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Hybrid Cloud HPC for Automotive Crash Simulation

30 Chapters
View Course Samples Online
01
Course overview
scope, hybrid cloud HPC architecture, and automotive crash simulation workflow.
HPCarchitecture
02
Crash simulation fundamentals
explicit finite element analysis, LS-DYNA and Radioss solvers, and typical model sizes.
simulationexplicit
03
HPC infrastructure for crash simulation
on-premise clusters, node types, interconnects, and storage systems.
HPCinfrastructure
04
Cloud HPC service models
IaaS, bare metal, and HPC-as-a-Service for CAE workloads.
HPCIaaS
05
Hybrid cloud reference architecture
on-premise head nodes, cloud burst workers, and secure network connectivity.
referencearchitecture
06
Workload characterization
CPU, memory, I/O, and MPI communication patterns in crash simulations.
Workloadcharacterization
07
Job scheduling and orchestration
Slurm, PBS Pro, and cloud-native batch schedulers across hybrid environments.
schedulingorchestration
08
Data management strategies
shared file systems, object storage, and data transfer for large CAE datasets.
managementstrategies
09
Licensing considerations
floating licenses, license servers, and cloud licensing models for LS-DYNA and Radioss.
Licensingconsiderations
10
Security and compliance
data encryption, VPN, VPC, and industry standards for automotive data.
Securitycompliance
11
Cost modeling and optimization
on-demand vs. spot instances, reserved capacity, and total cost of ownership.
modelingoptimization
12
Performance benchmarking
scaling tests, MPI tuning, and solver-specific benchmarks on hybrid cloud.
Performancebenchmarking
13
Cloud instance selection
compute-optimized, memory-optimized, and GPU instances for crash simulation.
instanceselection
14
Network fabric and latency
InfiniBand, RoCE, and Ethernet considerations for distributed crash jobs.
InfiniBandRoCE
15
Storage performance tuning
parallel file systems, NVMe, and caching for checkpoint and restart.
performanceparallel
16
Containerization and reproducibility
Singularity, Docker, and environment modules for CAE solvers.
Containerizationreproducibility
17
Workflow automation
job submission scripts, pre-processing, and post-processing pipelines.
Workflowautomation
18
Hybrid cloud burst strategies
policy-based bursting, queue thresholds, and cloud resource provisioning.
strategiespolicy-based
19
Monitoring and observability
job metrics, node health, and cloud resource utilization dashboards.
Monitoringobservability
20
Fault tolerance and resilience
checkpointing, job restart, and handling cloud instance preemption.
toleranceresilience
21
Case study
full vehicle frontal crash simulation on hybrid cloud with LS-DYNA.
simulationLS-DYNA
22
Case study
side impact simulation using Radioss on a cloud-burst HPC cluster.
simulationcloud-burst
23
Case study
pedestrian protection simulation with hybrid cloud and optimization loops.
pedestrianprotection
24
Case study
battery pack crash simulation for electric vehicles on hybrid cloud.
simulationelectric
25
Performance tuning
solver options, contact algorithms, and time step control for cloud HPC.
Performancealgorithms
26
Multi-physics coupling
fluid-structure interaction and thermal effects in crash simulation.
Multi-physicscoupling
27
Machine learning for crash simulation
surrogate models and anomaly detection in hybrid cloud.
simulationsurrogate
28
Cloud-native CAE platforms
commercial and open-source solutions for hybrid cloud HPC.
Cloud-nativeCAE
29
Future trends
exascale computing, AI-driven simulation, and edge-to-cloud continuum.
exascalecomputing
30
Capstone project
design and execute a hybrid cloud HPC workflow for a full vehicle crash simulation.
CapstoneHPC