GPU / HPC
Choose resources around the workload, then verify compute, network, storage and scheduling. Not an on-demand GPU platform.
Build a scalable and verifiable enterprise HPC platform around GPU/CPU nodes, high-speed interconnects, shared storage, job scheduling, software environments, cooling and power.
Start with workloads, dependencies, access boundaries and operating requirements, then decide the platform shape.
GPU/CPU nodes only become an effective computing platform when high-speed interconnects, shared storage, job queues, software environments and operating records are designed together.
Organize CPU, GPU and memory into schedulable resource pools.
Allocate resources by job requirements, queues and business priority.
Match compute nodes, shared storage and network paths to actual data throughput.
Keep job, resource, version, fault and result records for continuous optimization.
Connect the high-performance computing architecture layers
The platform is reliable only when compute, storage, network, identity and operations are designed as one delivery boundary.
Lines show dependency paths, not live monitoring data.
Use a staged path so compatibility, performance, access and rollback conditions are tested before wider adoption.
Inventory workloads, versions, dependencies, users and constraints.
Validate representative workloads, policies, performance and user access.
Expand by business wave with change windows and a support path.
Close with test results, configuration records and operating ownership.
The handover baseline should make future expansion, troubleshooting and change decisions easier to trace.
Track utilization, headroom, latency and growth against the baseline.
Keep versions, permissions, configuration and approval records current.
Use monitoring, logs, runbooks and validation records to support recovery.
Confirm workloads, dependencies, capacity, network paths, identities, support ownership, maintenance windows and rollback conditions.
A focused pilot is recommended when workloads, users or compatibility conditions differ. It makes experience and operating assumptions testable.
Reserve capacity, interfaces, network paths, operating space and documentation standards during the initial design.
Share the current workload, data path, capacity concern or scheduling issue so the practical scope can be reviewed.
