LS-DYNA Monitoring & Analytics

Leverage Vantage License Manager's specialized monitoring capabilities to gain deep insights into your LS-DYNA license utilization across users, teams, departments, and clusters. This guide focuses on using Vantage's license usage dashboard to understand solver-specific usage patterns and parallel processing efficiency for optimal license management.

Vantage License Usage Dashboard

Real-Time LS-DYNA Monitoring

The Vantage dashboard provides live monitoring of your LSTC license infrastructure with specialized insights for LS-DYNA solver usage:

Live Solver Status Overview

# Enable real-time LS-DYNA monitoring
vantage licenses monitor lsdyna-server \
  --real-time \
  --refresh-interval 30s \
  --include-solvers explicit,implicit,em,thermal \
  --include-parallel-tokens smp,mpp

# View live solver status
vantage dashboard licenses --server lsdyna-server \
  --solver-view \
  --parallel-token-tracking

Dashboard Features:

• Solver-specific utilization meters: Live usage for Explicit, Implicit, EM, and Thermal solvers
• Parallel token allocation: Real-time SMP and MPP token distribution
• Active simulation table: Current users, solver types, parallel configuration, and runtime
• Queue status with solver awareness: Users waiting with solver and token requirements
• Performance metrics: Solver efficiency and parallel scaling effectiveness

LS-DYNA Solver Utilization Heatmap

# Dashboard configuration for solver utilization heatmap
apiVersion: dashboard.vantage.com/v1
kind: Panel
metadata:
  name: lsdyna-solver-utilization-heatmap
spec:
  title: "LS-DYNA Solver Utilization by Time and Type"
  type: heatmap
  timeRange: 7d
  queries:
  - query: |
      lsdyna_solver_usage_percent{server="lsdyna-server",solver=~"explicit|implicit|em|thermal"}
    groupBy: [hour, solver]
    aggregation: avg
  visualization:
    colorScheme: "blue-green-yellow-red"
    thresholds:
      - value: 60
        color: yellow
      - value: 80
        color: orange
      - value: 90
        color: red

Parallel Processing Analytics

SMP and MPP Token Utilization

# Configure parallel token monitoring
vantage analytics parallel-tokens configure \
  --server lsdyna-server \
  --track-smp-efficiency \
  --track-mpp-scaling \
  --optimize-token-allocation \
  --generate-scaling-recommendations

# View parallel token analytics
vantage dashboard parallel-tokens --server lsdyna-server \
  --efficiency-metrics \
  --scaling-analysis \
  --cost-optimization

Parallel Token Analytics Features:

• Token efficiency tracking: How effectively users utilize allocated SMP and MPP tokens
• Scaling performance: Parallel efficiency across different token counts
• Optimal allocation recommendations: Suggested token counts for different problem sizes
• Cost per token analysis: Economic efficiency of parallel processing usage
• Idle token detection: Identification of over-allocated parallel resources

User Analytics and Tracking

Individual User LS-DYNA Insights

Vantage provides detailed analytics for understanding individual LS-DYNA user behavior and solver preferences:

User Solver Preference Dashboard

# Configure user-specific LS-DYNA monitoring
vantage analytics users configure \
  --server lsdyna-server \
  --track-solver-preferences \
  --track-parallel-efficiency \
  --track-simulation-complexity \
  --generate-user-recommendations

# View user analytics
vantage dashboard users --server lsdyna-server \
  --metrics solver-usage,parallel-efficiency,cost \
  --time-range 30d \
  --include-solver-breakdown

User Analytics Features:

• Solver usage patterns: Which LS-DYNA solvers each user prefers and frequency
• Parallel processing efficiency: How effectively users scale their simulations
• Simulation complexity analysis: Problem size correlation with resource usage
• Cost attribution per user: Detailed cost breakdown by solver type and token usage
• User productivity metrics: Solver throughput and time-to-solution analysis

User Performance Analysis

# User solver performance dashboard
apiVersion: dashboard.vantage.com/v1
kind: Dashboard
metadata:
  name: lsdyna-user-performance
spec:
  title: "LS-DYNA User Performance Analysis"
  refresh: 5m
  panels:
  - title: "Top LS-DYNA Users by Solver Hours"
    type: table
    targets:
    - query: |
        topk(10, 
          sum by (user) (
            lsdyna_solver_hours{server="lsdyna-server"}
          )
        )
    columns:
    - name: "User"
      field: "user"
    - name: "Total Solver Hours"
      field: "value"
    - name: "Preferred Solver"
      field: "primary_solver"
    - name: "Parallel Efficiency"
      field: "parallel_efficiency"
    - name: "Cost per Hour"
      field: "cost_per_hour"
      
  - title: "Solver Usage Distribution by User"
    type: stackedbarchart
    targets:
    - query: |
        lsdyna_solver_usage{server="lsdyna-server"} 
        by (user, solver)
    legend: "{{solver}}"
    
  - title: "Parallel Token Efficiency by User"
    type: scatter
    targets:
    - query: |
        lsdyna_parallel_efficiency{server="lsdyna-server"}
        vs
        lsdyna_token_count{server="lsdyna-server"}
    visualization:
      xAxis: "Token Count"
      yAxis: "Parallel Efficiency %"
      bubbleSize: "simulation_runtime"

Solver-Specific User Productivity

# LS-DYNA user productivity analysis script
#!/usr/bin/env python3
# /opt/lstc/bin/user-solver-analysis.py

import requests
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

class LSDynaUserAnalytics:
    def __init__(self, vantage_api_endpoint, api_key):
        self.api_endpoint = vantage_api_endpoint
        self.headers = {'Authorization': f'Bearer {api_key}'}
    
    def get_user_solver_data(self, server, days=30):
        """Fetch user solver data from Vantage API"""
        end_date = datetime.now()
        start_date = end_date - timedelta(days=days)
        
        params = {
            'server': server,
            'start_date': start_date.isoformat(),
            'end_date': end_date.isoformat(),
            'include_solver_details': True,
            'include_parallel_metrics': True
        }
        
        response = requests.get(
            f"{self.api_endpoint}/licenses/lsdyna/users", 
            headers=self.headers, 
            params=params
        )
        return response.json()
    
    def calculate_solver_efficiency_scores(self, solver_data):
        """Calculate solver-specific efficiency scores"""
        df = pd.DataFrame(solver_data['users'])
        
        # Calculate solver efficiency metrics
        df['explicit_efficiency'] = df['explicit_productive_time'] / df['explicit_total_time']
        df['implicit_efficiency'] = df['implicit_productive_time'] / df['implicit_total_time']
        df['parallel_scaling_score'] = df['achieved_speedup'] / df['theoretical_speedup']
        df['token_utilization'] = df['productive_tokens'] / df['allocated_tokens']
        
        # Composite efficiency score for each solver
        df['explicit_score'] = (
            (df['explicit_efficiency'] * 0.4) + 
            (df['parallel_scaling_score'] * 0.3) + 
            (df['token_utilization'] * 0.3)
        ) * 100
        
        df['implicit_score'] = (
            (df['implicit_efficiency'] * 0.5) + 
            (df['parallel_scaling_score'] * 0.3) + 
            (df['token_utilization'] * 0.2)
        ) * 100
        
        return df
    
    def analyze_solver_preferences(self, solver_data):
        """Analyze user solver preferences and usage patterns"""
        df = pd.DataFrame(solver_data['users'])
        
        # Calculate solver preference metrics
        df['primary_solver'] = df[['explicit_hours', 'implicit_hours', 'em_hours', 'thermal_hours']].idxmax(axis=1)
        df['solver_diversity'] = (df[['explicit_hours', 'implicit_hours', 'em_hours', 'thermal_hours']] > 0).sum(axis=1)
        df['solver_specialization'] = df[['explicit_hours', 'implicit_hours', 'em_hours', 'thermal_hours']].max(axis=1) / df[['explicit_hours', 'implicit_hours', 'em_hours', 'thermal_hours']].sum(axis=1)
        
        # Identify optimization opportunities
        df['optimization_potential'] = np.where(
            (df['parallel_scaling_score'] < 0.7) & (df['allocated_tokens'] > 8),
            'parallel_overallocation',
            np.where(
                (df['token_utilization'] < 0.5),
                'token_waste',
                np.where(
                    (df['solver_efficiency'] < 0.6),
                    'solver_mismatch',
                    'optimized'
                )
            )
        )
        
        return df
    
    def generate_user_recommendations(self, server):
        """Generate personalized recommendations for LS-DYNA users"""
        data = self.get_user_solver_data(server)
        efficiency_df = self.calculate_solver_efficiency_scores(data)
        preferences_df = self.analyze_solver_preferences(data)
        
        recommendations = []
        
        for _, user in preferences_df.iterrows():
            user_recommendations = {
                'user': user['username'],
                'recommendations': []
            }
            
            # Parallel efficiency recommendations
            if user['parallel_scaling_score'] < 0.7:
                user_recommendations['recommendations'].append({
                    'type': 'parallel_optimization',
                    'message': f"Consider reducing parallel tokens from {user['avg_tokens']} to {user['optimal_tokens']} for better efficiency",
                    'potential_savings': f"${user['potential_savings']:.2f}/month"
                })
            
            # Solver selection recommendations
            if user['solver_efficiency'] < 0.8 and user['solver_diversity'] > 2:
                user_recommendations['recommendations'].append({
                    'type': 'solver_specialization',
                    'message': f"Focus on {user['most_efficient_solver']} solver for better performance",
                    'efficiency_gain': f"{user['efficiency_improvement']:.1f}%"
                })
            
            # Token allocation recommendations
            if user['token_utilization'] < 0.6:
                user_recommendations['recommendations'].append({
                    'type': 'token_optimization',
                    'message': f"Reduce token allocation to improve cost efficiency",
                    'cost_savings': f"${user['token_savings']:.2f}/month"
                })
            
            recommendations.append(user_recommendations)
        
        return recommendations

# Usage example
if __name__ == "__main__":
    analytics = LSDynaUserAnalytics(
        "https://api.vantage.com/v1", 
        "your-api-key"
    )
    recommendations = analytics.generate_user_recommendations("lsdyna-server")
    
    for user_rec in recommendations:
        print(f"\nRecommendations for {user_rec['user']}:")
        for rec in user_rec['recommendations']:
            print(f"  - {rec['type']}: {rec['message']}")

Team and Department Analytics

Organizational Solver Distribution

Vantage provides comprehensive analytics for understanding LS-DYNA solver utilization across organizational structures:

Department-Level Solver Analytics

# Configure department tracking for LS-DYNA
vantage analytics departments configure \
  --server lsdyna-server \
  --map-users-to-departments /etc/vantage/user-department-mapping.yaml \
  --enable-solver-cost-attribution \
  --enable-parallel-budget-tracking \
  --track-solver-preferences-by-dept

# View department solver analytics
vantage dashboard departments --server lsdyna-server \
  --include-solver-breakdown \
  --include-parallel-efficiency \
  --include-cost-analysis

Department Analytics Features:

• Solver preference by department: Which departments use which LS-DYNA solvers most
• Parallel processing patterns: How different departments utilize SMP vs MPP tokens
• Cost center attribution: Automatic allocation of solver and token costs to departments
• Efficiency comparisons: Compare LS-DYNA utilization efficiency across departments
• Specialization analysis: Identify departments with specific solver expertise

Cross-Department Solver Collaboration

# Department solver analytics dashboard
apiVersion: dashboard.vantage.com/v1
kind: Dashboard
metadata:
  name: lsdyna-department-analytics
spec:
  title: "LS-DYNA Department Analytics"
  panels:
  - title: "Solver Usage by Department"
    type: stackedbarchart
    targets:
    - query: |
        sum by (department, solver) (
          lsdyna_solver_hours{server="lsdyna-server"}
        )
    legend: "{{solver}}"
    
  - title: "Department Parallel Efficiency"
    type: bargraph
    targets:
    - query: |
        avg by (department) (
          lsdyna_parallel_efficiency{server="lsdyna-server"}
        )
    thresholds:
    - value: 80
      color: green
    - value: 60
      color: yellow
    - value: 40
      color: red
    
  - title: "Solver Cost Distribution"
    type: piechart
    targets:
    - query: |
        sum by (department) (
          lsdyna_solver_cost{server="lsdyna-server"}
        )
    
  - title: "Department Solver Specialization"
    type: heatmap
    targets:
    - query: |
        lsdyna_solver_specialization_index{server="lsdyna-server"}
        by (department, solver)
    visualization:
      xAxis: "solver"
      yAxis: "department"
      colorScheme: "blue-white-red"

Engineering Team Optimization

# Configure team-specific solver optimization
vantage analytics teams configure \
  --server lsdyna-server \
  --track-team-solver-efficiency \
  --identify-knowledge-sharing-opportunities \
  --generate-team-optimization-recommendations

# View team collaboration opportunities
vantage analytics collaboration-opportunities \
  --server lsdyna-server \
  --solver-expertise-sharing \
  --parallel-optimization-training

Cluster-Based Analytics

License Distribution Across HPC Resources

Understand how LS-DYNA licenses and parallel tokens are utilized across different compute clusters:

Cluster Solver Performance Dashboard

# Configure cluster-based LS-DYNA tracking
vantage analytics clusters configure \
  --server lsdyna-server \
  --track-solver-locality \
  --monitor-parallel-scaling \
  --enable-hpc-integration \
  --optimize-token-distribution

# View cluster solver analytics
vantage dashboard clusters --server lsdyna-server \
  --include-solver-performance \
  --include-parallel-scaling \
  --include-queue-efficiency

Cluster Analytics Features:

• Solver performance by cluster: How different solvers perform on different hardware
• Parallel scaling efficiency: Token utilization effectiveness across cluster architectures
• Queue optimization: Solver-aware job scheduling across clusters
• Resource correlation: Relationship between compute resources and solver performance
• Geographic solver distribution: LS-DYNA usage across different data centers

Multi-Cluster Solver Optimization

# Cluster solver optimization dashboard
apiVersion: dashboard.vantage.com/v1
kind: Dashboard
metadata:
  name: lsdyna-cluster-optimization
spec:
  title: "LS-DYNA Multi-Cluster Optimization"
  panels:
  - title: "Solver Performance by Cluster"
    type: heatmap
    targets:
    - query: |
        lsdyna_solver_performance_score{server="lsdyna-server"}
        by (cluster, solver)
    visualization:
      xAxis: "solver"
      yAxis: "cluster"
      colorScheme: "red-yellow-green"
    
  - title: "Parallel Scaling Efficiency"
    type: timeseries
    targets:
    - query: |
        lsdyna_parallel_efficiency{server="lsdyna-server"}
        by (cluster, token_count)
    legend: "{{cluster}} - {{token_count}} tokens"
    
  - title: "Optimal Cluster Assignment"
    type: table
    targets:
    - query: |
        lsdyna_optimal_cluster_assignment{server="lsdyna-server"}
    columns:
    - name: "Solver"
    - name: "Problem Size"
    - name: "Recommended Cluster"
    - name: "Expected Efficiency"
    - name: "Cost Impact"
    
  - title: "Queue Wait Time by Solver"
    type: bargraph
    targets:
    - query: |
        avg by (cluster, solver) (
          lsdyna_queue_wait_time{server="lsdyna-server"}
        )
    unit: "minutes"

HPC Scheduler Integration Analytics

# Cluster LS-DYNA optimization script
#!/usr/bin/env python3
# /opt/lstc/bin/cluster-solver-optimization.py

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor

class LSDynaClusterOptimizer:
    def __init__(self, vantage_api):
        self.api = vantage_api
    
    def analyze_cluster_solver_performance(self, server, days=30):
        """Analyze solver performance across clusters"""
        data = self.api.get_cluster_solver_data(server, days)
        df = pd.DataFrame(data)
        
        # Calculate performance metrics per cluster-solver combination
        cluster_solver_metrics = df.groupby(['cluster', 'solver']).agg({
            'simulation_time': 'mean',
            'parallel_efficiency': 'mean',
            'cost_per_simulation': 'mean',
            'queue_wait_time': 'mean',
            'memory_usage': 'mean',
            'cpu_utilization': 'mean'
        }).reset_index()
        
        # Calculate performance scores
        cluster_solver_metrics['performance_score'] = (
            (1 / cluster_solver_metrics['simulation_time'].rank()) * 0.3 +
            cluster_solver_metrics['parallel_efficiency'].rank() * 0.3 +
            (1 / cluster_solver_metrics['cost_per_simulation'].rank()) * 0.2 +
            (1 / cluster_solver_metrics['queue_wait_time'].rank()) * 0.2
        )
        
        return cluster_solver_metrics
    
    def predict_optimal_allocation(self, cluster_metrics, job_requirements):
        """Predict optimal cluster allocation for LS-DYNA jobs"""
        # Prepare features for machine learning model
        features = ['solver_type', 'element_count', 'time_steps', 'parallel_tokens']
        target = 'performance_score'
        
        # Train model on historical data
        X = cluster_metrics[features]
        y = cluster_metrics[target]
        
        model = RandomForestRegressor(n_estimators=100, random_state=42)
        model.fit(X, y)
        
        # Predict optimal allocation for new jobs
        predictions = []
        for job in job_requirements:
            job_features = [job[feature] for feature in features]
            
            # Predict performance for each cluster
            cluster_predictions = {}
            for cluster in cluster_metrics['cluster'].unique():
                cluster_job = job_features + [cluster]
                predicted_score = model.predict([cluster_job])[0]
                cluster_predictions[cluster] = predicted_score
            
            # Recommend best cluster
            best_cluster = max(cluster_predictions, key=cluster_predictions.get)
            predictions.append({
                'job_id': job['job_id'],
                'recommended_cluster': best_cluster,
                'predicted_performance': cluster_predictions[best_cluster],
                'alternative_clusters': sorted(
                    cluster_predictions.items(), 
                    key=lambda x: x[1], 
                    reverse=True
                )[1:3]
            })
        
        return predictions
    
    def generate_cluster_optimization_report(self, server):
        """Generate comprehensive cluster optimization report"""
        cluster_metrics = self.analyze_cluster_solver_performance(server)
        
        # Identify optimization opportunities
        optimizations = []
        
        # Find underperforming cluster-solver combinations
        underperforming = cluster_metrics[
            cluster_metrics['performance_score'] < cluster_metrics['performance_score'].quantile(0.25)
        ]
        
        for _, row in underperforming.iterrows():
            optimizations.append({
                'type': 'performance_improvement',
                'cluster': row['cluster'],
                'solver': row['solver'],
                'issue': 'low_performance_score',
                'recommendation': 'Consider hardware upgrade or configuration tuning',
                'potential_improvement': f"{(cluster_metrics['performance_score'].max() - row['performance_score']) / row['performance_score'] * 100:.1f}%"
            })
        
        # Find cost optimization opportunities
        high_cost = cluster_metrics[
            cluster_metrics['cost_per_simulation'] > cluster_metrics['cost_per_simulation'].quantile(0.75)
        ]
        
        for _, row in high_cost.iterrows():
            cheaper_alternatives = cluster_metrics[
                (cluster_metrics['solver'] == row['solver']) & 
                (cluster_metrics['cost_per_simulation'] < row['cost_per_simulation'])
            ].sort_values('cost_per_simulation')
            
            if not cheaper_alternatives.empty:
                best_alternative = cheaper_alternatives.iloc[0]
                optimizations.append({
                    'type': 'cost_optimization',
                    'cluster': row['cluster'],
                    'solver': row['solver'],
                    'issue': 'high_cost',
                    'recommendation': f"Consider migrating to {best_alternative['cluster']}",
                    'potential_savings': f"${(row['cost_per_simulation'] - best_alternative['cost_per_simulation']):.2f} per simulation"
                })
        
        report = {
            'summary': {
                'total_clusters': cluster_metrics['cluster'].nunique(),
                'solvers_analyzed': cluster_metrics['solver'].nunique(),
                'avg_performance_score': cluster_metrics['performance_score'].mean(),
                'optimization_opportunities': len(optimizations)
            },
            'cluster_performance': cluster_metrics.to_dict('records'),
            'optimization_recommendations': optimizations
        }
        
        return report

Historical Trends and Predictive Analytics

Long-Term Solver Usage Analysis

# Configure historical LS-DYNA trend analysis
vantage analytics trends configure \
  --server lsdyna-server \
  --retention-period 2y \
  --enable-solver-seasonal-analysis \
  --enable-parallel-growth-prediction \
  --enable-capacity-planning \
  --track-solver-evolution

# Generate trend analysis
vantage analytics trends generate \
  --server lsdyna-server \
  --include-solver-adoption-patterns \
  --include-parallel-scaling-trends \
  --include-capacity-recommendations

Solver Evolution and Technology Adoption

# Solver evolution dashboard
apiVersion: dashboard.vantage.com/v1
kind: Dashboard
metadata:
  name: lsdyna-solver-evolution
spec:
  title: "LS-DYNA Solver Evolution and Adoption"
  panels:
  - title: "Solver Adoption Over Time"
    type: timeseries
    timeRange: 2y
    targets:
    - query: |
        rate(lsdyna_solver_usage_total{server="lsdyna-server"}[30d])
        by (solver)
    legend: "{{solver}}"
    
  - title: "Parallel Token Growth"
    type: timeseries
    targets:
    - query: |
        avg_over_time(
          lsdyna_parallel_tokens_used{server="lsdyna-server"}[7d]
        )
        by (token_type)
    legend: "{{token_type}} tokens"
    
  - title: "Simulation Complexity Trends"
    type: scatter
    targets:
    - query: |
        lsdyna_simulation_complexity{server="lsdyna-server"}
        vs
        lsdyna_simulation_duration{server="lsdyna-server"}
    visualization:
      xAxis: "Problem Complexity (elements)"
      yAxis: "Simulation Duration (hours)"
      bubbleSize: "parallel_tokens"
    
  - title: "Future Capacity Predictions"
    type: graph
    targets:
    - query: |
        predict_linear(
          lsdyna_peak_token_usage{server="lsdyna-server"}[8w], 12w
        )
    - query: |
        lsdyna_token_capacity{server="lsdyna-server"}
    legend:
    - "Predicted Peak Usage"
    - "Current Capacity"

Cost Analysis and Optimization

Solver-Specific Cost Attribution

# Configure LS-DYNA cost analysis
vantage analytics costs configure \
  --server lsdyna-server \
  --solver-license-costs /etc/vantage/lsdyna-license-costs.yaml \
  --parallel-token-costs /etc/vantage/lsdyna-token-costs.yaml \
  --enable-solver-roi-analysis \
  --enable-parallel-optimization-recommendations

# Generate cost analysis
vantage analytics costs analyze \
  --server lsdyna-server \
  --include-solver-utilization-costs \
  --include-parallel-waste-analysis \
  --include-optimization-savings

ROI and Efficiency Metrics by Solver

# LS-DYNA cost optimization dashboard
apiVersion: dashboard.vantage.com/v1
kind: Dashboard
metadata:
  name: lsdyna-cost-optimization
spec:
  title: "LS-DYNA Cost Analysis and Optimization"
  panels:
  - title: "Cost vs Performance by Solver"
    type: scatter
    targets:
    - query: |
        lsdyna_solver_annual_cost{server="lsdyna-server"} 
        vs 
        lsdyna_solver_performance_score{server="lsdyna-server"}
    visualization:
      xAxis: "Performance Score"
      yAxis: "Annual Cost $"
      bubbleSize: "usage_hours"
    
  - title: "Parallel Token ROI"
    type: bargraph
    targets:
    - query: |
        lsdyna_parallel_roi{server="lsdyna-server"}
        by (token_type, token_count)
    legend: "{{token_type}} - {{token_count}} tokens"
    
  - title: "Underutilized Solver Investment"
    type: stat
    targets:
    - query: |
        sum(
          lsdyna_solver_annual_cost{server="lsdyna-server"} 
          * (1 - lsdyna_solver_utilization_percent{server="lsdyna-server"} / 100)
        )
    unit: "currency"
    title: "Annual Underutilization Cost ($)"
    
  - title: "Cost per Simulation by Solver"
    type: table
    targets:
    - query: |
        lsdyna_cost_per_simulation{server="lsdyna-server"}
        by (solver, complexity_category)
    columns:
    - name: "Solver"
    - name: "Complexity"
    - name: "Avg Cost/Simulation"
    - name: "Parallel Efficiency"
    - name: "ROI Score"

Alerting and Performance Monitoring

LS-DYNA Specific Alerts

# Configure LS-DYNA specific alerting
vantage alerts configure lsdyna-server \
  --solver-utilization-threshold 85 \
  --parallel-efficiency-threshold 60 \
  --simulation-performance-degradation \
  --cost-budget-alerts \
  --queue-length-alerts

# Set up solver-specific notification channels
vantage alerts channels configure \
  --email simulation-team@company.com \
  --slack "#lsdyna-alerts" \
  --webhook https://alerts.company.com/lsdyna \
  --escalation-policy critical-simulations

Advanced LS-DYNA Alert Rules

# LS-DYNA intelligent alerting configuration
apiVersion: alerting.vantage.com/v1
kind: AlertRule
metadata:
  name: lsdyna-intelligent-alerts
spec:
  server: lsdyna-server
  rules:
  - name: "Solver Queue Backlog"
    condition: |
      lsdyna_queue_length > 10
    duration: 5m
    severity: warning
    message: "LS-DYNA {{$labels.solver}} queue has {{$value}} jobs waiting"
    
  - name: "Parallel Efficiency Drop"
    condition: |
      lsdyna_parallel_efficiency < 60
    duration: 10m
    severity: warning
    message: "User {{$labels.user}} parallel efficiency dropped to {{$value}}%"
    
  - name: "Token Waste Alert"
    condition: |
      lsdyna_unused_token_percent > 40
    duration: 15m
    severity: info
    message: "{{$labels.user}} has {{$value}}% unused tokens in simulation"
    
  - name: "Solver Performance Degradation"
    condition: |
      lsdyna_simulation_time > (lsdyna_baseline_time * 1.5)
    duration: 5m
    severity: warning
    message: "Simulation performance degraded: {{$value}}s vs baseline {{$labels.baseline}}s"
    
  - name: "License Cost Budget Exceeded"
    condition: |
      lsdyna_monthly_cost > lsdyna_monthly_budget
    duration: 1m
    severity: critical
    message: "LS-DYNA costs exceeded budget: ${{$value}} > ${{$labels.budget}}"

Integration with Simulation Workflows

CAE Workflow Integration

# LS-DYNA workflow optimization script
#!/usr/bin/env python3
# /opt/lstc/bin/workflow-optimization.py

import json
import yaml
from datetime import datetime

class LSDynaWorkflowOptimizer:
    def __init__(self, vantage_api):
        self.vantage = vantage_api
    
    def optimize_simulation_parameters(self, job_config):
        """Optimize LS-DYNA simulation parameters based on historical data"""
        # Get historical performance data for similar simulations
        historical_data = self.vantage.get_similar_simulations(
            element_count=job_config['element_count'],
            solver_type=job_config['solver'],
            tolerance=0.2
        )
        
        # Analyze optimal configurations
        optimal_config = self.analyze_optimal_parameters(historical_data)
        
        recommendations = {
            'solver_selection': optimal_config['best_solver'],
            'parallel_tokens': optimal_config['optimal_tokens'],
            'expected_runtime': optimal_config['estimated_time'],
            'cost_estimate': optimal_config['estimated_cost'],
            'efficiency_score': optimal_config['expected_efficiency']
        }
        
        return recommendations
    
    def generate_job_submission_script(self, job_config, recommendations):
        """Generate optimized job submission script"""
        import textwrap
        
        script_template = textwrap.dedent("""
            #!/bin/bash
            #SBATCH --job-name=lsdyna_{job_name}
            #SBATCH --nodes={nodes}
            #SBATCH --ntasks-per-node={tasks_per_node}
            #SBATCH --cpus-per-task={cpus_per_task}
            #SBATCH --time={walltime}
            #SBATCH --partition={partition}
            #SBATCH --licenses=lsdyna_{solver}:1,{token_type}_tokens:{token_count}

            # LS-DYNA optimized execution
            module load lsdyna/{version}

            # Set optimal environment variables
            export OMP_NUM_THREADS={omp_threads}
            export I_MPI_PIN_DOMAIN={pin_domain}

            # Execute LS-DYNA with optimized parameters
            {lsdyna_command}
            """).strip()
        
        script_params = {
            'job_name': job_config['name'],
            'nodes': recommendations['optimal_nodes'],
            'tasks_per_node': recommendations['tasks_per_node'],
            'cpus_per_task': recommendations['cpus_per_task'],
            'walltime': recommendations['estimated_walltime'],
            'partition': recommendations['optimal_partition'],
            'solver': recommendations['solver_selection'],
            'token_type': recommendations['token_type'],
            'token_count': recommendations['parallel_tokens'],
            'version': job_config['lsdyna_version'],
            'omp_threads': recommendations['omp_threads'],
            'pin_domain': recommendations['pin_domain'],
            'lsdyna_command': self.build_lsdyna_command(job_config, recommendations)
        }
        
        return script_template.format(**script_params)
    
    def monitor_simulation_progress(self, job_id):
        """Monitor and optimize running simulation"""
        job_status = self.vantage.get_job_status(job_id)
        
        monitoring_data = {
            'timestamp': datetime.now().isoformat(),
            'job_id': job_id,
            'status': job_status['status'],
            'runtime': job_status['runtime'],
            'parallel_efficiency': job_status['parallel_efficiency'],
            'memory_usage': job_status['memory_usage'],
            'license_utilization': job_status['license_utilization']
        }
        
        # Generate optimization recommendations for running job
        if monitoring_data['parallel_efficiency'] < 60:
            monitoring_data['recommendations'] = [
                'Consider reducing parallel token count for better efficiency',
                'Check for load imbalance in simulation model'
            ]
        
        return monitoring_data

Next Steps

• High Availability: Configure redundant LSTC servers
• Troubleshooting: Diagnose and resolve LS-DYNA issues
• LS-DYNA Introduction: Return to main LS-DYNA overview

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Analytics Best Practice: LS-DYNA's solver-specific licensing model provides unique opportunities for detailed analytics. Focus on parallel token efficiency and solver-specific performance metrics to maximize both computational and license ROI. Regular review of solver usage patterns helps identify opportunities for user training and workflow optimization.