def LoadDataRef(db,table) : db = MySQLdb.connect("localhost", "root", "", db) cursor = db.cursor() sql = "SELECT idstr from %s"%table idString = [] try: cursor.execute(sql) results = cursor.fetchall() for row in results : list_ref.append(float64(row[0])) except : print "Error" db.close
''' Demonstrate the vectorize API with automatical memory transfer and manual memory transfer. ''' from timeit import default_timer as timer import numpy from numbapro import vectorize, float64, cuda @vectorize([float64(float64, float64)], target='gpu') def vector_mul(a, b): return a * b a = numpy.random.rand(10000000) b = numpy.random.rand(10000000) # Let NumbaPro automatically convert host memory to device memory ts = timer() for i in xrange(10): result = vector_mul(a, b) te = timer() print 'auto', te - ts # Manual conversion between host and device memory ts = timer() for i in xrange(10): # copy host memory to device da = cuda.to_device(a) db = cuda.to_device(b)
''' Demonstrate the vectorize API with automatical memory transfer and manual memory transfer. ''' from timeit import default_timer as timer import numpy from numbapro import vectorize, float64, cuda @vectorize([float64(float64, float64)], target='gpu') def vector_mul(a, b): return a * b a = numpy.random.rand(10000000) b = numpy.random.rand(10000000) # Let NumbaPro automatically convert host memory to device memory ts = timer() for i in xrange(10): result = vector_mul(a, b) te = timer() print 'auto', te - ts # Manual conversion between host and device memory ts = timer() for i in xrange(10): # copy host memory to device da = cuda.to_device(a) db = cuda.to_device(b) # execute kernel