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+# StarPU --- Runtime system for heterogeneous multicore architectures.
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+#
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+# Copyright (C) 2020 Université de Bordeaux, CNRS (LaBRI UMR 5800), Inria
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+#
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+# StarPU is free software; you can redistribute it and/or modify
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+# it under the terms of the GNU Lesser General Public License as published by
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+# the Free Software Foundation; either version 2.1 of the License, or (at
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+# your option) any later version.
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+#
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+# StarPU is distributed in the hope that it will be useful, but
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+# WITHOUT ANY WARRANTY; without even the implied warranty of
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+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
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+#
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+# See the GNU Lesser General Public License in COPYING.LGPL for more details.
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+#
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+using StarPU
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+
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+@target STARPU_CPU+STARPU_CUDA
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+@codelet function gemm(A :: Matrix{Float32}, B :: Matrix{Float32}, C :: Matrix{Float32}, alpha :: Float32, beta :: Float32) :: Nothing
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+
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+ M :: Int32 = height(A)
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+ N :: Int32 = width(B)
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+ K :: Int32 = width(A)
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+ lda :: Int32 = height(A)
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+ ldb :: Int32 = height(B)
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+ ldc :: Int32 = height(C)
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+ STARPU_SGEMM("N", "N", M, N, K, alpha, A, lda, B, ldb, beta, C, ldc)
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+
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+ return
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+end
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+
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+function multiply_with_starpu(A :: Matrix{Float32}, B :: Matrix{Float32}, C :: Matrix{Float32}, alpha :: Float32, beta :: Float32, nslicesx, nslicesy)
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+ scale= 3
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+ tmin=0
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+ vert = starpu_data_filter(STARPU_MATRIX_FILTER_VERTICAL_BLOCK, nslicesx)
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+ horiz = starpu_data_filter(STARPU_MATRIX_FILTER_BLOCK, nslicesy)
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+ @starpu_block let
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+ hA,hB,hC = starpu_data_register(A, B, C)
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+ starpu_data_partition(hB, vert)
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+ starpu_data_partition(hA, horiz)
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+ starpu_data_map_filters(hC, vert, horiz)
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+ tmin=0
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+ perfmodel = starpu_perfmodel(
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+ perf_type = starpu_perfmodel_type(STARPU_HISTORY_BASED),
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+ symbol = "history_perf"
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+ )
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+ cl = starpu_codelet(
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+ cpu_func = CPU_CODELETS["gemm"],
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+ cuda_func = CUDA_CODELETS["gemm"],
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+ modes = [STARPU_R, STARPU_R, STARPU_RW],
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+ perfmodel = perfmodel
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+ )
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+
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+ for i in (1 : 10 )
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+ t=time_ns()
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+ @starpu_sync_tasks begin
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+ for taskx in (1 : nslicesx)
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+ for tasky in (1 : nslicesy)
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+ handles = [hA[tasky], hB[taskx], hC[taskx, tasky]]
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+ task = starpu_task(cl = cl, handles = handles, cl_arg=(alpha, beta))
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+ starpu_task_submit(task)
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+ #@starpu_async_cl matrix_mult(hA[tasky], hB[taskx], hC[taskx, tasky])
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+ end
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+ end
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+ end
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+ t=time_ns()-t
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+ if (tmin==0 || tmin>t)
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+ tmin=t
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+ end
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+ end
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+ end
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+ return tmin
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+end
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+
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+
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+function approximately_equals(
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+ A :: Matrix{Cfloat},
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+ B :: Matrix{Cfloat},
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+ eps = 1e-2
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+)
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+ (height, width) = size(A)
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+
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+ for j in (1 : width)
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+ for i in (1 : height)
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+ if (abs(A[i,j] - B[i,j]) > eps * max(abs(B[i,j]), abs(A[i,j])))
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+ println("A[$i,$j] : $(A[i,j]), B[$i,$j] : $(B[i,j])")
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+ return false
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+ end
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+ end
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+ end
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+
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+ return true
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+end
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+
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+function compute_times(io,start_dim, step_dim, stop_dim, nslicesx, nslicesy)
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+ for dim in (start_dim : step_dim : stop_dim)
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+ A = Array(rand(Cfloat, dim, dim))
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+ B = Array(rand(Cfloat, dim, dim))
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+ C = zeros(Float32, dim, dim)
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+ starpu_memory_pin(A)
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+ starpu_memory_pin(B)
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+ starpu_memory_pin(C)
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+ alpha = 4.0f0
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+ beta = 2.0f0
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+ mt = multiply_with_starpu(A, B, C, alpha, beta, nslicesx, nslicesy)
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+ gflop = 2 * dim * dim * dim * 1.e-9
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+ gflops = gflop / (mt * 1.e-9)
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+ size=dim*dim*dim*4*3/1024/1024
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+ println(io,"$dim $gflops")
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+ println("$dim $gflops")
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+ starpu_memory_unpin(A)
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+ starpu_memory_unpin(B)
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+ starpu_memory_unpin(C)
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+ end
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+end
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+
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+if size(ARGS, 1) < 1
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+ filename="x.dat"
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+else
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+ filename=ARGS[1]
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+end
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+
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+starpu_init()
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+
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+io=open(filename,"w")
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+compute_times(io,64,512,4096,2,2)
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+close(io)
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+
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+starpu_shutdown()
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+
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Pierre Huchant