def analysis_step(self, iteration, instance):
'''
function : Perform CoCo Analysis on the output of the simulation from the current iteration. Using the .ncdf
files generated in all the instance, generate the .crd file to be used in the next simulation.
coco :-
Purpose : Runs CoCo analysis on a set of .ncdf files and generates a coordinate file.
Arguments : --grid = Number of points along each dimension of the CoCo histogram
--dims = The number of projections to consider from the input pcz file
--frontpoints = Number of CUs
--topfile = Topology filename
--mdfile = MD Input filename
--output = Output filename
--cycle = Current iteration number
'''
k1 = Kernel(name="md.coco")
k1.arguments = ["--grid={0}".format(Kconfig.grid),
"--dims={0}".format(Kconfig.dims),
"--frontpoints={0}".format(Kconfig.num_CUs),
"--topfile={0}".format(os.path.basename(Kconfig.top_file)),
"--mdfile=*.ncdf",
"--output=pentaopt%s"%(iteration)]
k1.cores = RPconfig.PILOTSIZE
k1.copy_input_data = ['$PRE_LOOP/{0}'.format(os.path.basename(Kconfig.top_file)),'$PRE_LOOP/pycoco.py > pycoco.py']
for iter in range(1,iteration+1):
for i in range(1,Kconfig.num_CUs+1):
k1.copy_input_data = k1.copy_input_data + ['$SIMULATION_ITERATION_{0}_INSTANCE_{1}/md{0}.ncdf > md_{0}_{1}.ncdf'.format(iter,i)]
temp=list()
for i in range(0,Kconfig.num_CUs):
temp = temp + ['pentaopt{0}{1}.pdb > $PRE_LOOP/iter{0}/pentaopt{0}{1}.pdb'.format(iteration,i)]
k1.copy_output_data = temp
k2 = Kernel(name="md.tleap")
k2.arguments = ["--numofsims={0}".format(Kconfig.num_CUs),
"--cycle={0}".format(iteration)]
temp=list()
for i in range(0,Kconfig.num_CUs):
temp = temp + ['$PRE_LOOP/iter{0}/pentaopt{0}{1}.pdb > pentaopt{0}{1}.pdb'.format(iteration,i)]
k2.link_input_data=temp + ['$PRE_LOOP/postexec.py']
temp=list()
for i in range(0,Kconfig.num_CUs):
temp = temp + ['min{0}{1}.crd > $PRE_LOOP/iter{2}/min{0}{1}.crd'.format(iteration,i,iteration+1)]
k2.copy_output_data = temp
return [k1,k2]
def step_2(self, instances):
k = Kernel(name="misc.cat")
k.upload_input_data = ['./output_file_2.txt > file2.txt']
k.copy_input_data = ['$STEP_1/temp.txt > file1.txt']
k.arguments = ["--file1=file1.txt", "--file2=file2.txt"]
k.download_output_data = ['./file1.txt > output_file.txt']
return k
def stage_2(self, instances): k = Kernel(name="misc.cat") k.upload_input_data = ['./output_file_2.txt > file2.txt'] k.copy_input_data = ['$STAGE_1/temp.txt > file1.txt'] k.arguments = ["--file1=file1.txt","--file2=file2.txt"] k.download_output_data = ['./file1.txt > output_file.txt'] return k
def stage_2(self, instance):
k = Kernel(name="misc.ccount")
k.arguments = [
"--inputfile=asciifile-{0}.dat".format(instance),
"--outputfile=cfreqs-{0}.dat".format(instance)
]
k.copy_input_data = "$STAGE_1/asciifile-{0}.dat".format(instance)
k.download_output_data = "cfreqs-{0}.dat".format(instance)
return k
def step_1(self, instance):
k = Kernel(name="misc.chksum")
k.arguments = [
"--inputfile={0}".format(self._checksum_inputfile),
"--outputfile={0}".format(self._download_output)
]
k.copy_input_data = self._copy_directives
k.download_output_data = self._download_output
return k
def pre_loop(self):
'''
function : transfers input files and intermediate executables
pre_grlsd_loop :-
Purpose : Transfers files, Split the input file into smaller files to be used by each of the
gromacs instances in the first iteration.
Arguments : --inputfile = file to be split
--numCUs = number of simulation instances/ number of smaller files
'''
k = Kernel(name="custom.pre_grlsd_loop")
k.copy_input_data = ['$SHARED/spliter.py','$SHARED/gro.py','$SHARED/{0}'.format(os.path.basename(Kconfig.md_input_file))]
k.arguments = ["--inputfile={0}".format(os.path.basename(Kconfig.md_input_file))]
return k
def stage_1(self, instance):
global INPUT_PAR
global ENSEMBLE_SIZE
# "simulation" tasks
if instance <= ENSEMBLE_SIZE:
k1 = Kernel(name="sleep")
k1.arguments = ["--file=output.txt", "--text=simulation",
"--duration={0}".format(INPUT_PAR_Q[instance - 1])]
k1.cores = 1
# File staging can be added using the following
# k1.upload_input_data = []
# k1.copy_input_data = []
# k1.link_input_data = []
# k1.copy_output_data = []
# k1.download_output_data = []
return k1
# "analysis" task
else:
# Emulating some more analysis executin time
sleep(10)
# Analysis kernel produces a random integer (<20) to push into INPUT_PAR_Q
m1 = Kernel(name="randval")
m1.arguments = ["--upperlimit=20"]
m1.copy_input_data = []
# Copy simulation output data
for inst in range(1, ENSEMBLE_SIZE + 1):
m1.copy_input_data += [
'$ITER_{0}_STAGE_1_TASK_{1}/output.txt'.format(
ITER[instance - 1], inst)]
return m1
def pre_loop(self):
'''
function : transfers input files and intermediate executables
pre_grlsd_loop :-
Purpose : Transfers files, Split the input file into smaller files to be used by each of the
gromacs instances in the first iteration.
Arguments : --inputfile = file to be split
--numCUs = number of simulation instances/ number of smaller files
'''
k = Kernel(name="custom.pre_grlsd_loop")
k.copy_input_data = [
'$SHARED/spliter.py', '$SHARED/gro.py',
'$SHARED/{0}'.format(os.path.basename(Kconfig.md_input_file))
]
k.arguments = [
"--inputfile={0}".format(os.path.basename(Kconfig.md_input_file))
]
return k
def step_1(self, instance):
k = Kernel(name="misc.chksum")
k.arguments = ["--inputfile={0}".format(self._checksum_inputfile), "--outputfile={0}".format(self._download_output)]
k.copy_input_data = self._copy_directives
k.download_output_data = self._download_output
return k
def stage_2(self, instance):
k = Kernel(name="misc.ccount")
k.arguments = ["--inputfile=asciifile-{0}.dat".format(instance), "--outputfile=cfreqs-{0}.dat".format(instance)]
k.copy_input_data = "$STAGE_1/asciifile-{0}.dat".format(instance)
k.download_output_data = "cfreqs-{0}.dat".format(instance)
return k
def step_1(self, instance):
k = Kernel(name="misc.chksum")
k.arguments = ["--inputfile=input.txt", "--outputfile=checksum.txt"]
k.copy_input_data = ["/etc/passwd > input.txt"]
k.copy_output_data = self._output_copy_directives
return k
def step_1(self, instance):
k = Kernel(name="misc.chksum")
k.arguments = ["--inputfile=input.txt", "--outputfile=checksum.txt"]
k.copy_input_data = ["/etc/passwd > input.txt"]
k.copy_output_data = self._output_copy_directives
return k