Daniel
effd477558
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63 lines
2.3 KiB
Julia
63 lines
2.3 KiB
Julia
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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@testset "CPU performance" begin
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function test_cpu_interpreter(nrows; parallel = false)
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exprs = [
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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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exprs = map(e -> Expr(:->, :(x,p), e), exprs)
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X = randn(Float32, nrows, 10)
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p = [randn(Float32, 10) for _ in 1:length(exprs)] # generate 10 random parameter values for each expr
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# warmup
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interpret_cpu(exprs, X, p)
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expr_reps = 100 # for each expr
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reps= 100
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if parallel
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t_sec = @elapsed fetch.([Threads.@spawn interpret_cpu(exprs, X, p; repetitions=expr_reps) for i in 1:reps])
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println("~ $(round(30 * reps * expr_reps * nrows / 1e9 / t_sec, digits=2)) GFLOPS ($(Threads.nthreads()) threads) ($(round(peakflops(1000, eltype=Float32, ntrials=1) / 1e9, digits=2)) GFLOPS (peak, single-core))")
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else
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t_sec = @elapsed for i in 1:reps interpret_cpu(exprs, X, p; repetitions=expr_reps) end
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println("~ $(round(30 * reps * expr_reps * nrows / 1e9 / t_sec, digits=2)) GFLOPS (single-core) ($(round(peakflops(1000, eltype=Float32, ntrials=1) / 1e9, digits=2)) GFLOPS (peak, single-core))")
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end
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true
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end
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LinearAlgebra.BLAS.set_num_threads(1) # only use a single thread for peakflops
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@test test_cpu_interpreter(1000)
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@test test_cpu_interpreter(1000, parallel=true) # start julia -t 6 for six threads
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@test test_cpu_interpreter(10000)
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@test test_cpu_interpreter(10000, parallel=true)
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end
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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 |