stormlog.analyzer

Advanced analysis tools for memory profiling data.

Classes

`MemoryAnalyzer`([profiler, ...])	Advanced analyzer for memory profiling data.
`MemoryPattern`(pattern_type, description, ...)	Represents a detected memory usage pattern.
`PerformanceInsight`(category, title, ...)	Performance insight derived from profiling data.

class stormlog.analyzer.MemoryPattern(pattern_type, description, severity, affected_functions, metrics, suggestions)[source]

Bases: object

Represents a detected memory usage pattern.

Parameters:

pattern_type (str)
description (str)
severity (str)
affected_functions (List[str])
metrics (Dict[str, Any])
suggestions (List[str])

pattern_type: str

description: str

severity: str

affected_functions: List[str]

metrics: Dict[str, Any]

suggestions: List[str]

class stormlog.analyzer.PerformanceInsight(category, title, description, impact, confidence, data, recommendations)[source]

Bases: object

Performance insight derived from profiling data.

Parameters:

category (str)
title (str)
description (str)
impact (str)
confidence (float)
data (Dict[str, Any])
recommendations (List[str])

category: str

title: str

description: str

impact: str

confidence: float

data: Dict[str, Any]

recommendations: List[str]

class stormlog.analyzer.MemoryAnalyzer(profiler=None, collective_sensitivity='medium', collective_threshold_overrides=None)[source]

Bases: object

Advanced analyzer for memory profiling data.

Parameters:

profiler (GPUMemoryProfiler | None)
collective_sensitivity (str)
collective_threshold_overrides (Mapping[str, Any] | None)

analyze_memory_patterns(results=None)[source]

Detect memory usage patterns in profiling data.

Parameters:: results (List[ProfileResult] | None) – List of ProfileResults to analyze
Returns:: List of detected patterns
Return type:: List[MemoryPattern]

generate_performance_insights(results=None)[source]

Generate performance insights from profiling data.

Parameters:: results (List[ProfileResult] | None) – List of ProfileResults to analyze
Returns:: List of performance insights
Return type:: List[PerformanceInsight]

analyze_memory_gaps(events, *, phase_resolver=None)[source]

Classify allocator-vs-device hidden memory gaps over time.

Parameters:

events (List[TelemetryEventV2]) – Chronologically ordered telemetry samples.
phase_resolver (PhaseReplayIndex | None)

Returns:

Prioritized list of gap findings (severity desc, confidence desc).

Return type:

List[GapFinding]

analyze_cross_rank_timeline(events, *, phase_resolver=None)[source]

Merge rank timelines and detect the earliest cluster-wide spike cause.

Parameters:

events (List[TelemetryEventV2])
phase_resolver (PhaseReplayIndex | None)

Return type:

Dict[str, Any]

analyze_collective_attribution(events, *, phase_resolver=None)[source]

Attribute hidden-memory spikes to collective communication phases.

Parameters:

events (List[TelemetryEventV2])
phase_resolver (PhaseReplayIndex | None)

Return type:

List[CollectiveAttributionResult]

generate_optimization_report(results=None, events=None)[source]

Generate a comprehensive optimization report.

Parameters:

results (List[ProfileResult] | None) – List of ProfileResults to analyze
events (List[TelemetryEventV2] | None) – Optional telemetry event series for gap analysis. When provided, the report includes a gap_analysis section.

Returns:

Comprehensive optimization report

Return type:

Dict[str, Any]