Tag: ai

AI Workload with Power/Cooling

Posted on by lechuck park

Breakdown of the “AI Workload with Power/Cooling” Diagram

This diagram illustrates the flow of Power and Cooling changes throughout the execution stages of an AI workload. It divides the process into five phases, explaining how data center infrastructure (Power, Cooling) reacts and responds from the start to the completion of an AI job.

Here are the key details for each phase:

1. Pre-Run (Preparation Phase)

  • Work Job: Job Scheduling.
  • Key Metric: Requested TDP (Thermal Design Power). It identifies beforehand how much heat the job is expected to generate.
  • Power/Cooling: PreCooling. This is a proactive measure where cooling levels are increased based on the predicted TDP before the job actually starts and heat is generated.

2. Init / Ramp-up (Initialization Phase)

  • Work Job: Context Loading. The process of loading AI models and data into memory.
  • Key Metric: HBM Power Usage. The power consumption of High Bandwidth Memory becomes a key indicator.
  • Power/Cooling: As VRAM operates, Power consumption begins to rise (Power UP).

3. Execution (Execution Phase)

  • Work Job: Kernel Launch. The point where actual computation kernels begin running on the GPU.
  • Key Metric: Power Draw. The actual amount of electrical power being drawn.
  • Power/Cooling: Instant Power Peak. A critical moment where power consumption spikes rapidly as computation begins in earnest. The stability of the power supply unit (PSU) is vital here.

4. Sustained (Heavy Load Phase)

  • Work Job: Heavy Load. Continuous heavy computation is in progress.
  • Key Metric: Thermal/Power Cap. Monitoring against set limits for temperature or power.
  • Power/Cooling:
    • Throttling: If “What-if” scenarios occur (such as power supply leaks or reaching a Thermal Over-Limit), protection mechanisms activate. DVFS (Dynamic Voltage and Frequency Scaling) triggers Throttling (Down Clock) to protect the hardware.

5. Cooldown (Completion Phase)

  • Work Job: Job Complete.
  • Key Metric: Power State. The state changes to “Change Down.”
  • Power/Cooling: Although the job is finished, Residual Heat remains in the hardware. Instead of shutting off fans immediately, Ramp-down Control is used to cool the equipment gradually and safely.

Summary & Key Takeaways

This diagram demonstrates that managing AI infrastructure goes beyond simply “running a job.” It requires active control of the infrastructure (e.g., PreCooling, Throttling, Ramp-down) to handle the specific characteristics of AI workloads, such as rapid power spikes and high heat generation.

Phase 1 (PreCooling) for proactive heat management and Phase 4 (Throttling) for hardware protection are the core mechanisms determining the stability and efficiency of an AI Data Center.

#AI #ArtificialIntelligence #GPU #HPC #DataCenter #AIInfrastructure #DataCenterOps #GreenIT #SustainableTech #SmartCooling #PowerEfficiency #PowerManagement #ThermalEngineering #TDP #DVFS #Semiconductor #SystemArchitecture #ITOperations

With Gemini

Linux kernel for GPU Workload

Posted on by lechuck park

Linux Kernel GPU Workload Support Features

Goal: Maximize Memory Efficiency & Data Transfer

The core objective is to treat GPUs as a top-tier component like CPUs, reducing memory bottlenecks for large-scale AI workloads.

Key Features

1. Full CXL (Compute Express Link) Support

  • Standard interface for high-speed connections between CPUs, accelerators (GPU, FPGA), and memory expansion devices
  • Enables high-speed data transfer

2. Enhanced HMM (Heterogeneous Memory Management)

  • Heterogeneous memory management capabilities
  • Allows device drivers to map system memory pages to GPU page tables
  • Enables seamless GPU memory access

3. Enhanced P2P DMA & GPUDirect Support

  • Enables direct data exchange between GPUs
  • Direct communication with NVMe storage and network cards (GPUDirect RDMA)
  • Operates without CPU intervention for improved performance

4. DRM Scheduler & GPU Driver Improvements

  • Enhanced Direct Rendering Manager scheduling functionality
  • Active integration of latest drivers from major vendors: AMD (AMDGPU), Intel (i915/Xe), Intel Gaudi/Ponte Vecchio
  • NVIDIA still uses proprietary drivers

5. Advanced Async I/O via io_uring

  • Efficient I/O request exchange with kernel through Ring Buffer mechanism
  • Optimized asynchronous I/O performance

Summary

The Linux kernel now enables GPUs to independently access memory (CXL, HMM), storage, and network resources (P2P DMA, GPUDirect) without CPU involvement. Enhanced drivers from AMD, Intel, and improved schedulers optimize GPU workload management. These features collectively eliminate CPU bottlenecks, making the kernel highly efficient for large-scale AI and HPC workloads.

#LinuxKernel #GPU #AI #HPC #CXL #HMM #GPUDirect #P2PDMA #AMDGPU #IntelGPU #MachineLearning #HighPerformanceComputing #DRM #io_uring #HeterogeneousComputing #DataCenter #CloudComputing

With Claude

Big Changes with AI

Posted on by lechuck park

This image illustrates the dramatic growth in computing performance and data throughput from the Internet era to the AI/LLM era.

Key Development Stages

1. Internet Era

  • 10 TWh (terawatt-hours) power consumption
  • 2 PB/day (petabytes/day) data processing
  • 1K DC (1,000 data centers)
  • PUE 3.0 (Power Usage Effectiveness)

2. Mobile & Cloud Era

  • 200 TWh (20x increase)
  • 20,000 PB/day (10,000x increase)
  • 4K DC (4x increase)
  • PUE 1.8 (improved efficiency)

3. AI/LLM (Transformer) Era – “Now Here?” point

  • 400+ TWh (40x additional increase)
  • 1,000,000,000 PB/day = 1 billion PB/day (500,000x increase)
  • 12K DC (12x increase)
  • PUE 1.4 (further improved efficiency)

Summary

The chart demonstrates unprecedented exponential growth in data processing and power consumption driven by AI and Large Language Models. While data center efficiency (PUE) has improved significantly, the sheer scale of computational demands has skyrocketed. This visualization emphasizes the massive infrastructure requirements that modern AI systems necessitate.

#AI #LLM #DataCenter #CloudComputing #MachineLearning #ArtificialIntelligence #BigData #Transformer #DeepLearning #AIInfrastructure #TechTrends #DigitalTransformation #ComputingPower #DataProcessing #EnergyEfficiency

AI approach

Posted on by lechuck park

Legacy – The Era of Scale-Up

Traditional AI approach showing its limitations:

  • Simple Data: Starting with basic data
  • Simple Data & Logic: Combining data with logic
  • Better Data & Logic: Improving data and logic
  • Complex Data & Logic: Advancing to complex data and logic
  • Near The Limitation: Eventually hitting a fundamental ceiling

This approach gradually increases complexity, but no matter how much it improves, it inevitably runs into fundamental scalability limitations.

AI Works – The Era of Scale-Out

Modern AI transcending the limitations of the legacy approach through a new paradigm:

  • The left side shows the limitations of the old approach
  • The lightbulb icon in the middle represents a paradigm shift (Breakthrough)
  • The large purple box on the right demonstrates a completely different approach:
    • Massive parallel processing of countless “01/10” units (neural network neurons)
    • Horizontal scaling (Scale-Out) instead of sequential complexity increase
    • Fundamentally overcoming the legacy limitations

Key Message

No matter how much you improve the legacy approach, there’s a ceiling. AI breaks through that ceiling with a completely different architecture.

Summary

  • Legacy AI hits fundamental limits by sequentially increasing complexity (Scale-Up)
  • Modern AI uses massive parallel processing architecture to transcend these limitations (Scale-Out)
  • This represents a paradigm shift from incremental improvement to architectural revolution

#AI #MachineLearning #DeepLearning #NeuralNetworks #ScaleOut #Parallelization #AIRevolution #Paradigmshift #LegacyVsModern #AIArchitecture #TechEvolution #ArtificialIntelligence #ScalableAI #DistributedComputing #AIBreakthrough

Multi-Head Latent Attention – Changes

Posted on by lechuck park

Multi-Head Latent Attention (MLA) Interpretation

This image is a technical diagram explaining the structure of Multi-Head Latent Attention (MLA).

🎯 Core Concept

MLA is a mechanism that improves the memory efficiency of traditional Multi-Head Attention.

Traditional Approach (Before) vs MLA

Traditional Approach:

  • Stores K, V vectors of all past tokens
  • Memory usage increases linearly with sequence length

MLA:

  • Summarizes past information with a fixed-size Latent vector (c^KV)
  • Maintains constant memory usage regardless of sequence length

📊 Architecture Explanation

1. Input Processing

  • Starts from Input Hidden State (h_t)

2. Latent Vector Generation

  • Latent c_t^Q: For Query of current token (compressed representation)
  • Latent c_t^KV: For Key-Value (cached and reused)

3. Query, Key, Value Generation

  • Query (q): Generated from current token (h_t)
  • Key-Value: Generated from Latent c_t^KV
    • Creates Compressed (C) and Recent (R) versions from c_t^KV
    • Concatenates both for use

4. Multi-Head Attention Execution

  • Performs attention computation with generated Q, K, V
  • Uses BF16 (Mixed Precision)

✅ Key Advantages

  1. Memory Efficiency: Compresses past information into fixed-size vectors
  2. Faster Inference: Reuses cached Latent vectors
  3. Information Preservation: Maintains performance by combining compressed and recent information
  4. Mixed Precision Support: Utilizes FP8, FP32, BF16

🔑 Key Differences

  • v_t^R from Latent c_t^KV is not used (purple box on the right side of diagram)
  • Value of current token is directly generated from h_t
  • This enables efficient combination of compressed past information and current information

This architecture is an innovative approach to solve the KV cache memory problem during LLM inference.

Summary

MLA replaces the linearly growing KV cache with fixed-size latent vectors, dramatically reducing memory consumption during inference. It combines compressed past information with current token data through an efficient attention mechanism. This innovation enables faster and more memory-efficient LLM inference while maintaining model performance.

#MultiHeadLatentAttention #MLA #TransformerOptimization #LLMInference #KVCache #MemoryEfficiency #AttentionMechanism #DeepLearning #NeuralNetworks #AIArchitecture #ModelCompression #EfficientAI #MachineLearning #NLP #LargeLanguageModels

With Claude

Programming … AI

Posted on by lechuck park

This image contrasts traditional programming, where developers must explicitly code rules and logic (shown with a flowchart and a thoughtful programmer), with AI, where neural networks automatically learn patterns from large amounts of data (depicted with a network diagram and a smiling programmer). It illustrates the paradigm shift from manually defining rules to machines learning patterns autonomously from data.

#AI #MachineLearning #Programming #ArtificialIntelligence #AIvsTraditionalProgramming