Remove all restrictions to MPI rank zero #353
Conversation
WalkthroughThe update modifies the control flow in several functions within Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~8 minutes Poem
Note ⚡️ Unit Test Generation is now available in beta!Learn more here, or try it out under "Finishing Touches" below. ✨ Finishing Touches
🧪 Generate unit tests
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. 🪧 TipsChatThere are 3 ways to chat with CodeRabbit:
SupportNeed help? Create a ticket on our support page for assistance with any issues or questions. Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments. CodeRabbit Commands (Invoked using PR comments)
Other keywords and placeholders
CodeRabbit Configuration File (
|
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #353 +/- ##
=======================================
Coverage 81.12% 81.12%
=======================================
Files 5 5
Lines 551 551
=======================================
Hits 447 447
Misses 104 104 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
|
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 1
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
pylammpsmpi/mpi/lmpmpi.py(6 hunks)
🔇 Additional comments (16)
pylammpsmpi/mpi/lmpmpi.py (16)
82-83: Verify performance impact of unconditional execution on all ranks.The removal of rank restrictions means all MPI ranks will now execute
job.get_natoms()and process compute data. While this aligns with the PR objective, consider the potential performance overhead of redundant computations across all ranks.
89-89: LGTM - Version information accessible on all ranks.Making version information available on all MPI ranks is a reasonable change with minimal performance impact.
108-108: LGTM - Settings extraction available on all ranks.Allowing all ranks to extract settings is consistent with the PR objective and poses minimal risk.
112-112: LGTM - Global data extraction available on all ranks.Extracting global data on all ranks should be safe since global data is consistent across MPI ranks, though it may result in redundant operations.
116-116: LGTM - Box extraction available on all ranks.Box information should be consistent across MPI ranks, making this change safe and aligned with the PR objective.
149-149: LGTM - Fix extraction available on all ranks.Allowing fix data extraction on all ranks is consistent with the overall changes and should be safe.
159-160: LGTM - Variable extraction properly handles both array and scalar cases.The function correctly uses
_gather_data_from_all_processorsfor array variables while making scalar variables available on all ranks. This maintains data consistency while achieving the PR objective.
170-170: LGTM - Atom count available on all ranks.The number of atoms should be consistent across ranks, making this change safe and appropriate.
297-297: LGTM - Neighbor list access available on all ranks.Making neighbor list access available on all ranks is reasonable, though note that each rank may return its local neighbor list data.
301-301: LGTM - Pair neighbor list finding available on all ranks.This change is consistent with other neighbor list related functions and should be safe.
305-305: LGTM - Fix neighbor list finding available on all ranks.This change maintains consistency with other neighbor list operations.
309-309: LGTM - Compute neighbor list finding available on all ranks.This change is consistent with the pattern established for other neighbor list functions.
313-313: LGTM - Neighbor list size available on all ranks.This change allows all ranks to query neighbor list sizes, which is reasonable and safe.
317-317: LGTM - Neighbor list element access available on all ranks.Allowing all ranks to access neighbor list elements is consistent with the overall changes.
321-321: LGTM - Thermodynamic data available on all ranks.Thermodynamic data should be consistent across MPI ranks, making this a safe change that aligns with the PR objective.
431-431: Verify memory implications of replicating gathered data on all ranks.The helper function now returns gathered data on all ranks instead of just rank 0. While this aligns with the PR objective, it could significantly increase memory usage when large datasets are replicated across all MPI ranks.
Consider the memory footprint impact, especially for large simulations with many atoms or extensive computed data.
| # extract atoms return an internal data type | ||
| # this has to be reformatted | ||
| name = str(funct_args[0]) | ||
| if name not in atom_properties: | ||
| return [] | ||
|
|
||
| # this block prevents error when trying to access values | ||
| # that do not exist | ||
| try: | ||
| val = job.extract_atom(name, atom_properties[name]["type"]) | ||
| except ValueError: | ||
| return [] | ||
| # this is per atom quantity - so get | ||
| # number of atoms - first dimension | ||
| natoms = job.get_natoms() | ||
| # second dim is from dict | ||
| dim = atom_properties[name]["dim"] | ||
| data = [] | ||
| if dim > 1: | ||
| for i in range(int(natoms)): | ||
| dummy = [val[i][x] for x in range(dim)] | ||
| data.append(dummy) | ||
| else: | ||
| data = [val[x] for x in range(int(natoms))] | ||
| # this block prevents error when trying to access values | ||
| # that do not exist | ||
| try: | ||
| val = job.extract_atom(name, atom_properties[name]["type"]) | ||
| except ValueError: | ||
| return [] | ||
| # this is per atom quantity - so get | ||
| # number of atoms - first dimension | ||
| natoms = job.get_natoms() | ||
| # second dim is from dict | ||
| dim = atom_properties[name]["dim"] | ||
| data = [] | ||
| if dim > 1: | ||
| for i in range(int(natoms)): | ||
| dummy = [val[i][x] for x in range(dim)] | ||
| data.append(dummy) | ||
| else: | ||
| data = [val[x] for x in range(int(natoms))] | ||
|
|
||
| return np.array(data) | ||
| return np.array(data) |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Verify completeness of atom data across MPI ranks.
The extract_atom function now executes on all ranks, but unlike extract_compute or extract_variable, it doesn't use _gather_data_from_all_processors() to collect data from all ranks. This means each rank will only return its local subset of atoms, potentially leading to incomplete datasets.
Consider whether this function should gather data from all processors or if the current behavior is intentional:
🏁 Script executed:
#!/bin/bash
# Search for other atom extraction functions to compare patterns
rg -A 10 -B 2 "_gather_data_from_all_processors" Length of output: 2401
Gather per-atom data across all MPI ranks in extract_atom
The extract_atom implementation in pylammpsmpi/mpi/lmpmpi.py (lines 120–145) currently returns only the local subset of atoms on each MPI rank. To mirror the behavior of extract_compute and extract_variable, you should collect data from all ranks using _gather_data_from_all_processors.
Suggested changes:
• File: pylammpsmpi/mpi/lmpmpi.py
• Function: extract_atom
• Location: just before return np.array(data)
else:
data = [val[x] for x in range(int(natoms))]
- return np.array(data)
+ # gather lists from all ranks into a single list on rank 0
+ data = _gather_data_from_all_processors(data)
+ return np.array(data)This ensures the root process returns the complete per-atom dataset rather than only its local slice.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| # extract atoms return an internal data type | |
| # this has to be reformatted | |
| name = str(funct_args[0]) | |
| if name not in atom_properties: | |
| return [] | |
| # this block prevents error when trying to access values | |
| # that do not exist | |
| try: | |
| val = job.extract_atom(name, atom_properties[name]["type"]) | |
| except ValueError: | |
| return [] | |
| # this is per atom quantity - so get | |
| # number of atoms - first dimension | |
| natoms = job.get_natoms() | |
| # second dim is from dict | |
| dim = atom_properties[name]["dim"] | |
| data = [] | |
| if dim > 1: | |
| for i in range(int(natoms)): | |
| dummy = [val[i][x] for x in range(dim)] | |
| data.append(dummy) | |
| else: | |
| data = [val[x] for x in range(int(natoms))] | |
| # this block prevents error when trying to access values | |
| # that do not exist | |
| try: | |
| val = job.extract_atom(name, atom_properties[name]["type"]) | |
| except ValueError: | |
| return [] | |
| # this is per atom quantity - so get | |
| # number of atoms - first dimension | |
| natoms = job.get_natoms() | |
| # second dim is from dict | |
| dim = atom_properties[name]["dim"] | |
| data = [] | |
| if dim > 1: | |
| for i in range(int(natoms)): | |
| dummy = [val[i][x] for x in range(dim)] | |
| data.append(dummy) | |
| else: | |
| data = [val[x] for x in range(int(natoms))] | |
| return np.array(data) | |
| return np.array(data) | |
| # extract atoms return an internal data type | |
| # this has to be reformatted | |
| name = str(funct_args[0]) | |
| if name not in atom_properties: | |
| return [] | |
| # this block prevents error when trying to access values | |
| # that do not exist | |
| try: | |
| val = job.extract_atom(name, atom_properties[name]["type"]) | |
| except ValueError: | |
| return [] | |
| # this is per atom quantity - so get | |
| # number of atoms - first dimension | |
| natoms = job.get_natoms() | |
| # second dim is from dict | |
| dim = atom_properties[name]["dim"] | |
| data = [] | |
| if dim > 1: | |
| for i in range(int(natoms)): | |
| dummy = [val[i][x] for x in range(dim)] | |
| data.append(dummy) | |
| else: | |
| data = [val[x] for x in range(int(natoms))] | |
| # gather lists from all ranks into a single list on rank 0 | |
| data = _gather_data_from_all_processors(data) | |
| return np.array(data) |
🤖 Prompt for AI Agents
In pylammpsmpi/mpi/lmpmpi.py between lines 120 and 145, the extract_atom
function currently returns only the local atom data for each MPI rank. To fix
this, before the line returning np.array(data), call the
_gather_data_from_all_processors function with data as the argument to collect
and combine data from all MPI ranks. This will ensure the root process returns
the complete per-atom dataset instead of just its local portion.
Summary by CodeRabbit