Daniel
690ee33db1
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103 lines
3.5 KiB
Julia
103 lines
3.5 KiB
Julia
using LinearAlgebra
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using BenchmarkTools
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using BenchmarkPlots, StatsPlots
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using .Transpiler
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using .Interpreter
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# University setup at 10.20.1.7 if needed
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exprsCPU = [
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# CPU interpreter requires an anonymous function and array ref s
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:(p[1] * x[1] + p[2]), # 5 op
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:((((x[1] + x[2]) + x[3]) + x[4]) + x[5]), # 9 op
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:(log(abs(x[1]))), # 3 op
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:(powabs(p[2] - powabs(p[1] + x[1], 1/x[1]),p[3])) # 13 op
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] # 30 op
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exprsCPU = map(e -> Expr(:->, :(x,p), e), exprsCPU)
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exprsGPU = [
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# CPU interpreter requires an anonymous function and array ref s
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:(p1 * x1 + p2), # 5 op
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:((((x1 + x2) + x3) + x4) + x5), # 9 op
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:(log(abs(x1))), # 3 op
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:(powabs(p2 - powabs(p1 + x1, 1/x1),p3)) # 13 op
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] # 30 op
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# p is the same for CPU and GPU
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p = [randn(Float32, 10) for _ in 1:length(exprsCPU)] # generate 10 random parameter values for each expr
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nrows = 1000
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X = randn(Float32, nrows, 5)
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expr_reps = 100 # 100 parameter optimisation steps basically
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@testset "CPU performance" begin
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# warmup
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# interpret_cpu(exprsCPU, X, p)
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# @btime interpret_cpu(exprsCPU, X, p; repetitions=expr_reps) # repetitions simulates parameter optimisation
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# @btime test_cpu_interpreter(1000)
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# @btime fetch.([Threads.@spawn interpret_cpu(exprsCPU, X, p; repetitions=expr_reps) for i in 1:reps])
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# test_cpu_interpreter(1000, parallel=true) # start julia -t 6 for six threads
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# @btime test_cpu_interpreter(10000)
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# @btime test_cpu_interpreter(10000, parallel=true)
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end
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ncols = 1000
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X_GPU = randn(Float32, 5, ncols)
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@testset "Interpreter Performance" begin
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# Put data in shared memory:
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# https://cuda.juliagpu.org/v2.6/api/kernel/#Shared-memory
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# Make array const:
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# https://cuda.juliagpu.org/v2.6/api/kernel/#Device-arrays
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# Memory management like in C++ might help with performance improvements
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# https://cuda.juliagpu.org/v2.6/lib/driver/#Memory-Management
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end
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@testset "Transpiler Performance" begin
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# Put data in shared memory:
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# https://cuda.juliagpu.org/v2.6/api/kernel/#Shared-memory
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# Make array const:
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# https://cuda.juliagpu.org/v2.6/api/kernel/#Device-arrays
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# Memory management like in C++ might help with performance improvements
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# https://cuda.juliagpu.org/v2.6/lib/driver/#Memory-Management
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end
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suite = BenchmarkGroup()
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suite["CPU"] = BenchmarkGroup(["CPUInterpreter"])
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# suite["GPUI"] = BenchmarkGroup(["GPUInterpreter"])
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# suite["GPUT"] = BenchmarkGroup(["GPUTranspiler"])
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X_small = randn(Float32, 100, 5)
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suite["CPU"]["small varset"] = @benchmarkable interpret_cpu(exprsCPU, X_small, p; repetitions=expr_reps)
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X_normal = randn(Float32, 1000, 5)
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suite["CPU"]["normal varset"] = @benchmarkable interpret_cpu(exprsCPU, X_normal, p; repetitions=expr_reps)
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X_large = randn(Float32, 10000, 5)
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suite["CPU"]["large varset"] = @benchmarkable interpret_cpu(exprsCPU, X_large, p; repetitions=expr_reps)
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# tune!(suite)
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# BenchmarkTools.save("params.json", params(suite))
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loadparams!(suite, BenchmarkTools.load("params.json")[1], :samples, :evals, :gctrial, :time_tolerance, :evals_set, :gcsample, :seconds, :overhead, :memory_tolerance)
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results = run(suite, verbose=true, seconds=180)
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# results2 = run(suite, verbose=true, seconds=180)
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medianCPU = median(results["CPU"])
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# medianCPU2 = median(results2["CPU"])
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# medianInterpreter = median(results["GPUI"])
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# medianTranspiler = median(results["GPUT"])
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# jud = judge(medianCPU, medianCPU2; time_tolerance=0.001)
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# println(jud)
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# judge(medianCPU, medianInterpreter; time_tolerance=0.001)
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# judge(medianCPU, medianTranspiler; time_tolerance=0.001)
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# judge(medianInterpreter, medianTranspiler; time_tolerance=0.001)
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