Managing CAE Simulation Workloads in Cluster Environments
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1 Managing CAE Simulation Workloads in Cluster Environments Michael Humphrey V.P. Enterprise Computing Altair Engineering June 2003 Copyright 2003 Altair Engineering, Inc. All rights reserved. All trademarks are the property of their respective owners.
2 Agenda Automotive business initiatives Trends in virtual product development Cluster adoption Workload management Case studies Vision for distributed computing
3 Automotive Business Initiatives Reduce time-to-market Increase operating efficiency and reduce costs Specialty and niche vehicle development Off-shore manufacturing and engineering Six-Sigma quality Rapid and Robust Product Development
4 The Batch CAE Build Paradigm PDM CAD Initial Concept v1 v2 v3 Design Progression Revision History v4 Final Design vn Engineering Framework Programmable Distributed Automated Optimizing Repeatable Open NVH Meshing+Assembly Struct Durab Resource Manager Post & Correlation Test Impact Manufact Optimization Physical Prototypes
5 Trends in Virtual Product Development CAD, CAE and PDM toolset standardization CAE process automation Batch CAE processing Robust design Structural optimization (Stochastic, DOE, etc.) Engineering data management Manufacturing process simulation Increasing Compute Workload
6 IT Challenges Hardware and software cost containment Pooling and providing access to global engineering resources Heterogeneous and distributed compute networks End-user support Staff efficiency Cluster Adoption Workload Management
7 Typical Automotive CAE Simulation Workload CPU Seconds (millions) Impact Structural CFD
8 Compute Intensive Applications Impact Simulation Topology Optimization Manufacturing Process Simulation
9 CPU Efficiency Scalability of Automotive CAE Applications 100% CFD Structural Impact 0% CPUs Utilized by single Job
10 Cluster Adoption Desktop Cluster Total CPU Power Parallel Risc Linux Cluster Vector
11 Cluster Adoption Typical Automobile OEM Hardware Snapshot May 2003 Impact Workload CPUs Parallel RISC CPUs Linux Cluster CPUs Desktop Clusters Structural Workload CPUs Vector Supercomputers CPUs Parallel RISC CPUs Desktop Clusters CFD Workload CPUs Parallel RISC CPUs Linux Cluster
12 Defining Workload Management Middleware technology between compute intensive applications and networked hardware operating systems: Aggregates all computing resources (hardware and software assets) into a single virtual pool Intelligently schedules computation jobs to enforce business policies for resource sharing Provides user interface independent of operating system environment
13 Demand for Workload Management Complex computing infrastructures Distributed and heterogeneous environments Increased number of commodity CPUs Utilization of desktop clusters for production workload Distributed solver applications Movement towards MPP Solvers Central License Servers (CLS) facilitate sharing Increased CAE workload Model sizes and complexities Project simulations Automation leads and batch processing requirements
14 Defining Grid Computing Technology that extends workload management across administrative and organizational domains: Globus toolkit application provides workload secure user authentication Peer-to-Peer workload management balances workload across various geographic sites
15 Workload Manager Functional Components User interface Commands Execution Nodes Jobs Batch Server Job Execution Manager Scheduling Scheduler Resource Monitor Site Specific Policies for Sharing
16 Case Study: Automotive OEM Business Goals Technology Issues Reduce computing costs Increase computing resources Reduce job turnaround time Enable parametric analyses Hardware transition MPP solver adoption Administration of desktop clusters
17 Automotive OEM Data-Center Environment Failsafe Meta-Scheduler Head Node Data Staging Point SAN Disk Vector Systems Parallel RISC Systems Linux Clusters (Alpha) Linux Clusters (Intel)
18 Desktop Clusters at Automotive OEM Interactive UNIX workstations Augments data-center systems Significant increase in workstation usage Enables parametric analyses Head Node Storage Node Network Switch Network Switch Network Switch Network Switch
19 Desktop Cluster Utilization Workstation Cluster CPU Usage CPU seconds (millions) Jan Feb Mar Apr May June July Aug Sept Month (2002)
20 Case Study: Automotive Tier-1 Supplier Business Goals Technology Issues Implement HPC environment from scratch Transition from OEM data-center environment Continuity of services Totally new environment New hardware technology Software availability
21 Automotive Tier-1 Supplier HPC Environment Fault Tolerant Head Node Head Node NAS Disk Cascaded Switched Network Network Switch Network Switch Network Switch Network Switch Network Switch Cluster of Itanium2 nodes running HP/UX
22 Case Study: DaimlerChrysler in USA Business Goals Technology Issues Increase computing resources Contain computing costs Reduce job turnaround time Geographically separate sites Heterogeneous hardware Software availability Mix of interactive and batch processing
23 Functional Diagram of each site Workload Manager NFS File Server Interactive CPUs CPU #1 CPU #2 CPU #3 CPUs 1A & 1B CPUs 2A & 2B CPUs 3A & 3B Batch CPUs CPU #62 CPU #63 CPU #64 CPUs 30A & 30B CPUs 31A & 31B CPUs 32A & 32B 64 CPU Parallel RISC 64 CPU Linux Cluster
24 Cross-Site Integration at DaimlerChrysler Wide Area Network Parallel RISC System Peer to Peer Parallel RISC System Linux Cluster Linux Cluster Cross Mounted File System
25 Case Study: Altair Engineering Business Goals Technology Issues Reduce computing costs Reduce job turnaround time Increase productivity Heterogeneous environment Distributed workforce Trend towards optimization
26 Altair s Troy HPC Environment Workload Manager WEB Based Job Submission Active 1 CPU Linux System Standby 1 CPU Linux System Data Staging Point Windows Desktop Cluster 16 CPU UNIX SMP System 12 CPU UNIX SMP System 64 CPU Linux Cluster NAS Disk
27 LS-Dyna Benchmark on LINUX Cluster (dual CPU nodes) Wall Clock Time Total CPU Time 100% 5 78% Time (Hours) 67% 60% 2 CPU 4 CPU 6 CPU 8 CPU
28 Altair moved LS-Dyna work from UNIX to Linux Wall Clock Time (Hours) HW Cost per job (USD) $44 $5 UNIX 2 CPUs Linux 6 CPUs UNIX 2 CPUs Linux 6 CPUs Performance = 3X faster Hardware =10X less expensive
29 Future of Distributed Computing Short Term Linux clusters dominate data centers Desktop clusters augment data center resources Workload management becomes essential Grid computing across firewalls Long Term Utility computing models Licensing models
30 Questions?
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