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implementation: finished pre-processing section; updated code
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Daniel
2025-04-26 13:46:23 +02:00
parent ad2eab2e0a
commit e571fa5bd6
10 changed files with 238 additions and 46 deletions
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6
package/src/ExpressionExecutorCuda.jl
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@ -26,8 +26,9 @@ function interpret_gpu(exprs::Vector{Expr}, X::Matrix{Float32}, p::Vector{Vector
ncols = size(X, 2)
results = Matrix{Float32}(undef, ncols, length(exprs))
# TODO: create CuArray for variables here already, as they never change
for i in 1:repetitions # Simulate parameter tuning -> local search (X remains the same, p gets changed in small steps and must be performed sequentially)
for i in 1:repetitions # Simulate parameter tuning -> local search (X remains the same, p gets changed in small steps and must be performed sequentially, which it is with this impl)
results = Interpreter.interpret(exprs, X, p)
end
@ -40,8 +41,9 @@ function evaluate_gpu(exprs::Vector{Expr}, X::Matrix{Float32}, p::Vector{Vector{
ncols = size(X, 2)
results = Matrix{Float32}(undef, ncols, length(exprs))
# TODO: create CuArray for variables here already, as they never change
for i in 1:repetitions # Simulate parameter tuning -> local search (X remains the same, p gets changed in small steps and must be performed sequentially)
for i in 1:repetitions # Simulate parameter tuning -> local search (X remains the same, p gets changed in small steps and must be performed sequentially, which it is with this impl)
results = Transpiler.evaluate(exprs, X, p)
end