SARAHAI-DATACENTER

Overview



SARAHAI-DATACENTER is a patented and revolutionary datacenter branch-prediction solution to help optimize datacenter performance at scale. SARAHAI-DATACENTER is designed to easily customize your cluster’s real HPC environment. By extending the HPC integration points, machine learning pipeline, and scheduling logic, you can turn this into a robust HPC performance optimization system that continuously learns and adapts to your workload demands—fulfilling the vision of autonomous AI-driven HPC resource management.



SARAHAI-ABPv1_Extended ("DATACENTER") Datacenter branch prediction for performance and throughput optimization is inspired by U.S. Patent No. 11,308,384, aiming to:

Collect HPC metrics from an AI cluster or HPC job manager (e.g., NCCL, RCCL, or a custom API).
Persist these metrics in a local SQLite database for long-term analytics.
Use Machine Learning (a RandomForestRegressor) to predict network throughput based on relevant HPC features (GPU utilization, concurrency level, job queue length, GPU saturation, etc.).
Recommend HPC optimization decisions (“branch decisions”) that might reconfigure concurrency, scheduling policies, or cluster routes.
Provide a Web UI using Flask, including a minimal interactive dashboard with charted metrics and user scheduling controls.
Generate ODS/ODF reports from collected HPC data.
While the current code uses mock HPC data, you can replace the mock calls in the HPCManager class with real calls to your actual HPC or GPU cluster environment. Once integrated, SARAHAI-ABPv1_Extended can function as a flexible HPC performance monitoring and optimization tool.

2. Key Features

Autonomous Data Gathering

A DataGatherer class continuously collects cluster metrics (timestamp, GPU utilization, concurrency level, job queue length, etc.) and stores them in an SQLite database.
Includes placeholder logic (in HPCManager) to simulate integration with NCCL or RCCL.
Machine Learning Pipeline

A PerformancePredictor class trains a regression model on the collected metrics to predict network throughput (Gbps).
Incorporates HPC features: GPU utilization, latency, concurrency levels, queue length, and GPU saturation.
The minimal ML pipeline demonstrates how to incorporate HPC domain features into a scikit-learn model.
Branch Optimization

A BranchOptimizer class uses the trained model’s predictions and HPC domain logic to recommend reconfiguration steps.
The example logic includes naive checks, such as “If predicted throughput < 200 Gbps, reduce concurrency.” In production, this can be replaced with more nuanced HPC scheduling or routing logic.
SQLite Data Persistence

A DatabaseManager class manages the storage of HPC metrics and user scheduling inputs in SQLite.
It makes it easy to persist data for analytics, trending, and historical reporting.
User Interface (Web Dashboard)

A Flask-based dashboard displays HPC metrics over time using Plotly (interactive JavaScript charts).
Includes a simple form for users to set concurrency levels and job priority, simulating HPC scheduling actions.
Report Generation

A ReportGenerator class creates ODS spreadsheets containing the historical HPC metrics.
Useful for auditing or external reporting.
Dependency Installation on First Run

Checks for required Python packages and installs them if missing.
In production, you typically manage dependencies via containers or a requirements file.