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Benefits

  • Provides a single model for compute + network + memory + storage + power + thermal
  • Enables very early throughput and latency prediction for full AI/HPC workflows
  • Eliminates uncertainty in load balancing and resource allocation for heterogeneous compute
  • Reveals network bottlenecks, PCIe/CXL saturation, accelerator backpressure and DMA delays
  • Prevents power spikes and thermal violation failures long before hardware exists
  • Accelerates deployment planning for edge and data-center computing
  • Supports cost-vs-performance trade-offs through rapid architecture iteration

The HPC, Edge Systems, Edge AI and Data Center library in VisualSim Architect provides a comprehensive modeling framework for high-performance computing clusters, edge-AI platforms, edge servers and hyperscale data-center systems. It covers compute, network, memory, storage, power and thermal domains in a single executable architecture, enabling early evaluation of latency, throughput, power, thermal behavior, workload mapping and system scaling.

The library represents heterogeneous compute platforms consisting of traffic models, workloads, task graphs for software/DNN/AI/ML application definition, CPUs, GPUs, NPUs, TPUs, accelerators and domain-specific processors interconnected through PCIe, CXL, Ethernet, NVLink, NVSwitch and custom fabrics. It is designed for both edge-constrained deployments and large-scale data-center deployments with pods, racks and chassis.

Overview

The library contains configurable components to represent:

  • Compute Subsystems — multi-core CPUs, GPUs, AI accelerators (NPU/TPU), vector engines, DSPs and cache/memory hierarchies
  • Interconnect and Interfaces — PCIe, CXL, Ethernet (40G–800G), NVLink and NVSwitch fabrics
  • Network Scheduling and Latency Models — congestion, arbitration, QoS and load-balancing policies
  • Memory and DMA — HBM/DDR/LPDDR, scratchpad memory, DMA engines and buffer models
  • Storage Subsystems — NVMe, SSD, distributed storage and metadata traffic
  • System-Level Infrastructure — racks, pods, chassis and multi-rack clusters
  • Power and Thermal Architecture — rail-level power behavior, transient surges, cooling and temperature calculation
  • RTOS and Task-Graph Mapping — mapping of software/dataflow pipelines onto heterogeneous compute resources

The result is a full executable model that predicts end-to-end performance for AI workloads, HPC pipelines, edge inference, cloud compute, content delivery, communication systems and digital twinning.

Key Parameters

  • Compute_Configuration — number and type of CPUs, GPUs, NPUs, TPUs and accelerators
  • Interconnect_Topology — PCIe lanes, CXL configs, NVLink/NVSwitch maps, Ethernet bandwidth
  • Task_Graph_Mapping — workload distribution across heterogeneous compute nodes
  • Network_Scheduler — arbitration rules, congestion controls and per-flow QoS
  • Memory/Storage_Profile — bandwidth, queue depth, latency and DMA burst size
  • Power_Budget — per-node and per-rack consumption limits with allowable surge
  • Cooling_Model — cooling efficiency and thermal time constant per rack/chassis
  • Cluster_Scaling — rack/pod replication count and inter-rack bandwidth
  • Traffic_Pattern — inference, batch training, mixed-workload, streaming, microservice, VoIP, radio, CDN etc.

Analysis and Reports

  • Latency and throughput of full HPC/Edge/AI workloads
  • Accelerator sag/backpressure due to PCIe/CXL or NVLink saturation
  • End-to-end inference/training performance across racks and pods
  • Memory/Storage bottleneck analysis vs. compute saturation curve
  • Network congestion, queue buildup and packet drop statistics
  • Power spikes and rail-level excursion during workload oscillation
  • Temperature rise and cooling sufficiency per rack/chassis
  • Performance/cost curves comparing hardware configurations

Applications

  • Edge AI inference and edge server modeling
  • High-Performance Computing architecture and workload sizing
  • Autonomous software platform deployment on edge clusters
  • Telecom core and ORAN compute modeling
  • GPU/accelerator farm sizing for AI training and generative models
  • Large-scale data-center, pod, rack and chassis-level power/thermal planning
  • Cloud, edge and embedded compute platform co-design

Integrations

  • PCIe, CXL, Ethernet, NVLink and NVSwitch interconnect libraries
  • Memory / Storage / Cache / HBM / DDR components
  • Traffic and Workload Modeling, including AI Tensor/DNN workloads
  • Scheduling / RTOS and Task Graph libraries for workload orchestration
  • Power and Thermal libraries for heat, cooling and rail-level power consumption
  • Failure, Functional Safety and Cybersecurity libraries for operational resilience studies
  • Communication System and CSP models for high-bandwidth data pipelines

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