程序概述：此处的大部分代码创建了FrameProcessor对象。该对象以某种数据形状初始化，通常为2048xN，然后可以使用一系列内核（proc_frame）进行调用以处理数据。对于每个长度为2048的向量，程序将：
问题：
当前性能：
在使用GeForce RTX 2080的设备上，获得的基准测试（n = 60，这似乎表现出最佳性能）是：With n = 60 Average framerate over 1000 frames: 740Hz Effective A-line rate over 1000 frames: 44399Hz似乎在这里FFT是一个很大的瓶颈。当我运行示例而不运行FFT时，我得到了以下结果：
With n = 60 Average framerate over 1000 frames: 2494Hz Effective A-line rate over 1000 frames: 149652Hz但是，我不知道如何改善我正在使用的Reikna FFT计划的性能！文档似乎没有提到优化的任何步骤，而使用gpyfft（github repo的test-gpyfft分支中的代码）的性能甚至更差。
分析：
在proc_frame函数上使用cProfile的结果如下所示：可以在此处访问代码以及补充文件：https://github.com/mswallac/PyMotionOCT并且为方便起见，也在下面显示。Ordered by: standard name ncalls tottime percall cumtime percall filename:lineno(function) 1 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(reshape) 4 0.000 0.000 0.000 0.000 <frozen importlib._bootstrap>:1009(_handle_fromlist) 1 0.000 0.000 0.000 0.000 <generated code>:101(set_args) 2 0.000 0.000 0.000 0.000 <generated code>:4(enqueue_knl_kernel_fft) 1 0.000 0.000 0.000 0.000 <generated code>:71(set_args) 1 0.000 0.000 0.002 0.002 <string>:1(<module>) 3 0.000 0.000 0.000 0.000 __init__.py:1288(result) 1 0.000 0.000 0.000 0.000 __init__.py:1294(result) 3 0.000 0.000 0.001 0.000 __init__.py:1522(enqueue_copy) 1 0.000 0.000 0.000 0.000 __init__.py:222(wrap_in_tuple) 10 0.000 0.000 0.000 0.000 __init__.py:277(name) 3 0.000 0.000 0.000 0.000 __init__.py:281(default) 2 0.000 0.000 0.000 0.000 __init__.py:285(annotation) 9 0.000 0.000 0.000 0.000 __init__.py:289(kind) 1 0.000 0.000 0.000 0.000 __init__.py:375(__init__) 2 0.000 0.000 0.000 0.000 __init__.py:575(wrapper) 2 0.000 0.000 0.000 0.000 __init__.py:596(parameters) 1 0.000 0.000 0.000 0.000 __init__.py:659(_bind) 1 0.000 0.000 0.000 0.000 __init__.py:787(bind) 2 0.000 0.000 0.000 0.000 __init__.py:833(kernel_set_args) 2 0.000 0.000 0.000 0.000 __init__.py:837(kernel_call) 1 0.000 0.000 0.000 0.000 _asarray.py:16(asarray) 1 0.000 0.000 0.000 0.000 _internal.py:830(npy_ctypes_check) 1 0.000 0.000 0.000 0.000 abc.py:137(__instancecheck__) 1 0.000 0.000 0.000 0.000 api.py:376(empty_like) 1 0.000 0.000 0.000 0.000 api.py:405(to_device) 3 0.000 0.000 0.000 0.000 api.py:466(_synchronize) 2 0.000 0.000 0.000 0.000 api.py:678(prepared_call) 2 0.000 0.000 0.000 0.000 api.py:688(__call__) 2 0.000 0.000 0.000 0.000 api.py:779(__call__) 2 0.000 0.000 0.000 0.000 array.py:1474(add_event) 1 0.000 0.000 0.000 0.000 array.py:28(f_contiguous_strides) 1 0.000 0.000 0.000 0.000 array.py:38(c_contiguous_strides) 1 0.000 0.000 0.000 0.000 array.py:393(__init__) 3 0.000 0.000 0.000 0.000 array.py:48(equal_strides) 1 0.000 0.000 0.000 0.000 array.py:520(flags) 1 0.000 0.000 0.000 0.000 array.py:580(set) 1 0.000 0.000 0.000 0.000 array.py:59(is_f_contiguous_strides) 1 0.000 0.000 0.000 0.000 array.py:61(_dtype_is_object) 1 0.000 0.000 0.000 0.000 array.py:63(is_c_contiguous_strides) 1 0.000 0.000 0.001 0.001 array.py:635(_get) 1 0.000 0.000 0.000 0.000 array.py:68(__init__) 1 0.000 0.000 0.001 0.001 array.py:689(get) 1 0.000 0.000 0.000 0.000 computation.py:620(__call__) 2 0.000 0.000 0.000 0.000 computation.py:641(__call__) 1 0.000 0.000 0.000 0.000 dtypes.py:75(normalize_type) 1 0.000 0.000 0.001 0.001 frameprocessor.py:130(FFT) 1 0.000 0.000 0.001 0.001 frameprocessor.py:137(interp_hann) 1 0.000 0.000 0.002 0.002 frameprocessor.py:146(proc_frame) 1 0.000 0.000 0.000 0.000 frameprocessor.py:20(npcast) 1 0.000 0.000 0.000 0.000 frameprocessor.py:23(rshp) 1 0.000 0.000 0.000 0.000 fromnumeric.py:197(_reshape_dispatcher) 1 0.000 0.000 0.000 0.000 fromnumeric.py:202(reshape) 1 0.000 0.000 0.000 0.000 fromnumeric.py:55(_wrapfunc) 1 0.000 0.000 0.000 0.000 ocl.py:109(allocate) 1 0.000 0.000 0.000 0.000 ocl.py:112(_copy_array) 2 0.000 0.000 0.000 0.000 ocl.py:223(_prepared_call) 2 0.000 0.000 0.000 0.000 ocl.py:225(<listcomp>) 1 0.000 0.000 0.000 0.000 ocl.py:28(__init__) 1 0.000 0.000 0.001 0.001 ocl.py:63(get) 1 0.000 0.000 0.000 0.000 ocl.py:88(array) 1 0.000 0.000 0.000 0.000 signature.py:308(bind_with_defaults) 1 0.000 0.000 0.000 0.000 {built-in method _abc._abc_instancecheck} 1 0.000 0.000 0.002 0.002 {built-in method builtins.exec} 4 0.000 0.000 0.000 0.000 {built-in method builtins.getattr} 12 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr} 37 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance} 2 0.000 0.000 0.000 0.000 {built-in method builtins.iter} 14 0.000 0.000 0.000 0.000 {built-in method builtins.len} 6 0.000 0.000 0.000 0.000 {built-in method builtins.next} 1 0.000 0.000 0.000 0.000 {built-in method builtins.setattr} 1 0.000 0.000 0.000 0.000 {built-in method numpy.array} 1 0.000 0.000 0.000 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function} 1 0.000 0.000 0.000 0.000 {built-in method numpy.empty} 2 0.001 0.001 0.001 0.001 {built-in method pyopencl._cl._enqueue_read_buffer} 1 0.000 0.000 0.000 0.000 {built-in method pyopencl._cl._enqueue_write_buffer} 4 0.000 0.000 0.000 0.000 {built-in method pyopencl._cl.enqueue_nd_range_kernel} 2 0.000 0.000 0.000 0.000 {built-in method pyopencl._cl.get_cl_header_version} 6 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects} 1 0.000 0.000 0.000 0.000 {method 'astype' of 'numpy.ndarray' objects} 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects} 3 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects} 1 0.000 0.000 0.000 0.000 {method 'reshape' of 'numpy.ndarray' objects} 2 0.000 0.000 0.000 0.000 {method 'values' of 'mappingproxy' objects}代码：
编辑2：添加的cProfile结果# -*- coding: utf-8 -*- """ Created on Wed Jan 15 10:17:16 2020 @author: Mike """ import numpy as np import pyopencl as cl from pyopencl import cltypes from pyopencl import array from reikna.fft import FFT from reikna import cluda import time import matplotlib.pyplot as plt class FrameProcessor(): def npcast(self,inp,dt): return np.asarray(inp).astype(dt) def rshp(self,inp,shape): return np.reshape(inp,shape,'C') def __init__(self,nlines): # Define data formatting n = nlines # number of A-lines per frame alen = 2048 # length of A-line / # of spec. bins self.dshape = (alen*n,) self.dt_prefft = np.float32 self.dt_fft = np.complex64 self.data_prefft = self.npcast(np.zeros(self.dshape),self.dt_prefft) self.data_fft = self.npcast(np.zeros(self.dshape),self.dt_fft) # Load spectrometer bins and prepare for interpolation / hanning operation hanning_win = self.npcast(np.hanning(2048),self.dt_prefft) lam = self.npcast(np.load('lam.npy'),self.dt_prefft) lmax = np.max(lam) lmin = np.min(lam) kmax = 1/lmin kmin = 1/lmax self.d_l = (lmax - lmin)/alen self.d_k = (kmax - kmin)/alen self.k_raw = self.npcast([1/x for x in (lam)],self.dt_prefft) self.k_lin = self.npcast([kmax-(i*self.d_k) for i in range(alen)],self.dt_prefft) # Find nearest neighbors for interpolation prep. nn0 = np.zeros((2048,),np.int32) nn1 = np.zeros((2048,),np.int32) for i in range(0,2048): res = np.abs(self.k_raw-self.k_lin[i]) minind = np.argmin(res) if i==0: nn0[i]=0 nn1[i]=1 if res[minind]>=0: nn0[i]=minind-1 nn1[i]=minind else: nn0[i]=minind nn1[i]=minind+1 self.nn0=nn0 self.nn1=nn1 # Initialize PyOpenCL platform, device, context, queue self.platform = cl.get_platforms() self.platform = self.platform[0] self.device = self.platform.get_devices() self.device = self.device[0] self.context = cl.Context([self.device]) self.queue = cl.CommandQueue(self.context) # POCL input buffers mflags = cl.mem_flags self.win_g = cl.Buffer(self.context, mflags.READ_ONLY | mflags.COPY_HOST_PTR, hostbuf=hanning_win) self.nn0_g = cl.Buffer(self.context, mflags.READ_ONLY | mflags.COPY_HOST_PTR, hostbuf=self.nn0) self.nn1_g = cl.Buffer(self.context, mflags.READ_ONLY | mflags.COPY_HOST_PTR, hostbuf=self.nn1) self.k_lin_g = cl.Buffer(self.context, mflags.READ_ONLY | mflags.COPY_HOST_PTR, hostbuf=self.k_lin) self.k_raw_g = cl.Buffer(self.context, mflags.READ_ONLY | mflags.COPY_HOST_PTR, hostbuf=self.k_raw) self.d_k_g = cl.Buffer(self.context, mflags.READ_ONLY | mflags.COPY_HOST_PTR, hostbuf=self.d_k) # POCL output buffers self.npres_interp = self.npcast(np.zeros(self.dshape),self.dt_prefft) self.npres_hann = self.npcast(np.zeros(self.dshape),self.dt_prefft) self.result_interp = cl.Buffer(self.context, cl.mem_flags.COPY_HOST_PTR, hostbuf=self.npres_interp) self.result_hann = cl.Buffer(self.context, cl.mem_flags.COPY_HOST_PTR, hostbuf=self.npres_hann) # Define POCL global / local work group sizes self.global_wgsize = (2048,n) self.local_wgsize = (512,1) # Initialize Reikna API, thread, FFT plan, output memory self.api = cluda.ocl_api() self.thr = self.api.Thread.create() self.result = self.npcast(np.zeros((2048,n)),self.dt_fft) self.fft = FFT(self.result,axes=(0,)).compile(self.thr) # kernels for hanning window, and interpolation self.program = cl.Program(self.context, """ __kernel void hann(__global float *inp, __global const float *win, __global float *res) { int i = get_global_id(0)+(get_global_size(0)*get_global_id(1)); int j = get_local_id(0)+(get_group_id(0)*get_local_size(0)); res[i] = inp[i]*win[j]; } __kernel void interp(__global float *y,__global const int *nn0,__global const int *nn1, __global const float *k_raw,__global const float *k_lin,__global float *res) { int i_shift = (get_global_size(0)*get_global_id(1)); int i_glob = get_global_id(0)+i_shift; int i_loc = get_local_id(0)+(get_group_id(0)*get_local_size(0)); float x1 = k_raw[nn0[i_loc]]; float x2 = k_raw[nn1[i_loc]]; float y1 = y[i_shift+nn0[i_loc]]; float y2 = y[i_shift+nn1[i_loc]]; float x = k_lin[i_loc]; res[i_glob]=y1+((x-x1)*((y2-y1)/(x2-x1))); } """).build() self.hann = self.program.hann self.interp = self.program.interp # Wraps FFT kernel def FFT(self,data): inp = self.thr.to_device(self.npcast(data,self.dt_fft)) self.fft(inp,inp,inverse=0) self.result = inp.get() return # Wraps interpolation and hanning window kernels def interp_hann(self,data): self.data_pfg = cl.Buffer(self.context, cl.mem_flags.COPY_HOST_PTR, hostbuf=data) self.hann.set_args(self.data_pfg,self.win_g,self.result_hann) cl.enqueue_nd_range_kernel(self.queue,self.hann,self.global_wgsize,self.local_wgsize) self.interp.set_args(self.result_hann,self.nn0_g,self.nn1_g,self.k_raw_g,self.k_lin_g,self.result_interp) cl.enqueue_nd_range_kernel(self.queue,self.interp,self.global_wgsize,self.local_wgsize) cl.enqueue_copy(self.queue,self.npres_interp,self.result_interp) return def proc_frame(self,data): self.interp_hann(data) self.FFT(self.rshp(self.npres_interp,(2048,n))) return self.result if __name__ == '__main__': n=60 fp = FrameProcessor(n) data1 = np.load('data.npy').flatten() times = [] data = fp.npcast(data1[0:2048*n],fp.dt_prefft) for i in range(5000): t=time.time() res = fp.proc_frame(data) times.append(time.time()-t) res = np.reshape(res,(2048,n),'C') avginterval = np.mean(times) frate=(1/avginterval) afrate=frate*n print('With n = %d '%n) print('Average framerate over 1000 frames: %.0fHz'%frate) print('Effective A-line rate over 1000 frames: %.0fHz'%afrate)编辑：更新的代码和基准
程序的概述：此处的大部分代码都创建了FrameProcessor对象。该对象已初始化为某种数据形状，通常为2048xN，然后可以调用以处理...
将我的回复复制到Reikna group中以供参考。