GPU programming - role in model training **Massive Parallelism:** GPUs execute thousands of threads concurrently, enabling significant speedups for data-parallel tasks like deep learning and graphics rendering. Understanding the GPU’s execution model (kernels, threads, blocks, warps) is crucial for leveraging this parallel power. **Memory Hierarchy Mastery:** Effective GPU programming optimizes data placement across registers, shared memory, caches, and global VRAM. Coalescing memory accesses and using on-chip shared memory can significantly boost throughput by maximizing bandwidth. **Efficient Training Pipelines:** For ML, a robust data strategy (labeled datasets, augmentations, synthetic data) and careful training setup (learning rate, batch size, mixed precision, LoRA rank) ensure GPUs remain fully utilized, avoiding bottlenecks. **Robust Evaluation:** Use appropriate metrics (accuracy, throughput, latency, perplexity) and rigorous validation (hold-out tests, ablations) to verify model performance. Monitor for overfitting or numerical instability, as GPUs can introduce subtleties like non-deterministic float summations. **Deployment & Scaling:** Leverage optimized libraries (cuDNN, TensorRT) and multi-GPU scaling via NVLink/InfiniBand. Monitor GPU utilization, memory usage, and latency to ensure performance and catch regressions. **Advanced Optimizations:** Techniques like asynchronous execution, mixed-precision/Tensor Cores, unified memory, and kernel fusion can yield significant performance gains. For example, 8-bit precision can accelerate training/inference by 5–10× with minimal loss. **End-Goal | Why:** - **Maximize GPU Throughput:** Fully utilize thousands of cores for faster training/inference, shortening time-to-results for AI and HPC. - **Minimize Memory Bottlenecks:** Optimize data movement to avoid stalls, leveraging high-bandwidth HBM (~3 TB/s on H100 GPUs). - **Ensure Correct Execution:** Use synchronization (e.g., __syncthreads() in CUDA) for reliable, race-free results. - **Reduce Training Cost/Time:** Efficient techniques like mixed precision cut GPU hours without sacrificing accuracy. - **Leverage Specialized Hardware:** Tensor Cores accelerate matrix operations by ~9× for training and 30× for inference on LLMs. - **Real-time & Scalable Deployment:** Optimize for real-time constraints (e.g., 60 FPS graphics) and scalable multi-GPU services. **Environment/Infrastructure:** Set up a capable GPU environment with Linux x86_64, recent NVIDIA drivers, and CUDA Toolkit (e.g., CUDA 12.9). Use Python (3.10+) with PyTorch 2.x or TensorFlow 2.x for high-level GPU access, and NVCC for CUDA C/C++. Choose GPUs like NVIDIA A100 80GB for training or RTX 4090 for prototyping (~$1,599). Cloud options include AWS P4d/P5 (H100) or Google Cloud A3. Verify GPU visibility with `nvidia-smi` and Python (`torch.cuda.is_available()`). Manage cloud quotas and use containers (e.g., Docker nvidia/cuda:12.2.0) for reproducibility. Budget for costs: H100 GPUs cost ~$25k to purchase or $2.99–$10/hour to rent. **Data Strategy:** - **Supervised Core Data:** Use high-quality labeled datasets (e.g., 1M ImageNet images or domain-specific text). Ensure consistent labeling and fast storage (NVMe SSD) to avoid bottlenecks. - **Semi-Supervised & Augmentation:** Apply on-the-fly augmentations (crops, flips, paraphrasing) using GPU-accelerated libraries (DALI, Albumentations). Generate synthetic data or use pseudo-labeling to expand datasets, ensuring validation to avoid noise. - **Data Schema:** Store data in JSONL or CSV for easy ingestion, e.g., `{"instruction": "Translate to French", "input": "Hello, world!", "output": "Bonjour le monde !"}`. Perform sanity checks to avoid issues like corrupt data or test leakage. **References:** - Nguyen, H. (2025). “From Startup to Scale: Leveraging GPU Rentals for Cost-Efficient AI Development.” Nebula Block Blog. - Accio Analytics (2025). “2025 GPU Price Trends: Regional Shocks & Value Insights.” accio.com. - Heinonen, N. (2023). “Optimizing OpenMC performance for exascale.” Argonne Leadership Computing Facility. - Sooriyarachchi, A. (2023). “Efficient Fine-Tuning with LoRA.” Databricks Engineering Blog. - Fear, E. (2025). “Everything You Need to Know About Nvidia H100 GPUs.” Runpod Blog. - Lopez, G., et al. (2023). “Simplifying GPU Programming with NVIDIA Grace Hopper.” NVIDIA Technical Blog. - Salvator, D. (2023). “NVIDIA H100 GPUs Now Available on AWS Cloud.” NVIDIA Blog. - Jarvislabs.ai (2025). “NVIDIA H100 Price Guide 2025.” docs.jarvislabs.ai.