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rouson merged 1 commit into from
Nov 17, 2024
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feat(example): openmp inferences #225

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43 changes: 24 additions & 19 deletions example/concurrent-inferences.f90
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@ program concurrent_inferences
use julienne_m, only : string_t, command_line_t, file_t
use assert_m, only : assert
use iso_fortran_env, only : int64, real64
use omp_lib
implicit none

type(string_t) network_file_name
Expand Down Expand Up @@ -35,40 +36,44 @@ program concurrent_inferences
neural_network = neural_network_t(file_t(network_file_name))

print *,"Defining an array of tensor_t input objects with random normalized components"
allocate(outputs(lat,lon,lev))
allocate( inputs(lat,lon,lev))
allocate(input_components(lat,lon,lev,neural_network%num_inputs()))
allocate(outputs(lat,lev,lon))
allocate( inputs(lat,lev,lon))
allocate(input_components(lat,lev,lon,neural_network%num_inputs()))
call random_number(input_components)

do concurrent(i=1:lat, j=1:lon, k=1:lev)
inputs(i,j,k) = tensor_t(input_components(i,j,k,:))
do concurrent(i=1:lat, k=1:lev, j=1:lon)
inputs(i,k,j) = tensor_t(input_components(i,k,j,:))
end do

print *,"Performing concurrent inference"
print *,"Performing",lat*lev*lon," inferences inside `do concurrent`."
call system_clock(t_start, clock_rate)
do concurrent(i=1:lat, j=1:lon, k=1:lev)
outputs(i,j,k) = neural_network%infer(inputs(i,j,k))
do concurrent(i=1:lat, k=1:lev, j=1:lon)
outputs(i,k,j) = neural_network%infer(inputs(i,k,j))
end do
call system_clock(t_finish)
print *,"Concurrent inference time: ", real(t_finish - t_start, real64)/real(clock_rate, real64)
print *,"Elapsed system clock: ", real(t_finish - t_start, real64)/real(clock_rate, real64)

print *,"Performing loop-based inference"
call system_clock(t_start)
do k=1,lev
do j=1,lon
print *,"Performing",lat*lev*lon," inferences inside `omp parallel do`."
call system_clock(t_start, clock_rate)
!$omp parallel do shared(inputs,outputs)
do j=1,lon
do k=1,lev
do i=1,lat
outputs(i,j,k) = neural_network%infer(inputs(i,j,k))
outputs(i,k,j) = neural_network%infer(inputs(i,k,j))
end do
end do
end do
!$omp end parallel do
call system_clock(t_finish)
print *,"Looping inference time: ", real(t_finish - t_start, real64)/real(clock_rate, real64)
print *,"Elapsed system clock: ", real(t_finish - t_start, real64)/real(clock_rate, real64)

print *,"Performing elemental inferences"
print *,"Performing elemental inferences inside `omp workshare`"
call system_clock(t_start, clock_rate)
outputs = neural_network%infer(inputs) ! implicit (re)allocation of outputs array only if shape(inputs) /= shape(outputs)
!$omp workshare
outputs = neural_network%infer(inputs)
!$omp end workshare
call system_clock(t_finish)
print *,"Elemental inference time: ", real(t_finish - t_start, real64)/real(clock_rate, real64)
print *,"Elapsed system clock: ", real(t_finish - t_start, real64)/real(clock_rate, real64)

end block single_precision_inference

Expand Down Expand Up @@ -96,7 +101,7 @@ program concurrent_inferences
print *,"Performing double-precision concurrent inference"
call system_clock(t_start, clock_rate)
do concurrent(i=1:lat, j=1:lon, k=1:lev)
outputs(i,j,k) = neural_network%infer(inputs(i,j,k))
outputs(i,j,k) = neural_network%infer(inputs(i,j,k))
end do
call system_clock(t_finish)
print *,"Double-precision concurrent inference time: ", real(t_finish - t_start, real64)/real(clock_rate, real64)
Expand Down