CUDA

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. ...

Table of Contents Introduction Why Inference Optimization Matters The vLLM Architecture at a Glance 3.1 Dynamic Paging and Memory Management 3.2 Scheduler and Batch Fusion Identifying Bottlenecks in Standard LLM Serving Custom Kernel Development: When and How 5.1 Choosing the Right Kernel to Accelerate 5.2 CUDA Basics for LLM Engineers Hands‑On: Building a CUDA Kernel for Multi‑Head Attention 6.1 Reference Implementation in PyTorch 6.2 Porting to CUDA: Step‑by‑Step 6.3 Integrating the Kernel with vLLM Performance Evaluation 7.1 Benchmark Setup 7.2 Results and Analysis Production‑Ready Deployment Tips Future Directions & Community Roadmap Conclusion Resources Introduction Large language models (LLMs) have moved from research curiosities to production‑grade services that power chatbots, code assistants, and knowledge‑base search. While the training phase often dominates headlines, the inference phase is where cost, latency, and user experience converge. A single request to a 70‑billion‑parameter model can consume multiple gigabytes of GPU memory and stall a server for seconds if not carefully engineered. ...

Introduction Large Language Models (LLMs) such as GPT‑3, LLaMA, and PaLM have demonstrated unprecedented capabilities across natural‑language processing tasks. However, their size—often ranging from hundreds of millions to hundreds of billions of parameters—poses a formidable challenge when serving them in production. Inference latency, memory consumption, and throughput become critical bottlenecks, especially for real‑time applications like chat assistants, code generation, or recommendation engines. Two complementary strategies have emerged to address these challenges: ...

Introduction Training modern deep‑learning models—think GPT‑4‑scale transformers, ResNet‑152, or large recommendation systems—requires massive computational resources. A single GPU can no longer finish a training epoch in a reasonable amount of time, so practitioners turn to distributed training across dozens or even hundreds of accelerators. While the high‑level idea—split work, sync gradients, repeat—sounds simple, achieving linear scaling is surprisingly hard. Two low‑level pillars dominate performance: CUDA kernels that run on each GPU. Their efficiency determines how fast a single device can process its share of data. Network communication that stitches the devices together. Latency, bandwidth, and protocol overhead dictate how quickly gradients and parameters are exchanged. In this guide we dive deep into both aspects, exploring theory, practical tuning techniques, and real‑world examples. By the end you’ll have a checklist you can apply to any PyTorch/TensorFlow job, and a concrete case study that demonstrates measurable speed‑ups. ...

CUDA (Compute Unified Device Architecture) is NVIDIA’s powerful parallel computing platform that unlocks the immense computational power of GPUs for general-purpose computing. Mastering CUDA enables developers to accelerate applications in AI, scientific simulations, and high-performance computing by leveraging thousands of GPU cores.[1][2] This detailed guide takes you from beginner fundamentals to advanced optimization techniques, complete with code examples, architecture insights, and curated resources. Why Learn CUDA? GPUs excel at parallel workloads due to their architecture: thousands of lightweight cores designed for SIMD (Single Instruction, Multiple Data) operations, contrasting CPUs’ focus on sequential tasks with complex branching.[3] CUDA programs can achieve 100-1000x speedups over CPU equivalents for matrix operations, deep learning, and simulations.[1][4] ...