Daniel
561b37160b
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100 lines
2.8 KiB
Julia
100 lines
2.8 KiB
Julia
using CUDA
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using .ExpressionProcessing
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using .Transpiler
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expressions = Vector{Expr}(undef, 2)
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variables = Matrix{Float32}(undef, 2,2)
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parameters = Vector{Vector{Float32}}(undef, 2)
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# Resulting value should be 1.14... for the first expression
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expressions[1] = :(1 + 3 * 5 / 7 - sqrt(4))
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expressions[2] = :(5 + x1 + 1 * x2 + p1 + p2)
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variables[1,1] = 2.0
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variables[2,1] = 3.0
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variables[1,2] = 0.0
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variables[2,2] = 5.0
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parameters[1] = Vector{Float32}(undef, 1)
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parameters[2] = Vector{Float32}(undef, 2)
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parameters[1][1] = 5.0
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parameters[2][1] = 5.0
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parameters[2][2] = 0.0
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@testset "Test TMP transpiler" begin
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postfixExpr = expr_to_postfix(expressions[1])
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postfixExprs = Vector([postfixExpr])
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push!(postfixExprs, expr_to_postfix(expressions[2]))
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push!(postfixExprs, expr_to_postfix(:(5^3 + x1 - p1)))
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# generatedCode = Transpiler.transpile(postfixExpr)
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# generatedCode = Transpiler.transpile(postfixExprs[3], 2, 3, 2, 3) # TEMP
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# println(generatedCode)
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# CUDA.@sync interpret(postfixExprs, variables, parameters)
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# This is just here for testing. This will be called inside the execute method in the Transpiler module
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# linker = CuLink()
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# add_data!(linker, "ExpressionProcessing", generatedCode)
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# image = complete(linker)
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# mod = CuModule(image)
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# func = CuFunction(mod, "ExpressionProcessing")
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end
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@testset "Test transpiler evaluation" begin
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# postfixExprs = Vector{Expr}()
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# push!(postfixExprs, expressions[1])
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# push!(postfixExprs, expressions[2])
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expr = Vector{Expr}()
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push!(expr, expressions[1])
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# @time Transpiler.evaluate(expr, variables, parameters)
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end
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#TODO: test performance of transpiler PTX generation when doing "return String(take!(buffer))" vs "return take!(buffer)"
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@testset "TEMP" begin
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ptx = "
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.version 7.1
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.target sm_61
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.address_size 64
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.visible .entry ExpressionProcessing(
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.param .u32 param_1)
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{
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.reg .u32 %parameter<1>;
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.reg .u32 %i<1>;
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ld.param.u32 %i0, [param_1];
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cvta.to.global.u32 %parameter0, %i0;
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st.global.f32 [%parameter0], 10.0;
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ret;
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}"
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linker = CuLink()
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add_data!(linker, "ExpressionProcessing", ptx)
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image = complete(linker)
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mod = CuModule(image)
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func = CuFunction(mod, "ExpressionProcessing")
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variableCols = 2
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cudaResults = CuArray{Float32}(undef, 1)
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# cd = CUDA.alloc(CUDA.DeviceMemory, (variableCols * length(expressions)) * sizeof(Float32))
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# cudaResults = CUDA.fill(0f0, variableCols * length(expressions))
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# cudaResults = cu(zeros(Float32, variableCols * length(expressions)))
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config = launch_configuration(func)
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threads = min(variableCols, config.threads)
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blocks = cld(variableCols, threads)
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cudacall(func, Tuple{CuPtr{Float32}}, cudaResults; threads=1, blocks=1)
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# launch(func, cudaResults; threads=threads, blocks=blocks)
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println(Array(cudaResults))
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end
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# TODO: University setup at 10.20.1.7 |