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Merge pull request #437 from djhoese/feature-nogil-ewa
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Add GIL releasing to EWA cython code
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@djhoese
djhoese authored Jun 27, 2022
2 parents e531bd9 + bb06ebe commit 74eb088
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Showing 2 changed files with 88 additions and 64 deletions.
115 changes: 68 additions & 47 deletions pyresample/ewa/_fornav.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -39,10 +39,6 @@ cdef extern from "_fornav_templates.h":
ctypedef float ewa_param_type
ctypedef float accum_type
cdef float EPSILON
# ctypedef double weight_type
# ctypedef double ewa_param_type
# ctypedef double accum_type
# cdef double EPSILON

ctypedef struct ewa_parameters:
ewa_param_type a
Expand All @@ -64,42 +60,42 @@ cdef extern from "_fornav_templates.h":
weight_type * wtab

cdef int initialize_weight(size_t chan_count, unsigned int weight_count, weight_type weight_min, weight_type weight_distance_max,
weight_type weight_delta_max, weight_type weight_sum_min, ewa_weight * ewaw)
cdef void deinitialize_weight(ewa_weight * ewaw)
cdef accum_type ** initialize_grid_accums(size_t chan_count, size_t grid_cols, size_t grid_rows)
cdef weight_type ** initialize_grid_weights(size_t chan_count, size_t grid_cols, size_t grid_rows)
cdef void deinitialize_grids(size_t chan_count, void ** grids)
weight_type weight_delta_max, weight_type weight_sum_min, ewa_weight * ewaw) nogil
cdef void deinitialize_weight(ewa_weight * ewaw) nogil
cdef accum_type ** initialize_grid_accums(size_t chan_count, size_t grid_cols, size_t grid_rows) nogil
cdef weight_type ** initialize_grid_weights(size_t chan_count, size_t grid_cols, size_t grid_rows) nogil
cdef void deinitialize_grids(size_t chan_count, void ** grids) nogil

cdef int compute_ewa_parameters[CR_TYPE](size_t swath_cols, size_t swath_rows,
CR_TYPE * uimg, CR_TYPE * vimg, ewa_weight * ewaw, ewa_parameters * ewap)
CR_TYPE * uimg, CR_TYPE * vimg, ewa_weight * ewaw, ewa_parameters * ewap) nogil

cdef int compute_ewa[CR_TYPE, IMAGE_TYPE](
size_t chan_count, bint maximum_weight_mode,
size_t swath_cols, size_t swath_rows, size_t grid_cols, size_t grid_rows,
CR_TYPE * uimg, CR_TYPE * vimg,
IMAGE_TYPE ** images, IMAGE_TYPE img_fill, accum_type ** grid_accums, weight_type ** grid_weights,
ewa_weight * ewaw, ewa_parameters * ewap)
ewa_weight * ewaw, ewa_parameters * ewap) nogil

cdef int compute_ewa_single[CR_TYPE, IMAGE_TYPE](
bint maximum_weight_mode,
size_t swath_cols, size_t swath_rows, size_t grid_cols, size_t grid_rows,
CR_TYPE * uimg, CR_TYPE * vimg,
IMAGE_TYPE * image, IMAGE_TYPE img_fill, accum_type * grid_accum, weight_type * grid_weight,
ewa_weight * ewaw, ewa_parameters * ewap)
ewa_weight * ewaw, ewa_parameters * ewap) nogil

# For some reason cython can't deduce the type when using the template
# cdef int write_grid_image[GRID_TYPE](GRID_TYPE *output_image, GRID_TYPE fill, size_t grid_cols, size_t grid_rows,
# accum_type *grid_accum, weight_type *grid_weights,
# int maximum_weight_mode, weight_type weight_sum_min)
cdef unsigned int write_grid_image(numpy.float32_t * output_image, numpy.float32_t fill, size_t grid_cols, size_t grid_rows,
accum_type * grid_accum, weight_type * grid_weights,
int maximum_weight_mode, weight_type weight_sum_min)
int maximum_weight_mode, weight_type weight_sum_min) nogil
cdef unsigned int write_grid_image(numpy.float64_t * output_image, numpy.float64_t fill, size_t grid_cols, size_t grid_rows,
accum_type * grid_accum, weight_type * grid_weights,
int maximum_weight_mode, weight_type weight_sum_min)
int maximum_weight_mode, weight_type weight_sum_min) nogil
cdef unsigned int write_grid_image(numpy.int8_t * output_image, numpy.int8_t fill, size_t grid_cols, size_t grid_rows,
accum_type * grid_accum, weight_type * grid_weights,
int maximum_weight_mode, weight_type weight_sum_min)
int maximum_weight_mode, weight_type weight_sum_min) nogil

ctypedef fused cr_dtype:
numpy.float32_t
Expand All @@ -124,7 +120,7 @@ cdef int fornav(unsigned int * valid_list, size_t chan_count, size_t swath_cols,
image_dtype ** input_arrays, grid_dtype ** output_arrays,
image_dtype input_fill, grid_dtype output_fill, size_t rows_per_scan,
unsigned int weight_count, weight_type weight_min, weight_type weight_distance_max, weight_type weight_delta_max,
weight_type weight_sum_min, bint maximum_weight_mode) except -1:
weight_type weight_sum_min, bint maximum_weight_mode) nogil except -1:
cdef unsigned int row_idx
cdef unsigned int idx
cdef bint got_point = 0
Expand Down Expand Up @@ -279,54 +275,74 @@ def fornav_wrapper(numpy.ndarray[cr_dtype, ndim=2, mode='c'] cols_array,
if not all(output_array.dtype == out_type for output_array in output_arrays):
raise ValueError("Input arrays must all be of the same data type")

cdef void ** input_pointer = <void ** >malloc(num_items * sizeof(void * ))
cdef void** input_pointer = <void ** >malloc(num_items * sizeof(void * ))
if not input_pointer:
raise MemoryError()
cdef void ** output_pointer = <void ** >malloc(num_items * sizeof(void * ))
cdef void** output_pointer = <void ** >malloc(num_items * sizeof(void * ))
if not output_pointer:
raise MemoryError()
cdef unsigned int * valid_arr = <unsigned int * >malloc(num_items * sizeof(unsigned int))
valid_list = []
cdef numpy.ndarray[numpy.float32_t, ndim= 2] tmp_arr_f32
cdef numpy.ndarray[numpy.float64_t, ndim= 2] tmp_arr_f64
cdef numpy.ndarray[numpy.int8_t, ndim= 2] tmp_arr_i8
cdef cr_dtype * cols_pointer = &cols_array[0, 0]
cdef cr_dtype * rows_pointer = &rows_array[0, 0]
cdef bint mwm = maximum_weight_mode
cdef int func_result
cdef numpy.float32_t input_fill_f32
cdef numpy.float64_t input_fill_f64
cdef numpy.int8_t input_fill_i8
cdef numpy.float32_t output_fill_f32
cdef numpy.float64_t output_fill_f64
cdef numpy.int8_t output_fill_i8
cdef numpy.ndarray[numpy.float32_t, ndim= 2] tmp_arr_f32
cdef numpy.ndarray[numpy.float64_t, ndim= 2] tmp_arr_f64
cdef numpy.ndarray[numpy.int8_t, ndim= 2] tmp_arr_i8
cdef cr_dtype[:, ::1] tmp_arr

if in_type == numpy.float32:
input_fill_f32 = <numpy.float32_t>input_fill
output_fill_f32 = <numpy.float32_t>output_fill
for i in range(num_items):
tmp_arr_f32 = input_arrays[i]
input_pointer[i] = &tmp_arr_f32[0, 0]
tmp_arr_f32 = output_arrays[i]
output_pointer[i] = &tmp_arr_f32[0, 0]
func_result = fornav(valid_arr, num_items, swath_cols, swath_rows, grid_cols, grid_rows, cols_pointer, rows_pointer,
< numpy.float32_t ** >input_pointer, < numpy.float32_t ** >output_pointer,
< numpy.float32_t > input_fill, < numpy.float32_t > output_fill, rows_per_scan,
weight_count, weight_min, weight_distance_max, weight_delta_max, weight_sum_min,
< bint > maximum_weight_mode)
with nogil:
func_result = fornav(valid_arr, num_items, swath_cols, swath_rows, grid_cols, grid_rows,
cols_pointer, rows_pointer,
< numpy.float32_t ** >input_pointer, < numpy.float32_t ** >output_pointer,
output_fill_f32, output_fill_f32, rows_per_scan,
weight_count, weight_min, weight_distance_max, weight_delta_max, weight_sum_min,
mwm)
elif in_type == numpy.float64:
input_fill_f64 = <numpy.float64_t>input_fill
output_fill_f64 = <numpy.float64_t>output_fill
for i in range(num_items):
tmp_arr_f64 = input_arrays[i]
input_pointer[i] = &tmp_arr_f64[0, 0]
tmp_arr_f64 = output_arrays[i]
output_pointer[i] = &tmp_arr_f64[0, 0]
func_result = fornav(valid_arr, num_items, swath_cols, swath_rows, grid_cols, grid_rows, cols_pointer, rows_pointer,
< numpy.float64_t ** >input_pointer, < numpy.float64_t ** >output_pointer,
< numpy.float64_t > input_fill, < numpy.float64_t > output_fill, rows_per_scan,
weight_count, weight_min, weight_distance_max, weight_delta_max, weight_sum_min,
< bint > maximum_weight_mode)
with nogil:
func_result = fornav(valid_arr, num_items, swath_cols, swath_rows, grid_cols, grid_rows,
cols_pointer, rows_pointer,
< numpy.float64_t ** >input_pointer, < numpy.float64_t ** >output_pointer,
input_fill_f64, output_fill_f64, rows_per_scan,
weight_count, weight_min, weight_distance_max, weight_delta_max, weight_sum_min,
mwm)
elif in_type == numpy.int8:
input_fill_i8 = <numpy.int8_t>input_fill
output_fill_i8 = <numpy.int8_t>output_fill
for i in range(num_items):
tmp_arr_i8 = input_arrays[i]
input_pointer[i] = &tmp_arr_i8[0, 0]
tmp_arr_i8 = output_arrays[i]
output_pointer[i] = &tmp_arr_i8[0, 0]
func_result = fornav(valid_arr, num_items, swath_cols, swath_rows, grid_cols, grid_rows, cols_pointer, rows_pointer,
< numpy.int8_t ** >input_pointer, < numpy.int8_t ** >output_pointer,
< numpy.int8_t > input_fill, < numpy.int8_t > output_fill, rows_per_scan,
weight_count, weight_min, weight_distance_max, weight_delta_max, weight_sum_min,
< bint > maximum_weight_mode)
with nogil:
func_result = fornav(valid_arr, num_items, swath_cols, swath_rows, grid_cols, grid_rows,
cols_pointer, rows_pointer,
< numpy.int8_t ** >input_pointer, < numpy.int8_t ** >output_pointer,
input_fill_i8, output_fill_i8, rows_per_scan,
weight_count, weight_min, weight_distance_max, weight_delta_max, weight_sum_min,
mwm)
else:
raise ValueError("Unknown input and output data type")

Expand All @@ -347,7 +363,7 @@ cdef int fornav_weights_and_sums(
image_dtype * input_array, weight_type * grid_weights, accum_type * grid_accums,
image_dtype input_fill, grid_dtype output_fill, size_t rows_per_scan,
unsigned int weight_count, weight_type weight_min, weight_type weight_distance_max, weight_type weight_delta_max,
weight_type weight_sum_min, bint maximum_weight_mode) except -1:
weight_type weight_sum_min, bint maximum_weight_mode) nogil except -1:
"""Get the weights and sums arrays from the fornav algorithm.
Typically fornav performs the entire operation of computing the weights
Expand Down Expand Up @@ -482,12 +498,14 @@ def fornav_weights_and_sums_wrapper(numpy.ndarray[cr_dtype, ndim=2, mode='c'] co
cdef weight_type * weights_pointer = &grid_weights[0, 0]
cdef accum_type * accums_pointer = &grid_accums[0, 0]
cdef int got_point
cdef bint mwm = maximum_weight_mode

ret_val = fornav_weights_and_sums(swath_cols, swath_rows, grid_cols, grid_rows, cols_pointer, rows_pointer,
input_pointer, weights_pointer, accums_pointer,
input_fill, output_fill, rows_per_scan,
weight_count, weight_min, weight_distance_max, weight_delta_max, weight_sum_min,
< bint > maximum_weight_mode)
with nogil:
ret_val = fornav_weights_and_sums(swath_cols, swath_rows, grid_cols, grid_rows, cols_pointer, rows_pointer,
input_pointer, weights_pointer, accums_pointer,
input_fill, output_fill, rows_per_scan,
weight_count, weight_min, weight_distance_max, weight_delta_max, weight_sum_min,
mwm)

succeeded = ret_val == 0
return succeeded
Expand All @@ -501,8 +519,11 @@ def write_grid_image_single(numpy.ndarray[grid_dtype, ndim=2, mode='c'] output_a
grid_dtype output_fill,
weight_type weight_sum_min=-1.0,
cpython.bool maximum_weight_mode=False):
# unsigned int write_grid_image(GRID_TYPE *output_image, GRID_TYPE fill, size_t grid_cols, size_t grid_rows,
# accum_type *grid_accum, weight_type *grid_weights,
# int maximum_weight_mode, weight_type weight_sum_min) {
return write_grid_image(& output_array[0, 0], output_fill, < size_t > output_array.shape[1], < size_t > output_array.shape[0],
& grid_accums[0, 0], & grid_weights[0, 0], < int > maximum_weight_mode, weight_sum_min)
cdef int mwm = <int>maximum_weight_mode
cdef size_t grid_cols = <size_t>output_array.shape[1]
cdef size_t grid_rows = <size_t>output_array.shape[0]
cdef unsigned int result
with nogil:
result = write_grid_image(& output_array[0, 0], output_fill, grid_cols, grid_rows,
& grid_accums[0, 0], & grid_weights[0, 0], mwm, weight_sum_min)
return result
37 changes: 20 additions & 17 deletions pyresample/ewa/_ll2cr.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -226,39 +226,42 @@ def ll2cr_static(numpy.ndarray[cr_dtype, ndim=2] lon_arr, numpy.ndarray[cr_dtype
Note longitude and latitude arrays are limited to 64-bit floats because
of limitations in pyproj.
"""
# TODO: Rewrite so it is no GIL
# pure python stuff for now
p = Proj(proj4_definition)

# Pyproj currently makes a copy so we don't have to do anything special here
cdef tuple projected_tuple = p(lon_arr, lat_arr)
cdef cr_dtype[:, ::1] rows_out = projected_tuple[1]
cdef cr_dtype[:, ::1] cols_out = projected_tuple[0]
cdef cr_dtype[:, ::1] lons_view = lon_arr
cdef cr_dtype[:, ::1] lats_view = lat_arr

# indexes
cdef unsigned int row
cdef unsigned int col
# index bounds
cdef unsigned int num_rows = lon_arr.shape[0]
cdef unsigned int num_cols = lon_arr.shape[1]
cdef unsigned int num_rows = lons_view.shape[0]
cdef unsigned int num_cols = lons_view.shape[1]
cdef cr_dtype x_tmp
cdef cr_dtype y_tmp
cdef unsigned int points_in_grid = 0

for row in range(num_rows):
for col in range(num_cols):
x_tmp = cols_out[row, col]
y_tmp = rows_out[row, col]
if x_tmp >= 1e30:
lon_arr[row, col] = fill_in
lat_arr[row, col] = fill_in
continue
with nogil:
for row in range(num_rows):
for col in range(num_cols):
x_tmp = cols_out[row, col]
y_tmp = rows_out[row, col]
if x_tmp >= 1e30:
lons_view[row, col] = fill_in
lats_view[row, col] = fill_in
continue

x_tmp = (x_tmp - origin_x) / cell_width
y_tmp = (y_tmp - origin_y) / cell_height
if x_tmp >= -1 and x_tmp <= width + 1 and y_tmp >= -1 and y_tmp <= height + 1:
points_in_grid += 1
lon_arr[row, col] = x_tmp
lat_arr[row, col] = y_tmp
x_tmp = (x_tmp - origin_x) / cell_width
y_tmp = (y_tmp - origin_y) / cell_height
if x_tmp >= -1 and x_tmp <= width + 1 and y_tmp >= -1 and y_tmp <= height + 1:
points_in_grid += 1
lons_view[row, col] = x_tmp
lats_view[row, col] = y_tmp

# return points_in_grid, x_arr, y_arr
return points_in_grid

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