mirror of
https://github.com/smyalygames/FiniteVolumeGPU.git
synced 2025-05-18 14:34:13 +02:00
Refactoring / cleanup
This commit is contained in:
André R. Brodtkorb
parent
f9f0f20df8
commit
b266567d09
@ -104,6 +104,8 @@ class MagicLogger(Magics):
|
||||
|
||||
@line_magic
|
||||
@magic_arguments.magic_arguments()
|
||||
@magic_arguments.argument(
|
||||
'name', type=str, help='Name of context to create')
|
||||
@magic_arguments.argument(
|
||||
'--out', '-o', type=str, default='output.log', help='The filename to store the log to')
|
||||
@magic_arguments.argument(
|
||||
@ -146,6 +148,7 @@ class MagicLogger(Magics):
|
||||
logger.addHandler(fh)
|
||||
|
||||
logger.info("Python version %s", sys.version)
|
||||
self.shell.user_ns[args.name] = logger
|
||||
|
||||
|
||||
|
||||
|
||||
@ -25,168 +25,29 @@ from GPUSimulators import Simulator
|
||||
import numpy as np
|
||||
from mpi4py import MPI
|
||||
|
||||
class MPISimulator(Simulator.BaseSimulator):
|
||||
def __init__(self, sim, comm):
|
||||
|
||||
|
||||
|
||||
class MPIGrid(object):
|
||||
"""
|
||||
Class which represents an MPI grid of nodes. Facilitates easy communication between
|
||||
neighboring nodes
|
||||
"""
|
||||
def __init__(self, comm, ndims=2):
|
||||
self.logger = logging.getLogger(__name__)
|
||||
|
||||
autotuner = sim.context.autotuner
|
||||
sim.context.autotuner = None;
|
||||
super().__init__(sim.context,
|
||||
sim.nx, sim.ny,
|
||||
sim.dx, sim.dy,
|
||||
sim.cfl_scale,
|
||||
sim.num_substeps,
|
||||
sim.block_size[0], sim.block_size[1])
|
||||
sim.context.autotuner = autotuner
|
||||
assert ndims == 2, "Unsupported number of dimensions. Must be two at the moment"
|
||||
assert comm.size >= 1, "Must have at least one node"
|
||||
|
||||
self.sim = sim
|
||||
self.grid = MPIGrid.getGrid(comm.size, ndims)
|
||||
self.comm = comm
|
||||
self.rank = comm.rank
|
||||
|
||||
#Get global dimensions
|
||||
self.grid = MPISimulator.getFactors(self.comm.size, 2)
|
||||
self.logger.debug("Created MPI grid: {:}. Rank {:d} has coordinate {:}".format(
|
||||
self.grid, self.comm.rank, self.getCoordinate()))
|
||||
|
||||
#Get neighbor node ids
|
||||
self.east = self.getEast()
|
||||
self.west = self.getWest()
|
||||
self.north = self.getNorth()
|
||||
self.south = self.getSouth()
|
||||
|
||||
#Get local dimensions
|
||||
self.gc_x = int(self.sim.u0[0].x_halo)
|
||||
self.gc_y = int(self.sim.u0[0].y_halo)
|
||||
self.nx = int(self.sim.nx)
|
||||
self.ny = int(self.sim.ny)
|
||||
self.nvars = len(self.sim.u0.gpu_variables)
|
||||
|
||||
#Allocate data for receiving
|
||||
#Note that east and west also transfer ghost cells
|
||||
#whilst north/south only transfer internal cells
|
||||
self.in_e = np.empty((self.nvars, self.ny + 2*self.gc_y, self.gc_x), dtype=np.float32)
|
||||
self.in_w = np.empty((self.nvars, self.ny + 2*self.gc_y, self.gc_x), dtype=np.float32)
|
||||
self.in_n = np.empty((self.nvars, self.gc_y, self.nx), dtype=np.float32)
|
||||
self.in_s = np.empty((self.nvars, self.gc_y, self.nx), dtype=np.float32)
|
||||
|
||||
#Allocate data for sending
|
||||
self.out_e = np.empty_like(self.in_e)
|
||||
self.out_w = np.empty_like(self.in_w)
|
||||
self.out_n = np.empty_like(self.in_n)
|
||||
self.out_s = np.empty_like(self.in_s)
|
||||
|
||||
#Set regions for ghost cells to read from
|
||||
self.read_e = np.array([ self.nx, 0, self.gc_x, self.ny + 2*self.gc_y])
|
||||
self.read_w = np.array([self.gc_x, 0, self.gc_x, self.ny + 2*self.gc_y])
|
||||
self.read_n = np.array([self.gc_x, self.ny, self.nx, self.gc_y])
|
||||
self.read_s = np.array([self.gc_x, self.gc_y, self.nx, self.gc_y])
|
||||
|
||||
#Set regions for ghost cells to write to
|
||||
self.write_e = self.read_e + np.array([self.gc_x, 0, 0, 0])
|
||||
self.write_w = self.read_w - np.array([self.gc_x, 0, 0, 0])
|
||||
self.write_n = self.read_n + np.array([0, self.gc_y, 0, 0])
|
||||
self.write_s = self.read_s - np.array([0, self.gc_y, 0, 0])
|
||||
|
||||
self.logger.debug("Simlator rank {:d} created ".format(self.rank))
|
||||
|
||||
|
||||
def substep(self, dt, step_number):
|
||||
self.exchange()
|
||||
self.sim.substep(dt, step_number)
|
||||
|
||||
def download(self):
|
||||
return self.sim.download()
|
||||
|
||||
def synchronize(self):
|
||||
self.sim.synchronize()
|
||||
|
||||
def check(self):
|
||||
return self.sim.check()
|
||||
|
||||
def computeDt(self):
|
||||
local_dt = np.array([np.float32(self.sim.computeDt())]);
|
||||
global_dt = np.empty(1, dtype=np.float32)
|
||||
self.comm.Allreduce(local_dt, global_dt, op=MPI.MIN)
|
||||
self.logger.debug("Local dt: {:f}, global dt: {:f}".format(local_dt[0], global_dt[0]))
|
||||
return global_dt[0]
|
||||
|
||||
def exchange(self):
|
||||
#Shorthands for dimensions
|
||||
gc_x = self.gc_x
|
||||
gc_y = self.gc_y
|
||||
nx = self.nx
|
||||
ny = self.ny
|
||||
|
||||
####
|
||||
# First transfer internal cells north-south
|
||||
####
|
||||
|
||||
#Download from the GPU
|
||||
for k in range(self.nvars):
|
||||
self.sim.u0[k].download(self.sim.stream, cpu_data=self.out_n[k,:,:], async=True, extent=self.read_n)
|
||||
self.sim.u0[k].download(self.sim.stream, cpu_data=self.out_s[k,:,:], async=True, extent=self.read_s)
|
||||
self.sim.stream.synchronize()
|
||||
|
||||
#Send to north/south neighbours
|
||||
comm_send = []
|
||||
comm_send += [self.comm.Isend(self.out_n, dest=self.north, tag=4*self.nt + 0)]
|
||||
comm_send += [self.comm.Isend(self.out_s, dest=self.south, tag=4*self.nt + 1)]
|
||||
|
||||
#Receive from north/south neighbors
|
||||
comm_recv = []
|
||||
comm_recv += [self.comm.Irecv(self.in_s, source=self.south, tag=4*self.nt + 0)]
|
||||
comm_recv += [self.comm.Irecv(self.in_n, source=self.north, tag=4*self.nt + 1)]
|
||||
|
||||
#Wait for incoming transfers to complete
|
||||
for comm in comm_recv:
|
||||
comm.wait()
|
||||
|
||||
#Upload to the GPU
|
||||
for k in range(self.nvars):
|
||||
self.sim.u0[k].upload(self.sim.stream, self.in_n[k,:,:], extent=self.write_n)
|
||||
self.sim.u0[k].upload(self.sim.stream, self.in_s[k,:,:], extent=self.write_s)
|
||||
|
||||
#Wait for sending to complete
|
||||
for comm in comm_send:
|
||||
comm.wait()
|
||||
|
||||
|
||||
|
||||
####
|
||||
# Then transfer east-west including ghost cells that have been filled in by north-south transfer above
|
||||
# Fixme: This can be optimized by overlapping the GPU transfer with the pervious MPI transfer if the corners
|
||||
# har handled on the CPU
|
||||
####
|
||||
|
||||
#Download from the GPU
|
||||
for k in range(self.nvars):
|
||||
self.sim.u0[k].download(self.sim.stream, cpu_data=self.out_e[k,:,:], async=True, extent=self.read_e)
|
||||
self.sim.u0[k].download(self.sim.stream, cpu_data=self.out_w[k,:,:], async=True, extent=self.read_w)
|
||||
self.sim.stream.synchronize()
|
||||
|
||||
#Send to east/west neighbours
|
||||
comm_send = []
|
||||
comm_send += [self.comm.Isend(self.out_e, dest=self.east, tag=4*self.nt + 2)]
|
||||
comm_send += [self.comm.Isend(self.out_w, dest=self.west, tag=4*self.nt + 3)]
|
||||
|
||||
#Receive from east/west neighbors
|
||||
comm_recv = []
|
||||
comm_recv += [self.comm.Irecv(self.in_w, source=self.west, tag=4*self.nt + 2)]
|
||||
comm_recv += [self.comm.Irecv(self.in_e, source=self.east, tag=4*self.nt + 3)]
|
||||
|
||||
#Wait for incoming transfers to complete
|
||||
for comm in comm_recv:
|
||||
comm.wait()
|
||||
|
||||
#Upload to the GPU
|
||||
for k in range(self.nvars):
|
||||
self.sim.u0[k].upload(self.sim.stream, self.in_e[k,:,:], extent=self.write_e)
|
||||
self.sim.u0[k].upload(self.sim.stream, self.in_w[k,:,:], extent=self.write_w)
|
||||
|
||||
#Wait for sending to complete
|
||||
for comm in comm_send:
|
||||
comm.wait()
|
||||
|
||||
|
||||
def getCoordinate(self, rank):
|
||||
def getCoordinate(self, rank=None):
|
||||
if (rank is None):
|
||||
rank = self.comm.rank
|
||||
i = (rank % self.grid[0])
|
||||
j = (rank // self.grid[0])
|
||||
return i, j
|
||||
@ -195,28 +56,32 @@ class MPISimulator(Simulator.BaseSimulator):
|
||||
return j*self.grid[0] + i
|
||||
|
||||
def getEast(self):
|
||||
i, j = self.getCoordinate(self.rank)
|
||||
i, j = self.getCoordinate(self.comm.rank)
|
||||
i = (i+1) % self.grid[0]
|
||||
return self.getRank(i, j)
|
||||
|
||||
def getWest(self):
|
||||
i, j = self.getCoordinate(self.rank)
|
||||
i, j = self.getCoordinate(self.comm.rank)
|
||||
i = (i+self.grid[0]-1) % self.grid[0]
|
||||
return self.getRank(i, j)
|
||||
|
||||
def getNorth(self):
|
||||
i, j = self.getCoordinate(self.rank)
|
||||
i, j = self.getCoordinate(self.comm.rank)
|
||||
j = (j+1) % self.grid[1]
|
||||
return self.getRank(i, j)
|
||||
|
||||
def getSouth(self):
|
||||
i, j = self.getCoordinate(self.rank)
|
||||
i, j = self.getCoordinate(self.comm.rank)
|
||||
j = (j+self.grid[1]-1) % self.grid[1]
|
||||
return self.getRank(i, j)
|
||||
|
||||
def getFactors(number, num_factors):
|
||||
def getGrid(num_nodes, num_dims):
|
||||
assert(isinstance(num_nodes, int))
|
||||
assert(isinstance(num_dims, int))
|
||||
|
||||
# Adapted from https://stackoverflow.com/questions/28057307/factoring-a-number-into-roughly-equal-factors
|
||||
# Original code by https://stackoverflow.com/users/3928385/ishamael
|
||||
# Factorizes a number into n roughly equal factors
|
||||
|
||||
#Dictionary to remember already computed permutations
|
||||
memo = {}
|
||||
@ -253,22 +118,237 @@ class MPISimulator(Simulator.BaseSimulator):
|
||||
memo[(n, left)] = (best, bestTuple)
|
||||
return memo[(n, left)]
|
||||
|
||||
assert(isinstance(number, int))
|
||||
assert(isinstance(num_factors, int))
|
||||
|
||||
factors = dp(number, num_factors)[1]
|
||||
grid = dp(num_nodes, num_dims)[1]
|
||||
|
||||
if (len(factors) < num_factors):
|
||||
if (len(grid) < num_dims):
|
||||
#Split problematic 4
|
||||
if (4 in factors):
|
||||
factors.remove(4)
|
||||
factors.append(2)
|
||||
factors.append(2)
|
||||
if (4 in grid):
|
||||
grid.remove(4)
|
||||
grid.append(2)
|
||||
grid.append(2)
|
||||
|
||||
#Pad with ones to guarantee num_factors
|
||||
factors = factors + [1]*(num_factors - len(factors))
|
||||
#Pad with ones to guarantee num_dims
|
||||
grid = grid + [1]*(num_dims - len(grid))
|
||||
|
||||
#Sort in descending order
|
||||
factors = np.flip(np.sort(factors))
|
||||
grid = np.flip(np.sort(grid))
|
||||
|
||||
return grid
|
||||
|
||||
|
||||
def getExtent(self, width, height, rank):
|
||||
"""
|
||||
Function which returns the extent of node with rank
|
||||
rank in the grid
|
||||
"""
|
||||
i, j = self.getCoordinate(rank)
|
||||
x0 = i * width
|
||||
y0 = j * height
|
||||
x1 = x0+width
|
||||
y1 = y0+height
|
||||
return [x0, x1, y0, y1]
|
||||
|
||||
|
||||
def gatherData(self, data, rank=0):
|
||||
"""
|
||||
Function which gathers the data onto node with rank
|
||||
rank
|
||||
"""
|
||||
#Get shape of data
|
||||
ny, nx = data.shape
|
||||
|
||||
#Create list of buffers to return
|
||||
retval = []
|
||||
|
||||
#If we are the target node, recieve from others
|
||||
#otherwise send to target
|
||||
if (self.comm.rank == rank):
|
||||
mpi_requests = []
|
||||
retval = []
|
||||
|
||||
#Loop over all nodes
|
||||
for k in range(0, self.comm.size):
|
||||
#If k equal target node, add our own data
|
||||
#Otherwise receive it from node k
|
||||
if (k == rank):
|
||||
retval += [data]
|
||||
else:
|
||||
buffer = np.empty((ny, nx), dtype=np.float32)
|
||||
retval += [buffer]
|
||||
mpi_requests += [self.comm.Irecv(buffer, source=k, tag=k)]
|
||||
|
||||
#Wait for transfers to complete
|
||||
for mpi_request in mpi_requests:
|
||||
mpi_request.wait()
|
||||
else:
|
||||
mpi_request = self.comm.Isend(data, dest=rank, tag=self.comm.rank)
|
||||
mpi_request.wait()
|
||||
|
||||
return retval
|
||||
|
||||
|
||||
|
||||
class MPISimulator(Simulator.BaseSimulator):
|
||||
"""
|
||||
Class which handles communication between simulators on different MPI nodes
|
||||
"""
|
||||
def __init__(self, sim, grid):
|
||||
self.logger = logging.getLogger(__name__)
|
||||
|
||||
autotuner = sim.context.autotuner
|
||||
sim.context.autotuner = None;
|
||||
super().__init__(sim.context,
|
||||
sim.nx, sim.ny,
|
||||
sim.dx, sim.dy,
|
||||
sim.cfl_scale,
|
||||
sim.num_substeps,
|
||||
sim.block_size[0], sim.block_size[1])
|
||||
sim.context.autotuner = autotuner
|
||||
|
||||
self.sim = sim
|
||||
self.grid = grid
|
||||
|
||||
#Get neighbor node ids
|
||||
self.east = grid.getEast()
|
||||
self.west = grid.getWest()
|
||||
self.north = grid.getNorth()
|
||||
self.south = grid.getSouth()
|
||||
|
||||
#Get number of variables
|
||||
self.nvars = len(self.sim.u0.gpu_variables)
|
||||
|
||||
#Shorthands for computing extents and sizes
|
||||
gc_x = int(self.sim.u0[0].x_halo)
|
||||
gc_y = int(self.sim.u0[0].y_halo)
|
||||
nx = int(self.sim.nx)
|
||||
ny = int(self.sim.ny)
|
||||
|
||||
#Set regions for ghost cells to read from
|
||||
#These have the format [x0, y0, width, height]
|
||||
self.read_e = np.array([ nx, 0, gc_x, ny + 2*gc_y])
|
||||
self.read_w = np.array([gc_x, 0, gc_x, ny + 2*gc_y])
|
||||
self.read_n = np.array([gc_x, ny, nx, gc_y])
|
||||
self.read_s = np.array([gc_x, gc_y, nx, gc_y])
|
||||
|
||||
#Set regions for ghost cells to write to
|
||||
self.write_e = self.read_e + np.array([gc_x, 0, 0, 0])
|
||||
self.write_w = self.read_w - np.array([gc_x, 0, 0, 0])
|
||||
self.write_n = self.read_n + np.array([0, gc_y, 0, 0])
|
||||
self.write_s = self.read_s - np.array([0, gc_y, 0, 0])
|
||||
|
||||
#Allocate data for receiving
|
||||
#Note that east and west also transfer ghost cells
|
||||
#whilst north/south only transfer internal cells
|
||||
#Reuses the width/height defined in the read-extets above
|
||||
self.in_e = np.empty((self.nvars, self.read_e[3], self.read_e[2]), dtype=np.float32)
|
||||
self.in_w = np.empty((self.nvars, self.read_w[3], self.read_w[2]), dtype=np.float32)
|
||||
self.in_n = np.empty((self.nvars, self.read_n[3], self.read_n[2]), dtype=np.float32)
|
||||
self.in_s = np.empty((self.nvars, self.read_s[3], self.read_s[2]), dtype=np.float32)
|
||||
|
||||
#Allocate data for sending
|
||||
self.out_e = np.empty_like(self.in_e)
|
||||
self.out_w = np.empty_like(self.in_w)
|
||||
self.out_n = np.empty_like(self.in_n)
|
||||
self.out_s = np.empty_like(self.in_s)
|
||||
|
||||
self.logger.debug("Simlator rank {:d} initialized ".format(self.grid.comm.rank))
|
||||
|
||||
|
||||
def substep(self, dt, step_number):
|
||||
self.exchange()
|
||||
self.sim.substep(dt, step_number)
|
||||
|
||||
def download(self):
|
||||
return self.sim.download()
|
||||
|
||||
def synchronize(self):
|
||||
self.sim.synchronize()
|
||||
|
||||
def check(self):
|
||||
return self.sim.check()
|
||||
|
||||
def computeDt(self):
|
||||
local_dt = np.array([np.float32(self.sim.computeDt())]);
|
||||
global_dt = np.empty(1, dtype=np.float32)
|
||||
self.grid.comm.Allreduce(local_dt, global_dt, op=MPI.MIN)
|
||||
self.logger.debug("Local dt: {:f}, global dt: {:f}".format(local_dt[0], global_dt[0]))
|
||||
return global_dt[0]
|
||||
|
||||
def exchange(self):
|
||||
####
|
||||
# FIXME: This function can be optimized using persitent communications.
|
||||
# Also by overlapping some of the communications north/south and east/west of GPU and intra-node
|
||||
# communications
|
||||
####
|
||||
|
||||
####
|
||||
# First transfer internal cells north-south
|
||||
####
|
||||
|
||||
#Download from the GPU
|
||||
for k in range(self.nvars):
|
||||
self.sim.u0[k].download(self.sim.stream, cpu_data=self.out_n[k,:,:], async=True, extent=self.read_n)
|
||||
self.sim.u0[k].download(self.sim.stream, cpu_data=self.out_s[k,:,:], async=True, extent=self.read_s)
|
||||
self.sim.stream.synchronize()
|
||||
|
||||
#Send to north/south neighbours
|
||||
comm_send = []
|
||||
comm_send += [self.grid.comm.Isend(self.out_n, dest=self.north, tag=4*self.nt + 0)]
|
||||
comm_send += [self.grid.comm.Isend(self.out_s, dest=self.south, tag=4*self.nt + 1)]
|
||||
|
||||
#Receive from north/south neighbors
|
||||
comm_recv = []
|
||||
comm_recv += [self.grid.comm.Irecv(self.in_s, source=self.south, tag=4*self.nt + 0)]
|
||||
comm_recv += [self.grid.comm.Irecv(self.in_n, source=self.north, tag=4*self.nt + 1)]
|
||||
|
||||
#Wait for incoming transfers to complete
|
||||
for comm in comm_recv:
|
||||
comm.wait()
|
||||
|
||||
#Upload to the GPU
|
||||
for k in range(self.nvars):
|
||||
self.sim.u0[k].upload(self.sim.stream, self.in_n[k,:,:], extent=self.write_n)
|
||||
self.sim.u0[k].upload(self.sim.stream, self.in_s[k,:,:], extent=self.write_s)
|
||||
|
||||
#Wait for sending to complete
|
||||
for comm in comm_send:
|
||||
comm.wait()
|
||||
|
||||
|
||||
|
||||
####
|
||||
# Then transfer east-west including ghost cells that have been filled in by north-south transfer above
|
||||
####
|
||||
|
||||
#Download from the GPU
|
||||
for k in range(self.nvars):
|
||||
self.sim.u0[k].download(self.sim.stream, cpu_data=self.out_e[k,:,:], async=True, extent=self.read_e)
|
||||
self.sim.u0[k].download(self.sim.stream, cpu_data=self.out_w[k,:,:], async=True, extent=self.read_w)
|
||||
self.sim.stream.synchronize()
|
||||
|
||||
#Send to east/west neighbours
|
||||
comm_send = []
|
||||
comm_send += [self.grid.comm.Isend(self.out_e, dest=self.east, tag=4*self.nt + 2)]
|
||||
comm_send += [self.grid.comm.Isend(self.out_w, dest=self.west, tag=4*self.nt + 3)]
|
||||
|
||||
#Receive from east/west neighbors
|
||||
comm_recv = []
|
||||
comm_recv += [self.grid.comm.Irecv(self.in_w, source=self.west, tag=4*self.nt + 2)]
|
||||
comm_recv += [self.grid.comm.Irecv(self.in_e, source=self.east, tag=4*self.nt + 3)]
|
||||
|
||||
#Wait for incoming transfers to complete
|
||||
for comm in comm_recv:
|
||||
comm.wait()
|
||||
|
||||
#Upload to the GPU
|
||||
for k in range(self.nvars):
|
||||
self.sim.u0[k].upload(self.sim.stream, self.in_e[k,:,:], extent=self.write_e)
|
||||
self.sim.u0[k].upload(self.sim.stream, self.in_w[k,:,:], extent=self.write_w)
|
||||
|
||||
#Wait for sending to complete
|
||||
for comm in comm_send:
|
||||
comm.wait()
|
||||
|
||||
|
||||
|
||||
return factors
|
||||
Loading…
x
Reference in New Issue
Block a user