Category: Network

Infiniband vs RoCE v2

Posted on by lechuck park

This image provides a technical comparison between InfiniBand and RoCE v2 (RDMA over Converged Ethernet), the two dominant networking protocols used in modern AI data centers and High-Performance Computing (HPC) environments.

1. Architectural Philosophy

  • InfiniBand (Dedicated Hardware): Designed from the ground up specifically for high-throughput, low-latency communication. It is a proprietary ecosystem largely driven by NVIDIA (Mellanox).
  • RoCE v2 (General-Purpose + Optimization): An evolution of standard Ethernet designed to bring RDMA (Remote Direct Memory Access) capabilities to traditional network infrastructures. It is backed by the Open Consortium.

2. Hardware vs. Software Logic

  • Hardwired ASIC (InfiniBand): The protocol logic is baked directly into the silicon. This “Native” approach ensures consistent performance with minimal jitter.
  • Firmware & OS Dependent (RoCE v2): Relies more heavily on the NIC’s firmware and operating system configurations, making it more flexible but potentially more complex to stabilize.

3. Data Transfer Efficiency

  • Ultra-low Latency (InfiniBand): Utilizes Cut-through switching, where the switch starts forwarding the packet as soon as the destination address is read, without waiting for the full packet to arrive.
  • Encapsulation Overhead (RoCE v2): Because it runs on Ethernet, it must wrap RDMA data in UDP/IP/Ethernet headers. This adds “overhead” (extra data bits) and processing time compared to the leaner InfiniBand frames.

4. Reliability and Loss Management

  • Lossless by Design (InfiniBand): It uses a credit-based flow control mechanism at the hardware level, ensuring that a sender never transmits data unless the receiver has room to buffer it. This guarantees zero packet loss.
  • Tuning-Dependent (RoCE v2): Ethernet is natively “lossy.” To make RoCE v2 work effectively, the network must be “Converged” using complex features like PFC (Priority Flow Control) and ECN (Explicit Congestion Notification). Without precise tuning, performance can collapse during congestion.

5. Network Management

  • Subnet Manager (InfiniBand): Uses a centralized “Subnet Manager” to discover the topology and manage routing, which simplifies the management of massive GPU clusters.
  • Distributed Control (RoCE v2): Functions like a traditional IP network where routing and control are distributed across the switches and routers.

Comparison Summary

FeatureInfiniBandRoCE v2
Primary DriverPerformance & StabilityCost-effectiveness & Compatibility
ComplexityPlug-and-play (within IB ecosystem)Requires expert-level network tuning
LatencyAbsolute LowestLow (but higher than IB)
ScalabilityHigh (specifically for AI/HPC)High (standard Ethernet scalability)

Design & Logic: InfiniBand is a dedicated, hardware-native solution for ultra-low latency, whereas RoCE v2 adapts general-purpose Ethernet for RDMA through software-defined optimization and firmware.

Efficiency & Reliability: InfiniBand is “lossless by design” with minimal overhead via cut-through switching, while RoCE v2 incurs encapsulation overhead and requires precise network tuning to prevent packet loss.

Control & Management: InfiniBand utilizes centralized hardware-level management (Subnet Manager) for peak stability, while RoCE v2 relies on distributed software-level control over standard UDP/IP/Ethernet stacks.

#InfiniBand #RoCEv2 #RDMA #AIDataCenter #NetworkingArchitecture #NVIDIA #HighPerformanceComputing #GPUCluster #DataCenterDesign #Ethernet #AITraining

Network for AI

Posted on by lechuck park

1. Core Philosophy: All for Model Optimization

The primary goal is to create an “Architecture that fits the model’s operating structure.” Unlike traditional general-purpose data centers, AI infrastructure is specialized to handle the massive data throughput and synchronized computations required by LLMs (Large Language Models).

2. Hierarchical Network Design

The architecture is divided into two critical layers to handle different levels of data exchange:

A. Inter-Chip Network (Scale-Up)

This layer focuses on the communication between individual GPUs/Accelerators within a single server or node.

  • Key Goals: Minimize data copying and optimize memory utilization (Shared Memory/Memory Pooling).
  • Technologies: * NVLink / NVSwitch: NVIDIA’s proprietary high-speed interconnect.
  • UALink (Ultra Accelerator Link): The new open standard designed for scale-up AI clusters.

B. Inter-Server Network (Scale-Out)

This layer connects multiple server nodes to form a massive AI cluster.

  • Key Goals: Achieve “No Latency” (Ultra-low latency) and minimize routing overhead to prevent bottlenecks during collective communications (e.g., All-Reduce).
  • Technologies: * InfiniBand: A lossless, high-bandwidth fabric preferred for its low CPU overhead.
  • RoCE (RDMA over Converged Ethernet): High-speed Ethernet that allows direct memory access between servers.

3. Zero Trust Security & Physical Separation

A unique aspect of this architecture is the treatment of security.

  • Operational Isolation: The security and management plane is completely separated from the model operation plane.
  • Performance Integrity: By being physically separated, security protocols (like firewalls or encryption inspection) do not introduce latency into the high-speed compute fabric where the model runs. This ensures that a “Zero Trust” posture does not degrade training or inference speed.

4. Architectural Feedback Loop

The arrow at the bottom indicates a feedback loop: the performance metrics and requirements of the inter-chip and inter-server networks directly inform the ongoing optimization of the overall architecture. This ensures the platform evolves alongside advancing AI model structures.

The architecture prioritizes model-centric optimization, ensuring infrastructure is purpose-built to match the specific operating requirements of large-scale AI workloads.

It employs a dual-tier network strategy using Inter-chip (NVLink/UALink) for memory efficiency and Inter-server (InfiniBand/RoCE) for ultra-low latency cluster scaling.

Zero Trust security is integrated through complete physical separation from the compute fabric, allowing for robust protection without causing any performance bottlenecks.

#AIDC #ArtificialIntelligence #GPU #Networking #NVLink #UALink #InfiniBand #RoCEv2 #ZeroTrust #DataCenterArchitecture #MachineLearningOps #ScaleOut

Interconnection Driven Design (Deepseek v3)

Posted on by lechuck park

Interconnection Driven Design

This image outlines a technical approach to solving bottlenecks in High-Performance Computing (HPC) and AI/LLM infrastructure. It is categorized into three main rows, each progressing from a Problem to a Solution, and finally to a hardware-level Final Optimization.

1. Convergence of Scale-Up and Scale-Out

Focuses on resolving inefficiencies between server communication and GPU computation.

  • Problem (IB Communication): The speed of inter-server connections (e.g., InfiniBand) creates a bottleneck for total system performance.
  • Inefficiency (Streaming Multiprocessor): The GPU’s core computational units (SMs) waste resources handling network overhead instead of focusing on actual calculations.
  • Solution (SM Offload): Communication tasks are delegated (offloaded) to dedicated coprocessors, allowing SMs to focus exclusively on computation.
  • Final Optimization (Unified Network Adapter): Physically integrating intra-node and inter-node communication into a single Network Interface Card (NIC) to minimize data movement paths.

2. Bandwidth Contention & Latency

Addresses the limitations of data bandwidth and processing delays.

  • Problem (KV Cache): Reusable token data for LLM inference frequently travels between the CPU and GPU, consuming significant bandwidth.
  • Bottleneck (PCIe): The primary interconnect has limited bandwidth, leading to contention and performance degradation during traffic spikes.
  • Solution (Traffic Class – TC): A prioritization mechanism (QoS) ensures urgent, latency-sensitive traffic is processed before less critical data.
  • Final Optimization (I/O Die Chiplet Integration): Integrating network I/O directly alongside the GPU die bypasses PCIe entirely, eliminating contention and drastically reducing latency.

3. Node-Limited Routing

Optimizes data routing strategies for distributed neural networks.

  • Key Tech (NVLink): A high-speed, intra-node GPU interconnect strategically used to maximize local data transfer.
  • Context (Experts): Neural network modules (MoE – Mixture of Experts) are distributed across various nodes, requiring activation for specific tokens.
  • Solution/Strategy (Minimize IB Cost): Reducing overhead by restricting slow inter-node usage (InfiniBand) to a single hop while distributing data internally via fast NVLink.
  • Final Optimization (Node-Limited): Algorithms restrict the selection of “Experts” (modules) to a limited node group, reducing inter-node traffic and guaranteeing communication efficiency.

Summary

  1. Integration: The design overcomes system bottlenecks by physically unifying network adapters and integrating I/O dies directly with GPUs to bypass slow connections like PCIe.
  2. Offloading & Prioritization: It improves efficiency by offloading network tasks from GPU cores (SMs) and prioritizing urgent traffic (Traffic Class) to reduce latency.
  3. Routing Optimization: It utilizes “Node-Limited” routing strategies to maximize high-speed local connections (NVLink) and minimize slower inter-server communication in distributed AI models.

#InterconnectionDrivenDesign #AIInfrastructure #GPUOptimization #HPC #ChipletIntegration #NVLink #LatencyReduction #LLMHardware #infiniband

With Gemini

Multi-Plane Network Topology ( deepseek v3)

Posted on by lechuck park

Multi-Plane Network Topology for Scalable AI Clusters

Core Architecture (Left – Green Sections)

Topology Structure

  • Adopts 2-Tier Fat-Tree (FT2) architecture for reduced latency and cost efficiency compared to 3-Tier
  • Achieves massive scale connections at much lower cost than 3-tier architectures

Multi-Plane Design

  • 8-Plane Architecture: Each node contains 8 GPUs and 8 IB NICs
  • 1:1 Mapping: Dedicates specific GPU-NIC pairs to separate planes

NIC Specifications

  • Hardware: 400G InfiniBand (ConnectX-7)
  • Resilience: Multi-port connectivity ensures robustness against single-port failures

Maximum Scalability

  • Theoretically supports up to 16,384 GPUs within the 2-tier structure

Advantages (Center – Purple Sections)

Cost Efficiency: Connects massive scale at much lower cost compared to 3-tier architectures

Ultra-Low Latency: Fewer network hops ensure rapid data transfer, ideal for latency-sensitive AI models like MoE

Traffic Isolation: Independent communication lanes (planes) prevent congestion or faults in one lane from affecting others

Proven Performance: Validated in large-scale tests with 2048 GPUs, delivering stable and high-speed communication

Challenges (Right – Orange Sections)

Packet Ordering Issues: Current hardware (ConnectX-7) has limitations in handling out-of-order data packets

Cross-Plane Delays: Moving data between different network planes requires extra internal forwarding, causing higher latency during AI inference

Smarter Routing Needed: Standard traffic methods (ECMP) are inefficient for AI; requires Adaptive Routing that intelligently selects the best path based on network traffic

Hardware Integration: Future hardware should build network components directly into main chips to remove bottlenecks and speed up communication

Summary

This document presents a multi-plane network topology using 2-tier Fat-Tree architecture that scales AI clusters up to 16,384 GPUs cost-effectively with ultra-low latency. The 8-plane design with 1:1 GPU-NIC mapping provides traffic isolation and resilience, though challenges remain in packet ordering and cross-plane communication. Future improvements require smarter routing algorithms and deeper hardware-network integration to optimize AI workload performance.

#AIInfrastructure #DataCenterNetworking #HPC #InfiniBand #GPUCluster #NetworkTopology #FatTree #ScalableComputing #MLOps #AIHardware #DistributedComputing #CloudInfrastructure #NetworkArchitecture #DeepLearning #AIatScale

AI Data Center: Critical Bottlenecks and Technological Solutions

Posted on by lechuck park

AI Data Center: Critical Bottlenecks and Technological Solutions

This chart analyzes the major challenges facing modern AI Data Centers across six key domains. It outlines the [Domain] → [Bottleneck/Problem] → [Solution] flow, indicating the severity of each bottleneck with a score out of 100.

1. Generative AI

  • Bottleneck (45/100): Redundant Computation
    • Inefficiencies occur when calculating massive parameters for large models.
  • Solutions:
    • MoE (Mixture of Experts): Uses only relevant sub-models (experts) for specific tasks to reduce computation.
    • Quantization (FP16 → INT8/FP4): Reduces data precision to speed up processing and save memory.

2. OS for AI Works

  • Bottleneck (55/100): Low MFU (Model Flops Utilization)
    • Issues with resource fragmentation and idle time result in underutilization of hardware.
  • Solutions:
    • Dynamic Checkpointing: Efficiently saves model states during training.
    • AI-Native Scheduler: Optimizes task distribution based on network topology.

3. Computing / AI Engine (Most Critical)

  • Bottleneck (85/100): Memory Wall
    • Marked as the most severe bottleneck, where memory bandwidth cannot keep up with the speed of logic processors.
  • Solutions:
    • HBM3e/HBM4: Next-generation High Bandwidth Memory.
    • PIM (Processing In Memory): Performs calculations directly within memory to reduce data movement.

4. Network

  • Bottleneck (75/100): Communication Overhead
    • Latency issues arise during synchronization between multiple GPUs.
  • Solutions:
    • UEC-based RDMA: Ultra Ethernet Consortium standards for faster direct memory access.
    • CPO / LPO: Advanced optics (Co-Packaged/Linear Drive) to improve data transmission efficiency.

5. Power

  • Bottleneck (65/100): Density Cap
    • Physical limits on how much power can be supplied per server rack.
  • Solutions:
    • 400V HVDC: High Voltage Direct Current for efficient power delivery.
    • BESS Peak Shaving: Using Battery Energy Storage Systems to manage peak power loads.

6. Cooling

  • Bottleneck (70/100): Thermal Throttling Limit
    • Performance drops (throttling) caused by excessive heat in high-density racks.
  • Solutions:
    • DTC Liquid Cooling: Direct-to-Chip liquid cooling technologies.
    • CDU: Coolant Distribution Units for effective heat management.

Summary

  1. The “Memory Wall” (85/100) is identified as the most critical bottleneck in AI Data Centers, meaning memory bandwidth is the primary constraint on performance.
  2. To overcome these limits, the industry is adopting advanced hardware like HBM and Liquid Cooling, alongside software optimizations like MoE and Quantization.
  3. Scaling AI infrastructure requires a holistic approach that addresses computing, networking, power efficiency, and thermal management simultaneously.

#AIDataCenter #ArtificialIntelligence #MemoryWall #HBM #LiquidCooling #GenerativeAI #TechTrends #AIInfrastructure #Semiconductor #CloudComputing

With Gemini

AI Operation : All Connected

Posted on by lechuck park

AI Operation: All Connected – Image Analysis

This diagram explains the operational paradigm shift in AI Data Centers (AI DC).

Top Section: New Challenges

AI DC Characteristics:

  • Paradigm shift: Fundamental change in operations for the AI era
  • High Cost: Massive investment required for GPUs, infrastructure, etc.
  • High Risk: Greater impact during outages and increased complexity

Five Core Components of AI DC (left→right):

  1. Software: AI models, application development
  2. Computing: GPUs, servers, and computational resources
  3. Network: Data transmission and communication infrastructure
  4. Power: High-density power supply and management (highlighted in orange)
  5. Cooling: Heat management and cooling systems

→ These five elements are interconnected through the “All Connected Metric”

Bottom Section: Integrated Operations Solution

Core Concept:

📦 Tightly Fused Rubik’s Cube

  • The five core components (Software, Computing, Network, Power, Cooling) are intricately intertwined like a Rubik’s cube
  • Changes or issues in one element affect all other elements due to tight coupling

🎯 All Connected Data-Driven Operations

  • Data-driven integrated operations: Collecting and analyzing data from all connected elements
  • “For AI, With AI”: Operating the data center itself using AI technology for AI workloads

✅ Continuous Stability & Optimization

  • Ensuring continuous stability
  • Real-time monitoring and optimization

Key Message

AI data centers have five core components—Software, Computing, Network, Power, and Cooling—that are tightly fused together. To effectively manage this complex system, a data-centric approach that integrates and analyzes data from all components is essential, enabling continuous stability and optimization.

Summary

AI data centers are characterized by tightly coupled components (software, computing, network, power, cooling) that create high complexity, cost, and risk. This interconnected system requires data-driven operations that leverage AI to monitor and optimize all elements simultaneously. The goal is achieving continuous stability and optimization through integrated, real-time management of all connected metrics.

#AIDataCenter #DataDrivenOps #AIInfrastructure #DataCenterOptimization #TightlyFused #AIOperations #HybridInfrastructure #IntelligentOps #AIforAI #DataCenterManagement #MLOps #AIOps #PowerManagement #CoolingOptimization #NetworkInfrastructure

Large Scale Network Driven Design ( Deepseek V3)

Posted on by lechuck park

Deepseek v3 Large-Scale Network Architecture Analysis

This image explains the Multi-Plane Fat-Tree network structure of Deepseek v3.

Core Architecture

1. 8-Plane Architecture

  • Consists of eight independent network channels (highways)
  • Maximizes network bandwidth and distributes traffic for enhanced scalability

2. Fat-Tree Topology

  • Two-layer switch structure:
    • Leaf SW (Leaf Switches): Directly connected to GPUs
    • Spine SW (Spine Switches): Interconnect leaf switches
  • Enables high-speed communication among all nodes (GPUs) while minimizing switch contention

3. GPU/IB NIC Pair

  • Each GPU is paired with a dedicated Network Interface Card (NIC)
  • Each pair is exclusively assigned to one of the eight planes to initiate communication

Communication Methods

NVLink

  • Ultra-high-speed connection between GPUs within the same node
  • Fast data transfer path used for intra-node communication

Cross-plane Traffic

  • Occurs when communication happens between different planes
  • Requires intra-node forwarding through another NIC, PCIe, or NVLink
  • Primary factor that increases latency

Network Optimization Process

The workflow below minimizes latency and prevents network congestion:

  1. Workload Analysis
  2. All to All (analyzing all-to-all communication patterns)
  3. Plane & Layer Set (plane and layer assignment)
  4. Profiling (Hot-path opt K) (hot-path optimization)
  5. Static Routing (Hybrid) (hybrid static routing approach)

Goal: Low latency & no jamming

Scalability

This design is a scale-out network for large-scale distributed training supporting 16,384+ GPUs. Each plane operates independently to maximize overall system throughput.

3-Line Summary

Deepseek v3 uses an 8-plane fat-tree network architecture that connects 16,384+ GPUs through independent communication channels, minimizing contention and maximizing bandwidth. The two-layer switch topology (Spine and Leaf) combined with dedicated GPU-NIC pairs enables efficient traffic distribution across planes. Cross-plane traffic management and hot-path optimization ensure low-latency, high-throughput communication for large-scale AI training.

#DeepseekV3 #FatTreeNetwork #MultiPlane #NetworkArchitecture #ScaleOut #DistributedTraining #AIInfrastructure #GPUCluster #HighPerformanceComputing #NVLink #DataCenterNetworking #LargeScaleAI

With Claude