD.value,
G_enum.SPAWN_METHOD.value, G_enum.SPAWN_STRATEGY.value, G_enum.T_SPAWN.value, \
G_enum.T_SPAWN_REAL.value, G_enum.T_SR.value, G_enum.T_AR.value]
array_stages = [G_enum.STAGE_TYPES.value, \
G_enum.STAGE_TIMES.value, G_enum.STAGE_BYTES.value]
for index in array_groups:
......@@ -88,7 +101,7 @@ def record_stage_line(lineS, dataG_it, stage):
value = get_value(lineS, i+offset_lines)
if value.is_integer():
value = int(value)
index = array_stage[i]
index = array_stages[i]
dataG_it[index][stage] = value
# Obtains the parameters of a resize line
......@@ -96,10 +109,10 @@ def record_stage_line(lineS, dataG_it, stage):
# Is needed to indicate to which group refers
# the resize line
def record_resize_line(lineS, dataG_it, group):
array_stages = [G_enum.ITERS.value, G_enum.GROUPS.value, G_enum.FACTOR_S.value, G_enum.DIST.value, \
G_enum.ASYNCH_REDISTRIBUTION_TYPE.value, G_enum.SPAWN_METHOD.value, G_enum.SPAWN_STRATEGY.value]
array_groups = [G_enum.ITERS.value, G_enum.GROUPS.value, G_enum.FACTOR_S.value, G_enum.DIST.value, \
G_enum.REDISTRIBUTION_METHOD.value, G_enum.REDISTRIBUTION_STRATEGY.value, G_enum.SPAWN_METHOD.value, G_enum.SPAWN_STRATEGY.value]
offset_lines = 2
for i in range(len(array_stages)):
for i in range(len(array_groups)):
value = get_value(lineS, i+offset_lines)
if value.is_integer():
value = int(value)
......@@ -113,13 +126,25 @@ def record_time_line(lineS, dataG_it):
return
index = T_names.index(linesS[0])
index = T_values[index]
offset_lines = 1
for i in range(len(dataG_it[index])):
value = get_value(lineS, i+offset_lines)
dataG_it[index][i] = value
dataG_it[index][i] = get_value(lineS, i+offset_lines)
def record_multiple_times_line(lineS, dataG_it, ):
T_values = [G_enum.T_SPAWN.value, G_enum.T_SPAWN_REAL.value, G_enum.T_SR.value, G_enum.T_AR.value, G_enum.T_TOTAL.value]
if not (lineS[0] in T_names): # Execute only if line represents a Time
return
groups = dataG_it[G_enum.TOTAL_GROUPS.value]
index = T_names.index(linesS[0])
index = T_values[index]
offset_lines = 1
for i in range(len(dataG_it[index])):
#-----------------------------------------------
def read_global_file(f, dataG, it):
def read_local_file(f, dataG, it):
resizes = 0
timer = 0
previousNP = 0
......@@ -130,9 +155,7 @@ def read_global_file(f, dataG, it):
if len(lineS) > 0:
if lineS[0] == "Config": # CONFIG LINE
it += 1
dataG.append([None]*(25+1))
#dataG[it][-1] = None Indicates if local data has been recorded(1) or not(None)
record_config(lineS, dataG[it])
record_config(lineS, dataG[it], dataM[it])
resize = 0
stage = 0
......@@ -151,10 +174,8 @@ def read_global_file(f, dataG, it):
return it
#-----------------------------------------------
def read_local_file(f, dataG, it):
resizes = 0
timer = 0
previousNP = 0
def read_global_file(f, dataG, it):
run = -1
for line in f:
lineS = line.split()
......@@ -162,9 +183,12 @@ def read_local_file(f, dataG, it):
if len(lineS) > 0:
if lineS[0] == "Config": # CONFIG LINE
it += 1
record_config(lineS, dataG[it], dataM[it])
nonlocal columnsG
dataG.append([None]*len(columnsG))
record_config(lineS, dataG[it])
resize = 0
stage = 0
run += 1
elif lineS[0] == "Stage":
record_stage_line(lineS, dataG[it], stage)
......@@ -172,34 +196,30 @@ def read_local_file(f, dataG, it):
elif lineS[0] == "Resize":
record_resize_line(lineS, dataG[it], resize)
resize+=1
elif lineS[0] == "T_total:":
value = get_value(lineS, 1)
dataG[it][G_enum.T_TOTAL.value] = value
else:
record_time_line(lineS, dataG[it])
return it
#columnsG = ["Total_Resizes", "Total_Groups", "Total_Stages", "Granularity", "SDR", "ADR", "DR", "Asynch_Redistribution_Type", \\
# "Spawn_Method", "Spawn_Strategy", "Groups", "Dist", "Stage_Types", "Stage_Times", "Stage_Bytes", \\
# "Iters", "Asynch_Iters", "T_iter", "T_stages", "T_spawn", "T_spawn_real", "T_SR", "T_AR", "T_total"] #24
read_local_file(dataG[it])
return it
#-----------------------------------------------
if len(sys.argv) < 2:
print("The files name is missing\nUsage: python3 iterTimes.py resultsName directory csvOutName")
print("The files name is missing\nUsage: python3 MallTimes.py resultsName directory csvOutName")
exit(1)
if len(sys.argv) >= 3:
BaseDir = sys.argv[2]
print("Searching in directory: "+ BaseDir)
else:
BaseDir = sys.argv[2]
BaseDir = "./"
if len(sys.argv) >= 4:
print("Csv name will be: " + sys.argv[3] + "G.csv & " + sys.argv[3] + "M.csv")
name = sys.argv[3]
else:
name = "data"
print("Csv name will be: " + name + "G.csv & " + name + "M.csv")
insideDir = "Run"
lista = glob.glob("./" + BaseDir + insideDir + "*/" + sys.argv[1]+ "*Global.o*")
......@@ -225,5 +245,5 @@ dfM = pd.DataFrame(dataM, columns=columnsM)
#Poner en TC el valor real y en TH el necesario para la app
cond = dfM.TH != 0
dfM.loc[cond, ['TC', 'TH']] = dfB.loc[cond, ['TH', 'TC']].values
dfM.loc[cond, ['TC', 'TH']] = dfM.loc[cond, ['TH', 'TC']].values
dfM.to_csv(name + 'M.csv')
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment