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jasonb5 · 2023-08-17T00:13:48Z

Description

After some analysis, I determined we were losing some performance moving back and forth between xarray and numpy.

The first fix ensures we're doing all the heavy computation in numpy. This has reduce the example time from ~4.8583 to ~1.6833.

There's still some more room for improvement.

Closes [Bug]: slow regrid2 operation #506
Closes [Bug]: NaN handling in regrid2 #580
Closes [Bug]: Regrid2Regridder yields NANs #599

Checklist

My code follows the style guidelines of this project
I have performed a self-review of my own code
My changes generate no new warnings
Any dependent changes have been merged and published in downstream modules

If applicable:

I have added tests that prove my fix is effective or that my feature works
New and existing unit tests pass with my changes (locally and CI/CD build)
I have commented my code, particularly in hard-to-understand areas
I have made corresponding changes to the documentation
I have noted that this is a breaking change for a major release (fix or feature that would cause existing functionality to not work as expected)

codecov · 2023-08-17T00:17:19Z

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 100.00%. Comparing base (78dedf0) to head (e2125f9).

Additional details and impacted files

@@            Coverage Diff            @@
##              main      #533   +/-   ##
=========================================
  Coverage   100.00%   100.00%           
=========================================
  Files           15        15           
  Lines         1602      1588   -14     
=========================================
- Hits          1602      1588   -14

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tomvothecoder · 2023-09-26T17:08:13Z

Hey @jasonb5 just checking in to see your estimated timeline for when this will be ready for review and merge.

I'm shooting to have xCDAT v0.6.0 released in the next week or so.

tomvothecoder · 2023-09-27T16:40:14Z

Notes from 9/13/23 meeting:

Xarray performance overhead might be from Pandas Index, other things like dims, labels, etc.
- Creating the objects might have an overhead
- Xarray operations should be equivalent to numpy performance, but creating
- Do more research here for

tomvothecoder · 2023-10-11T17:50:51Z

10/11/23 Meeting Notes:

Next steps:

Focus on Python optimizations first
- Overhead in unavoidable operations: constructing Xarray objects with numpy arrays
- 12 calls to get_bounds(), .2 sec
- Explore compatibility with ufunc to take advantage of parallelization
@lee1043 will test performance with Python optimizations to see if it is now reasonable with large datasets
- If still not reasonable, we can look into C based optimizations
- Most time spent is on calculating means
  - Cython is a possible solution, changes build process -- @jasonb5 already started looking into Cython, might be able to fit it in this PR if implementation and build process are straightforward and performance is dramatically improved
  - Numba is another option (just-in-time compiler) -- consider adding on top of Cython implementation for more performance improvements

xcdat/regridder/regrid2.py

tomvothecoder · 2024-02-12T21:45:47Z

Any status updates here?

jasonb5 · 2024-02-12T22:35:34Z

@tomvothecoder @lee1043 @chengzhuzhang @pochedls
Here's the latest performance result's

	Input shape	Output shape	CDMS2 (seconds)	xCDAT (seconds)	Diff Mean	Diff Max	Diff Min
0	(50, 20, 180, 288)	(50, 20, 362, 720)	36.0516	16.3418	3.1614e-08	8.39233e-05	-0.000119209
1	(50, 180, 288)	(50, 362, 720)	0.414103	6.87195	3.1614e-08	8.39233e-05	-0.000119209
2	(180, 288)	(362, 720)	0.0105002	5.15758	3.1614e-08	8.39233e-05	-0.000119209
3	(50, 20, 180, 288)	(50, 20, 121, 240)	2.22065	3.87692	4.32748e-08	1.33514e-05	-3.24249e-05
4	(50, 180, 288)	(50, 121, 240)	0.0767616	1.66283	4.32748e-08	1.33514e-05	-3.24249e-05
5	(180, 288)	(121, 240)	0.00450883	0.74982	4.32748e-08	1.33514e-05	-3.24249e-05

I placed the notebook under /shared/regrid2_perf on jupyterhub if you want to checkout the notebook.

If everything looks alright lets merge and I'll get out the next few fixes and continue working on performance.

tomvothecoder · 2024-02-12T22:55:21Z

Thanks @jasonb5. I will review the code changes this week.

Maybe @lee1043 can test this branch on his code?

lee1043 · 2024-02-12T22:57:53Z

Thank you for the update. Sure, I can test the branch.

tomvothecoder

Hi @jasonb5, here's my initial code review with questions and minor suggestions.

xcdat/regridder/regrid2.py

tomvothecoder · 2024-02-16T21:20:14Z

xcdat/regridder/regrid2.py

+    mapping = [
+        np.where(
            np.logical_and(
-                shifted_src_west < dst_east[i], shifted_src_east > dst_west[i]
+                shifted_src_west < dst_east[x], shifted_src_east > dst_west[x]
            )
        )[0]
+        for x in range(dst_length)
+    ]

-        weight = np.minimum(dst_east[i], shifted_src_east[contrib]) - np.maximum(
-            dst_west[i], shifted_src_west[contrib]
-        )
-
-        weights.append(weight.values.reshape(1, contrib.shape[0]))
-
-        contrib += shift
+    weights = [
+        (
+            np.minimum(dst_east[x], shifted_src_east[y])
+            - np.maximum(dst_west[x], shifted_src_west[y])
+        ).reshape((1, -1))
+        for x, y in enumerate(mapping)
+    ]


Same comment about adding comment to explain logic and purpose

Resolved with 7695e2b

tomvothecoder · 2024-02-16T21:24:09Z

xcdat/regridder/regrid2.py

+    name = input_data_var.cf.axes[cf_axis_name]
+
+    if isinstance(name, list):
+        name = name[0]


Unless the intent here is to only interpret the axis CF attribute?

Instead of using cf_xarray directly, I think you can use xc.get_dim_keys() which can also interpret the standard_name attribute or use the xCDAT fall-back table of generally accepted axis names.

I could use get_dim_keys but we will be trading performance for robustness. If we accept this I'm fine making the change.

I see. Is the performance hit significant using get_dim_keys()? If so, I think it is fine to only interpret the axis attribute for performance.

@lee1043 any thoughts?

As this part of the code is in the back-end level that less likely be accessed by users, I would prefer prioritizing performance, unless the robustness trading off is too significant. How much performance change this would make?

Try passing data variable directly to get_dim_keys()

Looks like using get_dim_keys works just fine and no decrease in performance, actually a small increase.

tomvothecoder · 2024-02-16T21:26:56Z

xcdat/regridder/regrid2.py

+    try:
+        name = ds.cf.bounds[axis][0]
+    except (KeyError, IndexError):
+        raise RuntimeError(f"Could not determine {axis!r} bounds")


Suggested change

try:

name = ds.cf.bounds[axis][0]

except (KeyError, IndexError):

raise RuntimeError(f"Could not determine {axis!r} bounds")

try:

name = ds.bounds.get_bounds(axis)

except (ValueError, KeyError):

raise RuntimeError(f"Could not determine {axis!r} bounds")

I think you can use xCDAT's ds.bounds.get_bounds().

As from above I could use get_bounds but we're again trading performance for robustness.

Gotcha. If the current implementation is faster and using .get_bounds() isn't necessary, we can keep your current implementation.

After some review I'm going to leave the ds.cf.bounds usage. In this case it's fine to use as it's being called on the input/output grid, both of which have been generated/validated by previous code. We can guarantee that both grid objects only have lat/lon coordinates/bounds with the correct metadata thus we do not need the robustness of ds.bounds.get_bounds.

xcdat/regridder/regrid2.py

tomvothecoder · 2024-02-16T21:28:45Z

xcdat/regridder/regrid2.py

+    for y in range(y_length):
+        y_seg = np.take(input_data, lat_mapping[y], axis=y_index)

-            for lon_index, lon_map in enumerate(self._lon_mapping):
-                lon_weight = self._lon_weights[lon_index]
+        for x in range(x_length):
+            x_seg = np.take(y_seg, lon_mapping[x], axis=x_index, mode="wrap")

-                dot_weight = np.dot(lat_weight, lon_weight)
+            cell_weight = np.dot(lat_weights[y], lon_weights[x])

-                cell_weight = np.sum(dot_weight)
+            output_seg_index = y * x_length + x

-                input_lon_segment = np.take(
-                    input_lat_segment, lon_map, axis=input_lon_index
+            if is_2d:
+                output_data[output_seg_index] = np.divide(
+                    np.sum(
+                        np.multiply(x_seg, cell_weight),
+                        axis=(y_index, x_index),
+                    ),
+                    np.sum(cell_weight),
                )
-
-                data = (
-                    np.nansum(
-                        np.multiply(input_lon_segment, dot_weight),
-                        axis=(input_lat_index, input_lon_index),
-                    )
-                    / cell_weight
+            else:
+                output_seg = output_data[output_seg_index]
+
+                np.divide(
+                    np.sum(
+                        np.multiply(x_seg, cell_weight),
+                        axis=(y_index, x_index),
+                    ),
+                    np.sum(cell_weight),
+                    out=output_seg,


Comments explaining the logic here would be good. Maybe in the docstring.

Resolved with 7695e2b

lee1043

Sorry for the delayed approval. I thought I have marked approval but missed.

Fixes regrid2 operating on numpy arrays

5b1f3e5

jasonb5 changed the title ~~Regrid2 performance~~ Improving regrid2 performance Aug 17, 2023

tomvothecoder added this to the v0.6.0 milestone Sep 27, 2023

tomvothecoder assigned jasonb5 Sep 27, 2023

tomvothecoder added type: docs Updates to documentation type: enhancement New enhancement request and removed type: docs Updates to documentation labels Sep 27, 2023

tomvothecoder modified the milestones: FY23Q4 (07/01/23 - 09/30/23), FY24Q1 (10/01/23 - 12/31/23) Sep 27, 2023

tomvothecoder mentioned this pull request Oct 11, 2023

[Doc]: Add GitHub issues and pull request templates E3SM-Project/e3sm_diags#741

Merged

jasonb5 added 16 commits October 11, 2023 11:25

Merge branch 'main' into regrid2_performance

6ed8990

Removes regrid output mask

f50cf24

Fixes extracting input data variable

2e58cd7

Updates regrid2

df2d23a

Overrides dtype to match input

3d5d0d9

Fixes wrapping when using np.take

5f5e1be

Fixes copying variable attributes

dd671b4

Fixes typing errors

c6e65b7

Fixes correcting dtype before mapping/regridding

a68bf9f

Fixes tests

53989b7

Fixes latitude that was wrapping

e6fe7c9

Fixes copying coordinates when name is missmatched

934e028

Fixes shifting longitude

62fe454

Merge branch 'main' into regrid2_performance

d5d7f49

Fixes missmatched coordinate names

a2eb927

Fixes reshape/ordering output data

ea19fb0

tomvothecoder added priority: soon Should be addressed soon. priority: now Requires immediate attention and removed priority: soon Should be addressed soon. labels Dec 19, 2023

tomvothecoder reviewed Dec 20, 2023

View reviewed changes

xcdat/regridder/regrid2.py Show resolved Hide resolved

tomvothecoder modified the milestones: FY24Q1 (10/01/23 - 12/31/23), FY24Q2 (01/01/24 - 03/31/24) Jan 16, 2024

jasonb5 added 4 commits January 19, 2024 13:20

Fixes masking

bcdc652

Fixes failing tests

3312be3

Merge branch 'main' into regrid2_performance

9377a88

Fixes tests

9d49dbe

Merge branch 'main' into regrid2_performance

ad61067

jasonb5 marked this pull request as ready for review February 12, 2024 22:41

tomvothecoder requested a review from lee1043 February 12, 2024 22:56

tomvothecoder changed the title ~~Improving regrid2 performance~~ [PR]: Improving regrid2 performance Feb 12, 2024

lee1043 mentioned this pull request Feb 13, 2024

[Bug]: slow regrid2 operation #506

Closed

tomvothecoder approved these changes Feb 16, 2024

View reviewed changes

Fixes docstrings and adds comments

96eaae2

lee1043 approved these changes Feb 27, 2024

View reviewed changes

jasonb5 added 3 commits February 28, 2024 09:52

Adds comments

7695e2b

Merge branch 'main' into regrid2_performance

a5bf57c

Replaces cf.axes with get_dim_keys

e2125f9

jasonb5 merged commit 38cdfd5 into xCDAT:main Mar 8, 2024

jasonb5 deleted the regrid2_performance branch March 8, 2024 21:51

This was referenced Mar 12, 2024

Update MoV code to use xCDAT PCMDI/pcmdi_metrics#1020

Merged

[Bug]: Regrid2Regridder yields NANs #599

Closed

tomvothecoder mentioned this pull request Mar 15, 2024

[Bug]: Regrid2 breaks in _output_axis_sizes() if non-dimension Z axis coords exists (e.g., height) #616

Closed

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jasonb5 commented Aug 17, 2023 •

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