scalability_urchin.py

import pyopencl as cl
from sympy.core.cache import clear_cache
import numpy as np
from pytential import bind, sym
from sumpy.kernel import LaplaceKernel
from pytential.qbx import QBXLayerPotentialSource
from pytential.qbx.cost import CLQBXCostModel
from pytential.qbx.distributed import DistributedQBXLayerPotentialSource
from mpi4py import MPI
import logging
import os
import warnings
import functools
import time
import pickle

# Parameters
k = 0
qbx_order = 3
fmm_order = 10
_expansion_stick_out_factor = 0.5

# Configure logging
logger = logging.getLogger(__name__)

logging.basicConfig(level=os.environ.get("LOGLEVEL", "WARNING"))
logging.getLogger("pytential.qbx.distributed").setLevel(logging.INFO)
logging.getLogger("pytential.qbx.perf_model").setLevel(logging.INFO)
logging.getLogger("boxtree.distributed.local_tree").setLevel(logging.INFO)
logging.getLogger("boxtree.distributed.local_traversal").setLevel(logging.INFO)
logging.getLogger("boxtree.distributed.calculation").setLevel(logging.INFO)
logging.getLogger(__name__).setLevel(logging.INFO)

warnings.filterwarnings("ignore", category=UserWarning)

# Disable sympy cache
clear_cache()

# Setup PyOpenCL
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)


class GreenExpr(object):
    zero_op_name = "green"

    def get_zero_op(self, kernel, **knl_kwargs):

        u_sym = sym.var("u")
        dn_u_sym = sym.var("dn_u")

        return (
            sym.S(kernel, dn_u_sym, qbx_forced_limit=-1, **knl_kwargs)
            - sym.D(kernel, u_sym, qbx_forced_limit="avg", **knl_kwargs)
            - 0.5*u_sym)

    order_drop = 0


def get_urchin_mesh(m, n, order, est_rel_interp_tolerance):
    from meshmode.mesh.generation import refine_mesh_and_get_urchin_warper
    refiner, warp_mesh = refine_mesh_and_get_urchin_warper(
        order, m, n, est_rel_interp_tolerance
    )

    return refiner.get_current_mesh()


def replicate_along_axes(mesh, shape, sep_ratio):
    """
    :arg shape: Replication shape, e.g. (1, 2, 1) for 2 copies along y axis
    :arg sep_ratio: Distance between copies as a ratio of the bounding box size
    """
    from meshmode.mesh.processing import (
            find_bounding_box, affine_map, merge_disjoint_meshes)

    bbox = find_bounding_box(mesh)
    sizes = bbox[1] - bbox[0]

    meshes = [mesh]

    for i in range(mesh.ambient_dim):
        for j in range(1, shape[i]):
            vec = np.zeros(mesh.ambient_dim)
            vec[i] = j * sizes[i] * (1 + sep_ratio)
            meshes.append(affine_map(mesh, A=None, b=vec))

        # FIXME: https://gitlab.tiker.net/inducer/pytential/issues/6
        mesh = merge_disjoint_meshes(meshes, single_group=True)
        meshes = [mesh]

    return mesh


def generate_expr(m, n, target_order, est_rel_interp_tolerance, lp_source_contructor,
                  replicate_parameters=None):
    expr = GreenExpr()

    mesh = get_urchin_mesh(m, n, target_order, est_rel_interp_tolerance)
    if replicate_parameters is not None:
        mesh = replicate_along_axes(mesh, *replicate_parameters)
    d = mesh.ambient_dim

    lap_k_sym = LaplaceKernel(d)
    k_sym = lap_k_sym
    knl_kwargs = {}

    from meshmode.discretization import Discretization
    from meshmode.discretization.poly_element import \
        InterpolatoryQuadratureSimplexGroupFactory

    pre_density_discr = Discretization(
        ctx, mesh,
        InterpolatoryQuadratureSimplexGroupFactory(target_order)
    )

    refiner_extra_kwargs = {}

    qbx, _ = lp_source_contructor(
        pre_density_discr, 4 * target_order,
        qbx_order,
        fmm_order=fmm_order,
        _expansions_in_tree_have_extent=True,
        _expansion_stick_out_factor=_expansion_stick_out_factor,
    ).with_refinement(**refiner_extra_kwargs)

    density_discr = qbx.density_discr

    # {{{ compute values of a solution to the PDE

    nodes_host = density_discr.nodes().get(queue)
    normal = bind(density_discr, sym.normal(d))(queue).as_vector(np.object)
    normal_host = [normal[j].get() for j in range(d)]

    center = np.array([3, 1, 2])[:d]
    diff = nodes_host - center[:, np.newaxis]
    dist_squared = np.sum(diff ** 2, axis=0)
    dist = np.sqrt(dist_squared)
    if d == 2:
        u = np.log(dist)
        grad_u = diff / dist_squared
    elif d == 3:
        u = 1 / dist
        grad_u = -diff / dist ** 3
    else:
        assert False

    dn_u = 0
    for i in range(d):
        dn_u = dn_u + normal_host[i] * grad_u[i]

    # }}}

    u_dev = cl.array.to_device(queue, u)
    dn_u_dev = cl.array.to_device(queue, dn_u)
    grad_u_dev = cl.array.to_device(queue, grad_u)

    bound_op = bind(qbx, expr.get_zero_op(k_sym, **knl_kwargs))

    return bound_op, {'u': u_dev, 'dn_u': dn_u_dev, 'grad_u': grad_u_dev, 'k': k}


def train():
    training_cases = [
        (2, 3, 5, 0.01),  # dummy run
        (3, 5, 5, 0.01),
        (3, 5, 5, 0.01),
        (4, 8, 5, 0.01),
        (4, 8, 5, 0.01)
        # (8, 15, 5, 0.01) # 2m
        # (10, 16, 5, 0.01) # 2m
        # (4, 8, 5, 0.001),
        # (4, 8, 5, 0.001),
        # (6, 10, 5, 0.001), # 2m
        # (6, 10, 5, 0.001),
        # (8, 12, 5, 0.001),
        # (8, 12, 5, 0.001),
        # (10, 15, 5, 0.001), # 5m
        # (10, 15, 5, 0.001)
        # (20, 30, 5, 0.001) # 17m
        # (30, 50, 5, 0.001) # 33m
        # (40, 70, 5, 0.001) # 72m
    ]

    cost_model = CLQBXCostModel(
        queue, CLQBXCostModel.get_constantone_calibration_params()
    )

    lp_source_contructor = functools.partial(
        QBXLayerPotentialSource,
        fmm_backend='fmmlib',
        cost_model=cost_model
    )

    model_results = []
    timing_results = []

    for icase, (m, n, target_order, est_rel_interp_tolerance) in \
            enumerate(training_cases):
        expr, context = generate_expr(
            m, n, target_order, est_rel_interp_tolerance, lp_source_contructor
        )

        modeled_cost = expr.get_modeled_cost(queue, **context)

        timing_data = {}
        expr.eval(queue, context, timing_data=timing_data)

        if icase != 0:
            for key in modeled_cost:
                model_results.append(modeled_cost[key])
                timing_results.append(timing_data[key])

    calibration_params = cost_model.estimate_calibration_params(
        model_results, timing_results
    )

    return calibration_params


def evaluate(calibration_params):
    # MPI information
    comm = MPI.COMM_WORLD
    current_rank = comm.Get_rank()

    if current_rank == 0:

        cost_model = CLQBXCostModel(queue, calibration_params)

        # {{{ dummy run

        lp_source_contructor = functools.partial(
            DistributedQBXLayerPotentialSource,
            comm,
            cost_model=cost_model
        )

        bound_op, context = generate_expr(
            2, 3, 5, 0.01, lp_source_contructor
        )

        bound_op.eval(queue, context=context)

        # }}}

        comm.barrier()
        logger.info("Dummy run ends!")

        # {{{ actual run

        lp_source_contructor = functools.partial(
            DistributedQBXLayerPotentialSource,
            comm,
            cost_model=cost_model
        )

        generate_expr_start_time = time.time()

        bound_op, context = generate_expr(
            40, 70, 5, 0.001, lp_source_contructor,
            replicate_parameters=((2, 1, 1), 0.05)
        )

        logger.info("Generating geometry/expression elapsed {} secs.".format(
            time.time() - generate_expr_start_time)
        )

        bound_op.eval(queue, context=context)

        # }}}
    else:
        from pytential.qbx.distributed import drive_dfmm

        # {{{ dummy run

        for _ in range(2):
            lp_source = DistributedQBXLayerPotentialSource(comm, None, None)
            distribute_geo_data = lp_source.distibuted_geo_data(
                None, queue, None, None
            )

            weights = None
            drive_dfmm(
                queue, weights, distribute_geo_data, comm=comm
            )

        # }}}

        comm.barrier()

        # {{{ actual run

        for _ in range(2):
            lp_source = DistributedQBXLayerPotentialSource(comm, None, None)
            distribute_geo_data = lp_source.distibuted_geo_data(
                None, queue, None, None
            )

            weights = None
            drive_dfmm(
                queue, weights, distribute_geo_data, comm=comm
            )

        # }}}


if __name__ == '__main__':
    import sys
    if len(sys.argv) != 2:
        raise RuntimeError("Please specify train or eval")

    if sys.argv[1] == 'train':
        params = train()
        pickle.dump(params, open("param", "wb"))
    elif sys.argv[1] == 'eval':
        params = pickle.load(open("param", "rb"))
        evaluate(params)
    else:
        raise RuntimeError("Unrecognized command line argument. Please specify train or eval.")