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[loop-generated] [optimization] Profile and optimize memory usage in large modules #1384

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opened 2026-03-24 10:48:32 +00:00 by Timmy · 2 comments
Timmy commented 2026-03-24 10:48:32 +00:00
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Priority: Medium
Impact: System performance, resource efficiency
Component: Performance optimization

Problem

Several large modules likely have memory inefficiencies:

  • cascade.py (1241 lines) - router allocations
  • multimodal.py (579 lines) - model loading
  • dispatcher.py (917 lines) - task management
  • voice_loop.py (572 lines) - audio processing

Large modules often accumulate memory hotspots over time.

Investigation Needed

  1. Profile memory usage in hot paths
  2. Identify allocation patterns
  3. Find opportunities for object reuse
  4. Check for memory leaks in long-running processes

Acceptance Criteria

  • Memory profiling report for each target module
  • Identify top 5 memory optimization opportunities
  • Implement fixes for obvious inefficiencies
  • Measure before/after memory usage
  • Document memory usage patterns

Tools

  • Use memory_profiler for Python profiling
  • Monitor allocation patterns during typical workloads
  • Focus on frequently-called code paths

Performance is sovereignty.

**Priority**: Medium **Impact**: System performance, resource efficiency **Component**: Performance optimization ## Problem Several large modules likely have memory inefficiencies: - `cascade.py` (1241 lines) - router allocations - `multimodal.py` (579 lines) - model loading - `dispatcher.py` (917 lines) - task management - `voice_loop.py` (572 lines) - audio processing Large modules often accumulate memory hotspots over time. ## Investigation Needed 1. Profile memory usage in hot paths 2. Identify allocation patterns 3. Find opportunities for object reuse 4. Check for memory leaks in long-running processes ## Acceptance Criteria - [ ] Memory profiling report for each target module - [ ] Identify top 5 memory optimization opportunities - [ ] Implement fixes for obvious inefficiencies - [ ] Measure before/after memory usage - [ ] Document memory usage patterns ## Tools - Use `memory_profiler` for Python profiling - Monitor allocation patterns during typical workloads - Focus on frequently-called code paths Performance is sovereignty.
Timmy commented 2026-03-24 12:08:39 +00:00
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Implementation Instructions for Kimi

Objective: Profile and optimize memory usage in large modules to reduce allocations.

Scope: Focus on the largest modules that are likely memory-heavy:

  • src/infrastructure/router/cascade.py (1241 lines)
  • src/dashboard/routes/world.py (1065 lines)
  • src/dashboard/app.py (780 lines)
  • src/timmy/cli.py (693 lines)

Implementation Steps:

  1. Add memory profiling utilities:

    # Create src/infrastructure/profiling/memory.py
import tracemalloc, psutil, functools

def profile_memory(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        tracemalloc.start()
        start_memory = psutil.Process().memory_info().rss
        result = func(*args, **kwargs)
        current, peak = tracemalloc.get_traced_memory()
        end_memory = psutil.Process().memory_info().rss
        print(f"{func.__name__}: {(end_memory-start_memory)/1024/1024:.1f}MB RSS, {peak/1024/1024:.1f}MB peak")
        tracemalloc.stop()
        return result
    return wrapper

  2. Profile the hotspots in each target module:

    • Add @profile_memory decorator to major functions
    • Run typical workloads and measure allocations
    • Identify the top 3 memory-hungry functions per module

  3. Apply common optimizations:

    • Use __slots__ for frequently created classes
    • Cache expensive computations with @lru_cache
    • Use generators instead of list comprehensions where appropriate
    • Replace string concatenation with f-strings or join()
    • Use weakref for circular references if found

  4. Document findings:
    Create docs/profiling/memory_optimization.md with:

    • Baseline memory usage per module
    • Optimization techniques applied
    • Performance improvements achieved

Acceptance Criteria:

  • Memory profiling utilities created and working
  • Each target module profiled with before/after measurements
  • At least 10% memory reduction in hotspot functions
  • Documentation of optimizations applied
  • No functionality regressions (tests still pass)

Test command: tox -e unit && python -m pytest tests/test_memory_usage.py -v

Files to create/modify:

  • src/infrastructure/profiling/memory.py (new)
  • docs/profiling/memory_optimization.md (new)
  • Target modules: apply optimizations, add profiling
  • tests/test_memory_usage.py (new test file)
## Implementation Instructions for Kimi **Objective:** Profile and optimize memory usage in large modules to reduce allocations. **Scope:** Focus on the largest modules that are likely memory-heavy: - `src/infrastructure/router/cascade.py` (1241 lines) - `src/dashboard/routes/world.py` (1065 lines) - `src/dashboard/app.py` (780 lines) - `src/timmy/cli.py` (693 lines) **Implementation Steps:** 1. **Add memory profiling utilities:** ```python # Create src/infrastructure/profiling/memory.py import tracemalloc, psutil, functools def profile_memory(func): @functools.wraps(func) def wrapper(*args, **kwargs): tracemalloc.start() start_memory = psutil.Process().memory_info().rss result = func(*args, **kwargs) current, peak = tracemalloc.get_traced_memory() end_memory = psutil.Process().memory_info().rss print(f"{func.__name__}: {(end_memory-start_memory)/1024/1024:.1f}MB RSS, {peak/1024/1024:.1f}MB peak") tracemalloc.stop() return result return wrapper ``` 2. **Profile the hotspots in each target module:** - Add `@profile_memory` decorator to major functions - Run typical workloads and measure allocations - Identify the top 3 memory-hungry functions per module 3. **Apply common optimizations:** - Use `__slots__` for frequently created classes - Cache expensive computations with `@lru_cache` - Use generators instead of list comprehensions where appropriate - Replace string concatenation with f-strings or join() - Use `weakref` for circular references if found 4. **Document findings:** Create `docs/profiling/memory_optimization.md` with: - Baseline memory usage per module - Optimization techniques applied - Performance improvements achieved **Acceptance Criteria:** - [ ] Memory profiling utilities created and working - [ ] Each target module profiled with before/after measurements - [ ] At least 10% memory reduction in hotspot functions - [ ] Documentation of optimizations applied - [ ] No functionality regressions (tests still pass) **Test command:** `tox -e unit && python -m pytest tests/test_memory_usage.py -v` **Files to create/modify:** - `src/infrastructure/profiling/memory.py` (new) - `docs/profiling/memory_optimization.md` (new) - Target modules: apply optimizations, add profiling - `tests/test_memory_usage.py` (new test file)
kimi was assigned by Timmy 2026-03-24 12:08:39 +00:00
kimi was unassigned by Timmy 2026-03-24 19:33:32 +00:00
Timmy commented 2026-03-24 20:08:59 +00:00
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[triage] Duplicate of #1445 (memory profiling). Closing to reduce queue noise.

[triage] Duplicate of #1445 (memory profiling). Closing to reduce queue noise.
Timmy closed this issue 2026-03-24 20:08:59 +00:00
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Reference: Rockachopa/Timmy-time-dashboard#1384
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