implement hierachical matrix solver

examples/scaling-study-hmatrix.py

@@ -0,0 +1,198 @@
+__copyright__ = "Copyright (C) 2022 Alexandru Fikl"
+
+__license__ = """
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+"""
+
+from dataclasses import dataclass
+
+import numpy as np
+
+from meshmode.array_context import PyOpenCLArrayContext
+from pytools.convergence import EOCRecorder
+
+from pytential import GeometryCollection, sym
+
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+@dataclass(frozen=True)
+class Timings:
+    build: float
+    matvec: float
+
+
+def run_hmatrix_matvec(
+        actx: PyOpenCLArrayContext,
+        places: GeometryCollection, *,
+        dofdesc: sym.DOFDescriptor) -> None:
+    from sumpy.kernel import LaplaceKernel
+    kernel = LaplaceKernel(places.ambient_dim)
+    sym_u = sym.var("u")
+    sym_op = -0.5 * sym_u + sym.D(kernel, sym_u, qbx_forced_limit="avg")
+
+    density_discr = places.get_discretization(dofdesc.geometry, dofdesc.discr_stage)
+    u = actx.thaw(density_discr.nodes()[0])
+
+    def build_hmat():
+        from pytential.linalg.hmatrix import build_hmatrix_matvec_by_proxy
+        return build_hmatrix_matvec_by_proxy(
+            actx, places, sym_op, sym_u,
+            domains=[dofdesc],
+            context={},
+            auto_where=dofdesc,
+            id_eps=1.0e-10,
+            _tree_kind="adaptive-level-restricted",
+            _approx_nproxy=64,
+            _proxy_radius_factor=1.15)
+
+    # warmup
+    from pytools import ProcessTimer
+    with ProcessTimer() as pt:
+        hmat = build_hmat()
+        actx.queue.finish()
+
+    logger.info("build(warmup): %s", pt)
+
+    # build
+    with ProcessTimer() as pt:
+        hmat = build_hmat()
+        actx.queue.finish()
+
+    t_build = pt.wall_elapsed
+    logger.info("build: %s", pt)
+
+    # matvec
+    with ProcessTimer() as pt:
+        du = hmat @ u
+        assert du is not None
+        actx.queue.finish()
+
+    t_matvec = pt.wall_elapsed
+    logger.info("matvec: %s", pt)
+
+    return Timings(t_build, t_matvec)
+
+
+def run_scaling_study(
+        ambient_dim: int, *,
+        target_order: int = 4,
+        source_ovsmp: int = 4,
+        qbx_order: int = 4,
+        ) -> None:
+    dd = sym.DOFDescriptor(f"d{ambient_dim}", discr_stage=sym.QBX_SOURCE_STAGE2)
+
+    import pyopencl as cl
+    ctx = cl.create_some_context()
+    queue = cl.CommandQueue(ctx)
+    actx = PyOpenCLArrayContext(queue, force_device_scalars=True)
+
+    eoc_build = EOCRecorder()
+    eoc_matvec = EOCRecorder()
+
+    import meshmode.mesh.generation as mgen
+    import meshmode.discretization.poly_element as mpoly
+    resolutions = [64, 128, 256, 512, 1024, 1536, 2048, 2560, 3072]
+
+    for n in resolutions:
+        mesh = mgen.make_curve_mesh(
+            mgen.NArmedStarfish(5, 0.25),
+            np.linspace(0, 1, n),
+            order=target_order)
+
+        from meshmode.discretization import Discretization
+        pre_density_discr = Discretization(actx, mesh,
+            mpoly.InterpolatoryQuadratureGroupFactory(target_order))
+
+        from pytential.qbx import QBXLayerPotentialSource
+        qbx = QBXLayerPotentialSource(
+            pre_density_discr,
+            fine_order=source_ovsmp * target_order,
+            qbx_order=qbx_order,
+            fmm_order=False, fmm_backend=None,
+            )
+        places = GeometryCollection(qbx, auto_where=dd.geometry)
+        density_discr = places.get_discretization(dd.geometry, dd.discr_stage)
+
+        logger.info("ndofs:     %d", density_discr.ndofs)
+        logger.info("nelements: %d", density_discr.mesh.nelements)
+
+        timings = run_hmatrix_matvec(actx, places, dofdesc=dd)
+        eoc_build.add_data_point(density_discr.ndofs, timings.build)
+        eoc_matvec.add_data_point(density_discr.ndofs, timings.matvec)
+
+    for name, eoc in [("build", eoc_build), ("matvec", eoc_matvec)]:
+        logger.info("%s\n%s",
+            name, eoc.pretty_print(
+                abscissa_label="dofs",
+                error_label=f"{name} (s)",
+                abscissa_format="%d",
+                error_format="%.3fs",
+                eoc_format="%.2f",
+                )
+            )
+        visualize_eoc(f"scaling-study-hmatrix-{name}", eoc, 1)
+
+
+def visualize_eoc(
+        filename: str, eoc: EOCRecorder, order: int,
+        overwrite: bool = False) -> None:
+    try:
+        import matplotlib.pyplot as plt
+    except ImportError:
+        logger.info("matplotlib not available for plotting")
+        return
+
+    fig = plt.figure(figsize=(10, 10), dpi=300)
+    ax = fig.gca()
+
+    h, error = np.array(eoc.history).T  # type: ignore[no-untyped-call]
+    ax.loglog(h, error, "o-")
+
+    max_h = np.max(h)
+    min_e = np.min(error)
+    max_e = np.max(error)
+    min_h = np.exp(np.log(max_h) + np.log(min_e / max_e) / order)
+
+    ax.loglog(
+        [max_h, min_h], [max_e, min_e], "k-", label=rf"$\mathcal{{O}}(h^{order})$"
+    )
+
+    # }}}
+
+    ax.grid(True, which="major", linestyle="-", alpha=0.75)
+    ax.grid(True, which="minor", linestyle="--", alpha=0.5)
+
+    ax.set_xlabel("$N$")
+    ax.set_ylabel("$T~(s)$")
+
+    import pathlib
+    filename = pathlib.Path(filename)
+    if not overwrite and filename.exists():
+        raise FileExistsError(f"output file '{filename}' already exists")
+
+    fig.savefig(filename)
+    plt.close(fig)
+
+
+if __name__ == "__main__":
+    run_scaling_study(ambient_dim=2)

pytential/linalg/cluster.py

@@ -220,6 +220,23 @@ def make_block(i: int, j: int):
     else:
         raise ValueError(f"unsupported ndarray dimension: '{ndim}'")
 
+
+def uncluster(ary: np.ndarray, index: IndexList, clevel: ClusterLevel) -> np.ndarray:
+    assert ary.shape == (clevel.parent_map.size,)
+
+    if index.nclusters == 1:
+        return ary
+
+    result = np.empty(index.nclusters, dtype=object)
+    for ifrom, ppm in enumerate(clevel.parent_map):
+        offset = 0
+        for ito in ppm:
+            cluster_size = index.cluster_size(ito)
+            result[ito] = ary[ifrom][offset:offset + cluster_size]
+            offset += cluster_size
+
+    return result
+
 # }}}