wip

Python/numbapig.py

@@ -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))