GPU Computing

Table of Contents Introduction Background: Why Latency Matters for LLM Inference Core Challenges in Heterogeneous Multi‑GPU Environments Architectural Foundations 4.1 Model Parallelism 4.2 Pipeline Parallelism 4.3 Tensor Parallelism 4.4 Hybrid Strategies Communication Optimizations 5.1 NVLink & PCIe Topology 5.2 NCCL & Collective Algorithms 5.3 RDMA & GPUDirect 5.4 Compression & Quantization Scheduling, Load Balancing, and Straggler Mitigation Memory Management Techniques 7.1 KV‑Cache Sharding & Offloading 7.2 Activation Checkpointing for Inference Serving Patterns that Reduce Latency 8.1 Dynamic Batching 8.2 Asynchronous Request Pipelines Practical End‑to‑End Example Best‑Practice Checklist Conclusion Resources Introduction Large language models (LLMs) such as GPT‑4, LLaMA‑2, and Claude have moved from research curiosities to production‑grade services. Companies now expose these models through APIs that must deliver sub‑second response times while handling thousands of concurrent users. Achieving low inference latency is especially hard when the model does not fit on a single GPU and must be spread across a heterogeneous multi‑GPU cluster—a mix of different GPU generations, memory capacities, and interconnect topologies. ...

Table of Contents Introduction What Is WebGPU? Why WebGPU Matters: A Comparison with WebGL Core Architecture and Terminology Setting Up a WebGPU Development Environment Writing Shaders with WGSL Practical Example: A Rotating 3‑D Cube Performance Tips & Best Practices Debugging, Profiling, and Tooling Real‑World Use Cases and Success Stories The Future of WebGPU Conclusion Resources Introduction The web has evolved from static pages to rich, interactive experiences that rival native applications. Central to this evolution is the ability to harness the power of the graphics processing unit (GPU) directly from the browser. For more than a decade, WebGL has been the de‑facto standard for 3‑D graphics on the web. However, as developers demand more compute‑intensive workloads—real‑time ray tracing, machine‑learning inference, scientific visualization—the limitations of WebGL’s API surface become apparent. ...

Table of Contents Introduction Why Scaling LLM Inference Is Hard 2.1 Memory Footprint 2.2 Compute Throughput 2.3 Latency vs. Batch Size Trade‑offs Fundamentals of CUDA for LLMs 3.1 Thread Hierarchy & Memory Types 3.2 Warp‑level Primitives 3.3 Common Pitfalls Designing Custom CUDA Kernels for Transformer Ops 4.1 Matrix‑Multiplication (GEMM) Optimizations 4.2 Fused Attention Kernel 4.3 Layer Normalization & Activation Fusion 4.4 Kernel Launch Configuration Best Practices Distributed Memory Management Strategies 5.1 Tensor Parallelism 5.2 Pipeline Parallelism 5.3 Hybrid Parallelism 5.4 Memory Swapping & Off‑loading Putting It All Together: A Full‑Stack Inference Pipeline 6.1 Data Flow Diagram 6.2 Implementation Sketch (Python + PyCUDA) 6.3 Performance Benchmarking Methodology Real‑World Case Studies 7.1 OpenAI’s “ChatGPT” Scaling Journey 7.2 Meta’s LLaMA‑2 Production Deployment 7.3 Start‑up Example: Low‑Latency Chatbot on a 4‑GPU Node Future Directions & Emerging Technologies 8.1 Tensor Cores Beyond FP16/BF16 8.2 NVidia Hopper & Transformer Engine 8.3 Unified Memory & NVLink‑based Hierarchical Memory Conclusion Resources Introduction Large language models (LLMs) have transitioned from research curiosities to production‑grade services that power chatbots, code assistants, and search engines. While training these models often dominates headlines, inference—the process of generating predictions from a trained model—poses its own set of engineering challenges. As model sizes balloon past 100 B parameters, a single forward pass can consume tens of gigabytes of GPU memory and require hundreds of teraflops of compute. ...