Tag: Network

Data Center Shift with AI

Posted on by lechuck park

Data Center Shift with AI

This diagram illustrates how data centers are transforming as they enter the AI era.

๐Ÿ“… Timeline of Technological Evolution

The top section shows major technology revolutions and their timelines:

  • Internet ’95 (Internet era)
  • Mobile ’07 (Mobile era)
  • Cloud ’10 (Cloud era)
  • Blockchain
  • AI(LLM) ’22 (Large Language Model-based AI era)

๐Ÿข Traditional Data Center Components

Conventional data centers consisted of the following core components:

  • Software
  • Server
  • Network
  • Power
  • Cooling

These were designed as relatively independent layers.

๐Ÿš€ New Requirements in the AI Era

With the introduction of AI (especially LLMs), data centers require specialized infrastructure:

  1. LLM Model – Operating large language models
  2. GPU – High-performance graphics processing units (essential for AI computations)
  3. High B/W – High-bandwidth networks (for processing large volumes of data)
  4. SMR/HVDC – Switched-Mode Rectifier/High-Voltage Direct Current power systems
  5. Liquid/CDU – Liquid cooling/Cooling Distribution Units (for cooling high-heat GPUs)

๐Ÿ”— Key Characteristic of AI Data Centers: Integrated Design

The circular connection in the center of the diagram represents the most critical feature of AI data centers:

Tight Interdependency between SW/Computing/Network โ†” Power/Cooling

Unlike traditional data centers, in AI data centers:

  • GPU-based computing consumes enormous power and generates significant heat
  • High B/W networks consume additional power during massive data transfers between GPUs
  • Power systems (SMR/HVDC) must stably supply high power density
  • Liquid cooling (Liquid/CDU) must handle high-density GPU heat in real-time

These elements must be closely integrated in design, and optimizing just one element cannot guarantee overall system performance.

๐Ÿ’ก Key Message

AI workloads require moving beyond the traditional layer-by-layer independent design approach of conventional data centers, demanding that computing-network-power-cooling be designed as one integrated system. This demonstrates that a holistic approach is essential when building AI data centers.

๐Ÿ“ Summary

AI data centers fundamentally differ from traditional data centers through the tight integration of computing, networking, power, and cooling systems. GPU-based AI workloads create unprecedented power density and heat generation, requiring liquid cooling and HVDC power systems. Success in AI infrastructure demands holistic design where all components are co-optimized rather than independently engineered.

#AIDataCenter #DataCenterEvolution #GPUInfrastructure #LiquidCooling #AIComputing #LLM #DataCenterDesign #HighPerformanceComputing #AIInfrastructure #HVDC #HolisticDesign #CloudComputing #DataCenterCooling #AIWorkloads #FutureOfDataCenters

With Claude

‘tightly fused’

Posted on by lechuck park

This illustration visualizes the evolution of data centers, contrasting the traditionally separated components with the modern AI data center where software, compute, network, and crucially, power and cooling systems are ‘tightly fused’ together. It emphasizes how power and advanced cooling are organically intertwined with GPU and memory, directly impacting AI performance and highlighting their inseparable role in meeting the demands of high-performance AI. This tight integration symbolizes a pivotal shift for the modern AI era.

Data Center ?

Posted on by lechuck park

This infographic compares the evolution from servers to data centers, showing the progression of IT infrastructure complexity and operational requirements.

Left – Server

  • Shows individual hardware components: CPU, motherboard, power supply, cooling fans
  • Labeled “No Human Operation,” indicating basic automated functionality

Center – Modular DC

  • Represented by red cubes showing modular architecture
  • Emphasizes “More Bigger” scale and “modular” design
  • Represents an intermediate stage between single servers and full data centers

Right – Data Center

  • Displays multiple server racks and various infrastructure components (networking, power, cooling systems)
  • Marked as “Human & System Operation,” suggesting more complex management requirements

Additional Perspective on Automation Evolution:

While the image shows data centers requiring human intervention, the actual industry trend points toward increasing automation:

  1. Advanced Automation: Large-scale data centers increasingly use AI-driven management systems, automated cooling controls, and predictive maintenance to minimize human intervention.
  2. Lights-Out Operations Goal: Hyperscale data centers from companies like Google, Amazon, and Microsoft ultimately aim for complete automated operations with minimal human presence.
  3. Paradoxical Development: As scale increases, complexity initially requires more human involvement, but advanced automation eventually enables a return toward unmanned operations.

Summary: This diagram illustrates the current transition from simple automated servers to complex data centers requiring human oversight, but the ultimate industry goal is achieving fully automated “lights-out” data center operations. The evolution shows increasing complexity followed by sophisticated automation that eventually reduces the need for human intervention.

With Claude

HOPE OF THE NEXT

Posted on by lechuck park

Hope to jump

This image visualizes humanity’s endless desire for ‘difference’ as the creative force behind ‘newness.’ The organic human brain fuses with the logical AI circuitry, and from their core, a burst of light emerges. This light symbolizes not just the expansion of knowledge, but the very moment of creation, transforming into unknown worlds and novel concepts.

network issue in a GPU workload

Posted on by lechuck park

This diagram illustrates network bottleneck issues in large-scale AI/ML systems.

Key Components:

Left side:

  • Big Data and AI Model/Workload connected to the system via network

Center:

  • Large-scale GPU cluster (multiple GPUs arranged in a grid pattern)
  • Each GPU is interconnected for distributed processing

Right side:

  • Power supply and cooling systems

Core Problem:

The network interface specifications shown at the bottom reveal bandwidth mismatches:

  • inter GPU NVLink: 600GB/s
  • inter Server Infiniband: 400Gbps
  • CPU/RAM/DISK PCIe/NVLink: (relatively lower bandwidth)

“One Issue” – System-wide Propagation:

A network bottleneck or failure at a specific point (marked with red circle) “spreads throughout the entire system” as indicated by the yellow arrows.

This diagram warns that in large-scale AI training, a single network bottleneck can have catastrophic effects on overall system performance. It visualizes how bandwidth imbalances at various levels – GPU-to-GPU communication, server-to-server communication, and storage access – can compromise the efficiency of the entire system. The cascading effect demonstrates how network issues can quickly propagate and impact the performance of distributed AI workloads across the infrastructure.

with Claude

Components for AI Work

Posted on by lechuck park

This diagram visualizes the core concept that all components must be organically connected and work together to successfully operate AI workloads.

Importance of Organic Interconnections

Continuity of Data Flow

  • The data pipeline from Big Data โ†’ AI Model โ†’ AI Workload must operate seamlessly
  • Bottlenecks at any stage directly impact overall system performance

Cooperative Computing Resource Operations

  • GPU/CPU computational power must be balanced with HBM memory bandwidth
  • SSD I/O performance must harmonize with memory-processor data transfer speeds
  • Performance degradation in one component limits the efficiency of the entire system

Integrated Software Control Management

  • Load balancing, integration, and synchronization coordinate optimal hardware resource utilization
  • Real-time optimization of workload distribution and resource allocation

Infrastructure-based Stability Assurance

  • Stable power supply ensures continuous operation of all computing resources
  • Cooling systems prevent performance degradation through thermal management of high-performance hardware
  • Facility control maintains consistency of the overall operating environment

Key Insight

In AI systems, the weakest link determines overall performance. For example, no matter how powerful the GPU, if memory bandwidth is insufficient or cooling is inadequate, the entire system cannot achieve its full potential. Therefore, balanced design and integrated management of all components is crucial for AI workload success.

The diagram emphasizes that AI infrastructure is not just about having powerful individual components, but about creating a holistically optimized ecosystem where every element supports and enhances the others.

With Claude

Silicon Photonics

Posted on by lechuck park

This diagram compares PCIe (Electrical Copper Circuit) and Silicon Photonics (Optical Signal) technologies.

PCIe (Left, Yellow Boxes)

  • Signal Transmission: Uses electrons (copper traces)
  • Speed: Gen5 512Gbps (x16), Gen6 ~1Tbps expected
  • Latency: ฮผs~ns level delay due to resistance
  • Power Consumption: High (e.g., Gen5 x16 ~20W), increased cooling costs due to heat generation
  • Pros/Cons: Mature standard with low cost, but clear bandwidth/distance limitations

Silicon Photonics (Right, Purple Boxes)

  • Signal Transmission: Uses photons (silicon optical waveguides)
  • Speed: 400Gbps~7Tbps (utilizing WDM technology)
  • Latency: Ultra-low latency (tens of ps, minimal conversion delay)
  • Power Consumption: Low (e.g., 7Tbps ~10W or less), minimal heat with reduced cooling needs
  • Key Benefits:
    • Overcomes electrical circuit limitations
    • Supports 7Tbps-level AI communication
    • Optimized for AI workloads (high speed, low power)

Key Message

Silicon Photonics overcomes the limitations of existing PCIe technology (high power consumption, heat generation, speed limitations), making it a next-generation technology particularly well-suited for AI workloads requiring high-speed data processing.

With Claude