forked from jan/opencl_fdfd
cleanup and improved reporting for csr
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parent
03f7f6d3c4
commit
bb7b90e938
@ -1,4 +1,5 @@
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import numpy
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import numpy
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from numpy.linalg import norm
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import pyopencl
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import pyopencl
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import pyopencl.array
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import pyopencl.array
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@ -52,17 +53,15 @@ def create_ops(context):
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v_out[i] = dot;
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v_out[i] = dot;
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'''
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'''
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v_out_args = ctype + ' *v_out, int v_len_half'
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v_out_args = ctype + ' *v_out'
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m_args = 'int m_nnz, int *m_row_ptr, int *m_col_ind, ' + ctype + ' *m_data'
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m_args = 'int *m_row_ptr, int *m_col_ind, ' + ctype + ' *m_data'
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v_in_args = ctype + ' *v_in'
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v_in_args = ctype + ' *v_in'
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spmv_kernel = ElementwiseKernel(context, operation=spmv_source, preamble=preamble,
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spmv_kernel = ElementwiseKernel(context, operation=spmv_source, preamble=preamble,
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arguments=', '.join((v_out_args, m_args, v_in_args)))
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arguments=', '.join((v_out_args, m_args, v_in_args)))
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def spmv(v_out, m, v_in, e):
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def spmv(v_out, m, v_in, e):
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return spmv_kernel(v_out, (v_out.size - 1)//2,
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return spmv_kernel(v_out, m.row_ptr, m.col_ind, m.data, v_in, wait_for=e)
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m.data.size, m.row_ptr, m.col_ind, m.data,
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v_in, wait_for=e)
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# -------------------------------------
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# -------------------------------------
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@ -149,7 +148,7 @@ def cg(a, b, max_iters=10000, err_thresh=1e-6, context=None, queue=None, verbose
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ops = create_ops(context)
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ops = create_ops(context)
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x = pyopencl.array.zeros(queue, dtype=numpy.complex128, shape=b.shape)
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x = pyopencl.array.zeros(queue, dtype=numpy.complex128, shape=b.shape)
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v = pyopencl.array.empty_like(x)
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v = pyopencl.array.zeros_like(x)
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p = pyopencl.array.zeros_like(x)
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p = pyopencl.array.zeros_like(x)
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r = pyopencl.array.to_device(queue, b)
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r = pyopencl.array.to_device(queue, b)
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alpha = 1.0 + 0j
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alpha = 1.0 + 0j
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@ -158,20 +157,19 @@ def cg(a, b, max_iters=10000, err_thresh=1e-6, context=None, queue=None, verbose
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m = CSRMatrix(queue, a)
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m = CSRMatrix(queue, a)
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e = ops['spmv'](v, m, x, [])
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_, err2 = ops['ri_update'](r, [])
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e = ops['xr_update'](x, p, r, v, 0.0, [e])
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_, err2 = ops['ri_update'](r, [e])
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b_norm = numpy.sqrt(err2)
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b_norm = numpy.sqrt(err2)
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print('b_norm check: ', b_norm)
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print('b_norm check: ', b_norm)
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start_time2 = time.perf_counter()
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start_time2 = time.perf_counter()
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success = False
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for k in range(max_iters):
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for k in range(max_iters):
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if verbose:
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if verbose:
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print('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha), end=' ')
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print('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha), end=' ')
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rho_prev = rho
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rho_prev = rho
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e = ops['xr_update'](x, p, r, v, alpha, [e])
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e = ops['xr_update'](x, p, r, v, alpha, [])
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rho, err2 = ops['ri_update'](r, [e])
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rho, err2 = ops['ri_update'](r, [e])
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errs += [numpy.sqrt(err2) / b_norm]
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errs += [numpy.sqrt(err2) / b_norm]
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@ -179,17 +177,35 @@ def cg(a, b, max_iters=10000, err_thresh=1e-6, context=None, queue=None, verbose
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print('err', errs[-1])
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print('err', errs[-1])
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if errs[-1] < err_thresh:
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if errs[-1] < err_thresh:
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time_elapsed = time.perf_counter() - start_time
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success = True
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print('Success, {} iterations in {} sec: {} iterations/sec'.format(k,
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break
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time_elapsed, k/time_elapsed))
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print('overhead', start_time2-start_time)
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return x.get(), errs, True
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e = ops['p_update'](p, r, rho/rho_prev, [])
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e = ops['p_update'](p, r, rho/rho_prev, [])
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e.wait()
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ops['spmv'](v, m, p, [e]).wait()
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ops['spmv'](v, m, p, [e]).wait()
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# v2 = a @ p.get()
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# print('norm', norm(v2 - v.get()))
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alpha = rho / pyopencl.array.dot(p, v).get()
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alpha = rho / pyopencl.array.dot(p, v).get()
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if k % 1000 == 0:
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if k % 1000 == 0:
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print(k)
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print(k)
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return x.get(), errs, False
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'''
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Done solving
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'''
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time_elapsed = time.perf_counter() - start_time
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x = x.get()
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if success:
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print('Success', end='')
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else:
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print('Failure', end=', ')
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print(', {} iterations in {} sec: {} iterations/sec \
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'.format(k, time_elapsed, k / time_elapsed))
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print('final error', errs[-1])
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print('overhead {} sec'.format(start_time2 - start_time))
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print('Post-everything residual:', norm(a @ x - b) / norm(b))
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return x
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