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CAE Solver License and HPC Capacity Optimization

30 Chapters
View Course Samples Online
01
CAE solver licensing landscape
token-based, feature-based, and hybrid models; license servers (FlexNet, RLM, DSLS); key metrics (license utilization, queue time, denial rate).
CAElicensing
02
HPC capacity fundamentals for CAE
nodes, cores, memory, interconnect, storage; job schedulers (Slurm, PBS, LSF); capacity vs. utilization vs. throughput.
HPCcapacity
03
Mapping solver license features to HPC resources
how solvers consume tokens per core/GPU; affinity between license types and hardware profiles; common bottlenecks.
featuresHPC
04
License server architecture and high availability
redundant servers, triad configurations, failover, and impact on solver job startup.
architectureavailability
05
Monitoring and telemetry for license and HPC usage
collecting license events, job accounting, and resource metrics; tools (Grafana, Prometheus, ELK) and custom scripts.
Monitoringtelemetry
06
Baseline analysis
measuring current license utilization, queue wait times, and solver runtimes; identifying peak vs. off-peak patterns.
Baselineanalysis
07
License optimization techniques
token pooling, license borrowing, queuing policies, and dynamic license allocation.
optimizationtechniques
08
HPC capacity optimization techniques
node partitioning, core reservation, GPU sharing, and job preemption strategies.
HPCcapacity
09
Co-scheduling licenses and compute resources
preventing license starvation and idle cores; integration of license-aware scheduling in Slurm/PBS.
Co-schedulinglicenses
10
Queue design and priority policies
fair-share, backfill, and license-aware queues; balancing interactive and batch CAE workloads.
prioritypolicies
11
Solver-specific tuning for license efficiency
solver options that reduce token consumption (e.g., solver mode, parallel efficiency) without sacrificing accuracy.
Solver-specificefficiency
12
Workload characterization for CAE
profiling typical simulations (structural, CFD, thermal) to determine license and resource footprints.
Workloadcharacterization
13
Capacity planning models
forecasting license and HPC demand using historical data; scenario analysis for new projects or solver versions.
Capacityplanning
14
License cost modeling and chargeback
allocating license costs to projects; optimizing procurement vs. cloud bursting.
modelingchargeback
15
Cloud and hybrid HPC for CAE
license portability, cloud license servers, and dynamic capacity extension; cost and performance trade-offs.
HPCCAE
16
Containerization and CAE solvers
Docker/Singularity for solver isolation; license implications and HPC scheduler integration.
ContainerizationCAE
17
GPU acceleration for CAE solvers
license models for GPU-based solvers; optimizing GPU utilization and token consumption.
GPUacceleration
18
License server performance tuning
reducing checkout latency, handling high concurrency, and optimizing server resources.
performancereducing
19
Automation and orchestration
scripts and APIs for license monitoring, auto-scaling, and job submission; integration with CI/CD for CAE.
Automationorchestration
20
Case study
optimizing a structural analysis workflow: from license checkout to solver completion; reducing queue time and token waste.
optimizingstructural
21
Case study
CFD workload optimization: balancing license tokens, core counts, and memory bandwidth; using profiling to guide changes.
CFDworkload
22
Case study
multi-solver environment: managing mixed license types (e.g., Abaqus, ANSYS, Star-CCM+) on shared HPC resources.
multi-solverenvironment
23
Troubleshooting license and capacity issues
common errors (denied licenses, hung jobs), diagnostic steps, and remediation.
Troubleshootingcapacity
24
Policy and governance
license usage policies, priority rules, and stakeholder communication; ensuring compliance and fairness.
governancepolicies
25
Benchmarking and validation
designing experiments to measure optimization impact; key performance indicators (KPIs) for license and HPC efficiency.
Benchmarkingvalidation
26
Advanced scheduling
license-aware backfilling, reservation-based scheduling, and integration with workload managers (e.g., Altair PBS Works).
Advancedscheduling
27
Energy efficiency and sustainability
reducing power consumption through optimized scheduling and resource allocation; metrics and trade-offs.
efficiencysustainability
28
Future trends
AI-driven optimization, license models for AI-augmented solvers, and evolving HPC architectures (exascale, quantum).
AI-drivenoptimization
29
Building a continuous optimization framework
monitoring, feedback loops, and iterative improvement; roles and responsibilities.
Buildingcontinuous
30
Capstone project
design and implement an optimization plan for a realistic CAE environment; present results and recommendations.
Capstoneimplement