from __future__ import division __copyright__ = "Copyright (C) 2012 Andreas Kloeckner" __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. """ import numpy as np import pyopencl as cl import pyopencl.array # noqa import loopy as lp import logging # noqa from pyopencl.tools import ( # noqa pytest_generate_tests_for_pyopencl as pytest_generate_tests) def test_dg_volume(ctx_factory): #logging.basicConfig(level=logging.DEBUG) dtype = np.float32 dtype4 = cl.array.vec.float4 ctx = ctx_factory() order = "F" N = 3 # noqa Np = (N+1)*(N+2)*(N+3)//6 # noqa K = 10000 # noqa knl = lp.make_kernel([ "{[n,m,k]: 0<= n,m < Np and 0<= k < K}", ], """ <> du_drst = sum(m, DrDsDt[n,m]*u[k,m]) <> dv_drst = sum(m, DrDsDt[n,m]*v[k,m]) <> dw_drst = sum(m, DrDsDt[n,m]*w[k,m]) <> dp_drst = sum(m, DrDsDt[n,m]*p[k,m]) # volume flux rhsu[k,n] = dot(drst_dx[k],dp_drst) rhsv[k,n] = dot(drst_dy[k],dp_drst) rhsw[k,n] = dot(drst_dz[k],dp_drst) rhsp[k,n] = dot(drst_dx[k], du_drst) + dot(drst_dy[k], dv_drst) \ + dot(drst_dz[k], dw_drst) """, [ lp.GlobalArg("u,v,w,p,rhsu,rhsv,rhsw,rhsp", dtype, shape="K, Np", order="C"), lp.GlobalArg("DrDsDt", dtype4, shape="Np, Np", order="C"), lp.GlobalArg("drst_dx,drst_dy,drst_dz", dtype4, shape="K", order=order), lp.ValueArg("K", np.int32, approximately=1000), ], name="dg_volume", assumptions="K>=1", defines=dict(Np=Np)) seq_knl = knl def variant_basic(knl): knl = lp.tag_inames(knl, dict(k="g.0", n="l.0")) return knl def variant_more_per_work_group(knl): knl = lp.tag_inames(knl, dict(n="l.0")) knl = lp.split_iname(knl, "k", 3, outer_tag="g.0", inner_tag="l.1") return knl def variant_image_d(knl): knl = lp.tag_inames(knl, dict(n="l.0")) knl = lp.split_iname(knl, "k", 3, outer_tag="g.0", inner_tag="l.1") knl = lp.change_arg_to_image(knl, "DrDsDt") return knl def variant_prefetch_d(knl): knl = lp.tag_inames(knl, dict(n="l.0")) knl = lp.split_iname(knl, "k", 3, outer_tag="g.0", inner_tag="l.1") knl = lp.add_prefetch(knl, "DrDsDt[:,:]") return knl def variant_prefetch_fields(knl): knl = lp.tag_inames(knl, dict(n="l.0")) knl = lp.split_iname(knl, "k", 3, outer_tag="g.0", inner_tag="l.1") for name in ["u", "v", "w", "p"]: knl = lp.add_prefetch(knl, "%s[k,:]" % name, ["k_inner"]) return knl def variant_k_ilp(knl): knl = lp.tag_inames(knl, dict(n="l.0")) knl = lp.split_iname(knl, "k", 3, outer_tag="g.0", inner_tag="ilp") knl = lp.tag_inames(knl, dict(m="unr")) return knl def variant_simple_padding(knl): knl = lp.tag_inames(knl, dict(n="l.0")) knl = lp.split_iname(knl, "k", 3, outer_tag="g.0", inner_tag="l.1") arg_names = [ prefix+name for name in ["u", "v", "w", "p"] for prefix in ["", "rhs"]] for name in arg_names: knl = lp.add_padding(knl, name, axis=0, align_bytes=32) knl = lp.tag_inames(knl, dict(m="unr")) return knl def variant_fancy_padding(knl): knl = lp.tag_inames(knl, dict(n="l.0")) pad_mult = lp.find_padding_multiple(knl, "u", 1, 32) arg_names = [ prefix+name for name in ["u", "v", "w", "p"] for prefix in ["", "rhs"]] knl = lp.split_arg_axis(knl, [(nm, 0) for nm in arg_names], pad_mult) return knl parameters_dict = dict(K=K) variants = [ variant_basic, variant_more_per_work_group, variant_prefetch_d, variant_prefetch_fields, variant_k_ilp, variant_simple_padding, variant_fancy_padding ] if (ctx.devices[0].image_support and ctx.devices[0].platform.name != "Portable Computing Language"): variants.append(variant_image_d) for variant in variants: lp.auto_test_vs_ref( seq_knl, ctx, variant(knl), parameters=parameters_dict, #codegen_kwargs=dict(with_annotation=True) ) def no_test_dg_surface(ctx_factory): # tough to test, would need the right index info dtype = np.float32 ctx = ctx_factory() order = "F" N = 3 # noqa Np = (N+1)*(N+2)*(N+3)//6 # noqa Nfp = (N+1)*(N+2)//2 # noqa Nfaces = 4 # noqa K = 10000 # noqa knl = lp.make_kernel( [ "{[m,n,k]: 0<= m < NfpNfaces and 0<= n < Np and 0<= k < K }" ], """ <> idP = vmapP[m,k] <> idM = vmapM[m,k] <> du = u[[idP]]-u[[idM]] <> dv = v[[idP]]-v[[idM]] <> dw = w[[idP]]-w[[idM]] <> dp = bc[m,k]*p[[idP]] - p[[idM]] <> dQ = 0.5*Fscale[m,k]* \ (dp - nx[m,k]*du - ny[m,k]*dv - nz[m,k]*dw) <> fluxu = -nx[m,k]*dQ <> fluxv = -ny[m,k]*dQ <> fluxw = -nz[m,k]*dQ <> fluxp = dQ # reduction here rhsu[n,k] = sum(m, LIFT[n,m]*fluxu) rhsv[n,k] = sum(m, LIFT[n,m]*fluxv) rhsw[n,k] = sum(m, LIFT[n,m]*fluxw) rhsp[n,k] = sum(m, LIFT[n,m]*fluxp) """, [ lp.GlobalArg("vmapP,vmapM", np.int32, shape="NfpNfaces, K", order=order), lp.GlobalArg("u,v,w,p,rhsu,rhsv,rhsw,rhsp", dtype, shape="Np, K", order=order), lp.GlobalArg("nx,ny,nz,Fscale,bc", dtype, shape="NfpNfaces, K", order=order), lp.GlobalArg("LIFT", dtype, shape="Np, NfpNfaces", order="C"), lp.ValueArg("K", np.int32, approximately=1000), ], name="dg_surface", assumptions="K>=1", defines=dict(Np=Np, Nfp=Nfp, NfpNfaces=Nfaces*Nfp, nsurf_dofs=K*Nfp), ) seq_knl = knl def variant_basic(knl): return knl parameters_dict = dict(K=K) for variant in [ variant_basic, ]: lp.auto_test_vs_ref(seq_knl, ctx, variant(knl), parameters=parameters_dict) if __name__ == "__main__": import sys if len(sys.argv) > 1: exec(sys.argv[1]) else: from py.test.cmdline import main main([__file__])