{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Analyzes PyTorch Profiler traces to identify performance bottlenecks in distributed training workloads by breaking down GPU time, kernel execution, communication overlap, and idle periods.","skillfed_tags":["pytorch-profiling","distributed-training","performance-analysis"],"use_cases":["Identify GPU idle time causes and communication bottlenecks in multi-GPU or multi-node training runs.","Compare performance profiles across different training configurations or hardware setups to validate optimization changes.","Analyze kernel launch overhead and memory bandwidth utilization to guide kernel-level optimization efforts.","Investigate temporal patterns of computation and communication overlap to improve distributed training efficiency.","Diagnose performance regressions by comparing trace files from different training runs side-by-side."],"what_it_does":"Holistic Trace Analysis is a performance profiling tool built to analyze PyTorch Profiler traces from distributed training workloads. It ingests trace files collected via PyTorch's Kineto profiler and provides structured breakdowns of GPU activity\u2014computation time, communication time, memory events, and idle periods\u2014across all training ranks. The package runs as a Python library, typically within Jupyter notebooks, and exposes an API for querying temporal breakdowns, kernel statistics, communication-computation overlap, memory bandwidth utilization, and queue lengths.\n\nThe tool helps identify where training time is spent and why GPUs are idle, which is essential for optimizing large-scale distributed training. It includes trace comparison capabilities to visualize differences between runs, CUDA kernel pattern analysis to find frequently launched operations, and experimental GPU performance counter analysis. Dependencies include numpy, pandas, networkx for data processing and plotly for visualization.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, installs with low friction, and solves a specific and important problem for anyone optimizing distributed PyTorch training. The MIT license imposes no restrictions. Install it if you profile distributed training workloads and need structured analysis of PyTorch Profiler traces."},"id":"holistictraceanalysis","links":{"html":"https://skillfed.io/packages/holistictraceanalysis","md":"https://skillfed.io/packages/holistictraceanalysis.md","pypi":"https://pypi.org/project/holistictraceanalysis/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-05-29","license_spdx":null,"license_treatment":"permissive","name":"HolisticTraceAnalysis","python_support":"supports_current","summary":"A python library for analyzing PyTorch Profiler traces"},"popularity":{"monthly_downloads":155871,"position":10798,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.0"}