Commit 21d19cbd authored by Neil Gershenfeld's avatar Neil Gershenfeld

wip

parent 4135bd71
Pipeline #4932 passed with stage
in 4 seconds
......@@ -11,51 +11,92 @@ import time
# problem size
#
block_size = 2**10
grid_size = 2**20
grid_size = 2**21
NPTS = grid_size*block_size
#
# CUDA kernels
# kernels and functions
#
@cuda.jit
def init(arr):
i = 1+cuda.grid(1)
arr[i] = 0.5/((i-0.75)*(i-0.25))
arr[i-1] = 0.5/((i-0.75)*(i-0.25))
#
@cuda.reduce
def sum_reduce(a,b):
def Numba_reduce(a,b):
return a+b
#
# compile kernels
@cuda.jit
def CUDA_sum(arr,len):
i = cuda.grid(1)
if (i < len):
arr[i] += arr[i+len]
#
def CUDA_reduce(arr,NPTS):
len = NPTS >> 1
while (1):
CUDA_sum[grid_size,block_size](arr,len)
len = len >> 1
if (len == 0):
return
#
# device array
#
arr = cuda.device_array(NPTS,np.float32)
#
# compile kernels
#
init[grid_size,block_size](arr)
pi = sum_reduce(arr)
pi = Numba_reduce(arr)
CUDA_reduce(arr,NPTS)
#
# array calc
# CUDA kernel array calculation
#
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("CUDA kernel array calculation:")
print(" time = %f, estimated MFlops = %f"%(end_time-start_time,mflops))
#
# reduction
# Numba reduce
#
init[grid_size,block_size](arr)
start_time = time.time()
pi = sum_reduce(arr)
pi = Numba_reduce(arr)
end_time = time.time()
mflops = NPTS*1.0/(1.0e6*(end_time-start_time))
print("Numba CUDA reduction:")
print("Numba reduce:")
print(" time = %f, estimated MFlops = %f"%(end_time-start_time,mflops))
#
# both
# both with Numba reduce
#
start_time = time.time()
init[grid_size,block_size](arr)
pi = sum_reduce(arr)
pi = Numba_reduce(arr)
end_time = time.time()
mflops = NPTS*5.0/(1.0e6*(end_time-start_time))
print("Numba CUDA both:")
print("both with Numba reduce:")
print(" NPTS = %d, pi = %f"%(NPTS,pi))
print(" time = %f, estimated MFlops = %f"%(end_time-start_time,mflops))
#
# CUDA kernel reduction
#
init[grid_size,block_size](arr)
start_time = time.time()
CUDA_reduce(arr,NPTS)
end_time = time.time()
mflops = NPTS*1.0/(1.0e6*(end_time-start_time))
print("CUDA kernel reduction:")
print(" time = %f, estimated MFlops = %f"%(end_time-start_time,mflops))
#
# both with CUDA kernel reduction
#
start_time = time.time()
init[grid_size,block_size](arr)
CUDA_reduce(arr,NPTS)
end_time = time.time()
darr = arr.copy_to_host()
mflops = NPTS*5.0/(1.0e6*(end_time-start_time))
print("both with CUDA kernel reduction:")
print(" NPTS = %d, pi = %f"%(NPTS,darr[0]))
print(" time = %f, estimated MFlops = %f"%(end_time-start_time,mflops))
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