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- import Libdl
- using StarPU
- using LinearAlgebra
- @target STARPU_CPU+STARPU_CUDA
- @codelet function vector_scal(m::Int32, v :: Vector{Float32}, k :: Float32, l :: Float32) :: Float32
- N :: Int32 = length(v)
- # Naive version
- @parallel for i in (1 : N)
- v[i] = v[i] * m + l + k
- end
- end
- starpu_init()
- function vector_scal_with_starpu(v :: Vector{Float32}, m :: Int32, k :: Float32, l :: Float32)
- tmin=0
- @starpu_block let
- hV = starpu_data_register(v)
- tmin=0
- perfmodel = StarpuPerfmodel(
- perf_type = STARPU_HISTORY_BASED,
- symbol = "history_perf"
- )
- cl = StarpuCodelet(
- cpu_func = CPU_CODELETS["vector_scal"],
- # cuda_func = CUDA_CODELETS["vector_scal"],
- #opencl_func="ocl_matrix_mult",
- modes = [STARPU_RW],
- perfmodel = perfmodel
- )
- for i in (1 : 1)
- t=time_ns()
- @starpu_sync_tasks begin
- handles = [hV]
- task = StarpuTask(cl = cl, handles = handles, cl_arg=(m, k, l))
- starpu_task_submit(task)
- end
- # @starpu_sync_tasks for task in (1:1)
- # @starpu_async_cl vector_scal(hV, STARPU_RW, [m, k, l])
- # end
- t=time_ns()-t
- if (tmin==0 || tmin>t)
- tmin=t
- end
- end
- end
- return tmin
- end
- function compute_times(io,start_dim, step_dim, stop_dim)
- for size in (start_dim : step_dim : stop_dim)
- V = Array(rand(Cfloat, size))
- starpu_memory_pin(V)
- m :: Int32 = 10
- k :: Float32 = 2.
- l :: Float32 = 3.
- println("INPUT ", V[1:10])
- mt = vector_scal_with_starpu(V, m, k, l)
- starpu_memory_unpin(V)
- println("OUTPUT ", V[1:10])
- println(io,"$size $mt")
- println("$size $mt")
- end
- end
- if size(ARGS, 1) < 1
- filename="x.dat"
- else
- filename=ARGS[1]
- end
- io=open(filename,"w")
- compute_times(io,1024,1024,4096)
- close(io)
- starpu_shutdown()
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