Enterprise AI Analysis
Aurora Acceptance: A Collaborative Exascale Test Harness
The Aurora exascale system, deployed at Argonne Leadership Computing Facility (ALCF), underwent a rigorous acceptance testing process. This collaborative effort involved ALCF, Intel, and HPE, culminating in successful acceptance in December 2024. The testing, which mimicked real-world utilization, stressed the system and components, tracked regressions, and extended an open-source test harness. Key capabilities include collaborative test creation, support for diverse workloads, automated test execution, unique workplace creation, functional and performance validation, artifact retention, failure notification, and collaborative root cause analysis. The process leveraged tools like ReFrame, Jenkins, Slack, GitLab, Data Warehouse, Data Lake, and StabilityDB to manage complexity and ensure the system's readiness for scientific discovery.
Key Impact Metrics
Total Jobs Executed
Acceptance Test Duration
Hundreds
Test Configurations
Compute Nodes
Deep Analysis & Enterprise Applications
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System Architecture
Testing Framework
Collaboration & Analysis
Key Capabilities
Aurora is a complex leadership-class system with 166 HPE Cray EX4000 cabinets, 10,624 nodes. Each node has 2 Intel Xeon CPU Max Series processors with HBM2e and DDR5, and 6 Intel Data Center GPU Max Series (Ponte Vecchio) with HBM2e. The system interconnect is Slingshot 11 with a Dragonfly topology. Additionally, it includes 1,024 DAOS nodes with NVRAM and NVMe SSD, providing a total raw capacity of 250 PiB. Managed by HPE HPCM, nodes run SUSE Linux with HPE Cray Programming Environment and Intel oneAPI HPC Toolkit. This architecture underpins the rigorous demands of exascale computing.
The Aurora acceptance test harness extends the Polaris system's harness, which used ReFrame, Jenkins, and Slack. Enhancements were made to support Aurora’s scale and requirements, adding GitLab for version control, a Data Warehouse/Lake for data management, and StabilityDB for error tracking. The framework facilitates collaborative test creation, automated execution, and comprehensive failure analysis.
Close collaboration among ALCF, Intel, and HPE was crucial. GitLab's issue system tracks root cause analysis, integrating with Kafka for RAS events and other system metrics, forwarded to a central Data Warehouse. Intel-developed software monitors failed jobs and pulls BMC logs. The Data Lake, implemented with Delta Lake, stores and analyzes massive system log data, overcoming traditional database limitations. It includes hourly DNS zone transfers and a 'Location to Mask' for efficient bitwise filtering. This robust analysis framework enables efficient identification and resolution of systemic issues.
The harness provides collaborative test creation, support for multiple workflow/application patterns, hands-off automated test inception, unique workplace creation, optional compilation, automated job submission, functional/performance validation, artifact retention, failure notification, collaborative root cause analysis, and stakeholder-accessible dashboards. These capabilities ensure system stability, accuracy, and performance for scientific discovery.
10,624
Compute Nodes Tested
Acceptance Test Process Flow
Submit Test
→
Launch Test
→
Stage Test
→
Build/Compile?
→
Submit Job
→
Wait for Job
→
Check Correctness
→
Collect Performance
→
Pass?
→
Notify Failure
→
Create Issue
→
Stability DB
→
Root Cause
→
Data Warehouse
| Capability | Polaris (Legacy) | Aurora (Enhanced) |
|---|---|---|
| Test Creation |
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| Failure Analysis |
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| Hardware Targeting |
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| Software Stack Testing |
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Collaborative Acceptance Testing
Client: Argonne Leadership Computing Facility (ALCF)
Challenge: Deploying the Aurora exascale system with 10,624 nodes and complex software/hardware stack, requiring robust and collaborative acceptance testing.
Solution: Extended open-source test harness (ReFrame, Jenkins, Slack) with GitLab, Data Warehouse, Data Lake, and StabilityDB. Implemented collaborative test creation and automated root cause analysis workflows. Enabled hardware targeting and flexible software stack testing.
Results: Successful acceptance in December 2024. Executed 131,470 jobs across hundreds of configurations over 28 days. Enhanced transparency, reproducibility, and knowledge sharing. Accelerated troubleshooting and system validation.
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