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Yuqi Intelligent Enterprise IT infrastructure and systems integration

ENTERPRISE HIGH-PERFORMANCE COMPUTING

High-Performance Computing (HPC)

Organize an expandable, schedulable and verifiable high-performance computing environment around CPU/GPU nodes, high-speed networking, shared storage and job scheduling.

YUQI INTELLIGENT · enterprise IT infrastructure and systems integration TEL 139 1891 4454

CPU, GPU, networking and storage together determine HPC cluster capability

01 Why planning matters

The scale of parallel work
determines how the computing environment is organized.

Building HPC is not simply adding servers. Nodes and resources are planned around parallel workloads, data scale, compute type and runtime.

Compute, data and interconnect paths affect one another; adding one resource type alone often does not improve overall job efficiency.

  • 01
    Centralized compute scheduling

    Organize distributed CPU, GPU and memory into an allocatable compute pool.

  • 02
    Clear jobs and priorities

    Allocate compute resources by job demand, queue and business priority.

  • 03
    High-speed data paths

    Match compute nodes, shared storage and network paths to actual data throughput.

  • 04
    Verifiable operating results

    Keep task, resource, version, failure and result records for continuous improvement.

02 PLATFORM ARCHITECTURE

Compute, networking and storage
must be designed together around the data path.

Data movement, node interconnects and shared storage directly affect job efficiency, so the architecture starts with the real data path.

HPC-ENV · system view 01/01

Layered HPC platform architecture: workloads, job scheduling, compute nodes, high-speed fabric, shared storage and operations monitoring From top to bottom, the five layers are workloads, job scheduling, compute nodes, high-speed fabric and shared storage; operations monitoring writes status back on the right. L1 Workloads WORKLOADS Compute-intensive · data-intensive · network-intensive L2 Job scheduling SCHEDULER Queues · priority · quotas L3 Compute nodes COMPUTE NODES CPU · GPU · memory L4 High-speed fabric FABRIC Redundancy · bandwidth boundary L5 Shared storage STORAGE Capacity · throughput · access pattern Compute-intensive COMPUTE INTENSIVE Data-intensive DATA INTENSIVE Network-intensive NETWORK INTENSIVE Job scheduling CPU CPU node CPU CPU node GPU GPU node Cooling and power GPU GPU node Cooling and power High-speed fabric · redundancy and bandwidth boundary Shared storage · parallel files Capacity CAPACITY Throughput THROUGHPUT Access pattern ACCESS L6 Operations monitoring TELEMETRY Queue Utilization Network Capacity
A
Compute · interconnect

Waiting for node-to-node communication directly affects parallel job efficiency.

B
Interconnect · storage

Data throughput and access patterns shape storage-path design.

C
Scheduling · monitoring

Queue and resource status feed back into continuous policy adjustment.

The architecture follows the real data path and does not assume a fixed equipment scale.

High-performance computing, data and scheduling architecture

03 JOB SCHEDULING

From benchmarking to job scheduling
validate cluster capability step by step.

Implementation validates hardware, systems, the scheduler, software environment and usage workflow together; powering on equipment is not enough.

  1. 01
    Requirements and baseline

    Confirm task type, dataset, parallel method and performance measures.

  2. 02
    Cluster deployment

    Complete node, network, storage, system and scheduler configuration.

  3. 03
    Job validation

    Use representative jobs to validate queues, resources, failure and recovery.

  4. 04
    Operations handover

    Hand over nodes, queues, software environment, monitoring and maintenance records.

HPC job scheduling and resource queue view
Schedule compute by job priority, resource demand and queue

04 COMPUTE DENSITY

Compute density and interconnect method
together with cooling and power determine node capability.

Node specifications should follow compute density, interconnect method and the expansion plan; GPU nodes, high-speed fabric and cooling conditions together determine high-density capability.

  • 01
    Task type

    Separate CPU, GPU, memory, parallel and batch tasks.

  • 02
    Node specifications

    Select nodes around compute density, interconnect method and the expansion plan.

  • 03
    Resource pool

    Bring nodes, quotas, queues and usage permissions into unified scheduling.

05 OPERATIONS

Operations focus on resource utilization,
jobs and failures.

Stable cluster operations require continued attention to resource use, queued tasks, software versions, storage capacity and node state.

  • 01
    Resources and queues

    Adjust quotas, queues and resource allocation as tasks change.

  • 02
    Software environment

    Manage drivers, runtimes, images and application-version baselines.

  • 03
    Failure and recovery

    Record the causes of node, network, storage and job failures.

06 DELIVERY

One verifiable delivery path.

  1. 01 Site inventory

    Inventory the current environment and build conditions, then confirm the implementation boundary.

  2. 02 Architecture and configuration design

    Define node, network and storage options around task characteristics.

  3. 03 Installation and integration testing

    Complete equipment installation and system, scheduler and network integration testing.

  4. 04 Scenario and failure testing

    Use representative jobs to validate performance, failure and recovery behavior.

  5. 05 Documentation and operations handover

    Hand over topology, configuration, monitoring and contact records.

START A CONVERSATION

Start with task characteristics
to plan your high-performance computing environment.

supports@yuqi-sh.com Contact information is for project communication only