#
# numbapig.py
# Neil Gershenfeld 2/9/20
# calculation of pi by a Numba CUDA sum
# pi = 3.14159265358979323846 
#
from numba import cuda
import numpy as np
import time
#
# problem size
#
block_size = 2**10
grid_size = 2**20
NPTS = grid_size*block_size
#
# CUDA kernels
#
@cuda.jit
def init(arr):
    i = 1+cuda.grid(1)
    arr[i] = 0.5/((i-0.75)*(i-0.25))

@cuda.reduce
def sum_reduce(a,b):
    return a+b
#
# compile kernels
#
arr = cuda.device_array(NPTS,np.float32)
init[grid_size,block_size](arr)
pi = sum_reduce(arr)
#
# array calc 
#
start_time = time.time()
init[grid_size,block_size](arr)
end_time = time.time()
mflops = NPTS*4.0/(1.0e6*(end_time-start_time))
print("Numba CUDA array calculation:")
print("   time = %f, estimated MFlops = %f"%(end_time-start_time,mflops))
#
# reduction
#
start_time = time.time()
pi = sum_reduce(arr)
end_time = time.time()
mflops = NPTS*1.0/(1.0e6*(end_time-start_time))
print("Numba CUDA reduction:")
print("   time = %f, estimated MFlops = %f"%(end_time-start_time,mflops))
#
# both
#
start_time = time.time()
init[grid_size,block_size](arr)
pi = sum_reduce(arr)
end_time = time.time()
mflops = NPTS*5.0/(1.0e6*(end_time-start_time))
print("Numba CUDA both:")
print("   NPTS = %d, pi = %f"%(NPTS,pi))
print("   time = %f, estimated MFlops = %f"%(end_time-start_time,mflops))