CUDA
CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing platform that allows developers to use GPUs for general-purpose processing. CUDA is the foundation of GPU-accelerated deep learning training.
Understanding CUDA
CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA that allows developers to harness GPU acceleration for general-purpose computing tasks. In artificial intelligence, CUDA is the backbone enabling fast training and inference of deep learning models by distributing matrix operations across thousands of GPU cores simultaneously. Frameworks like PyTorch and TensorFlow rely on CUDA to execute tensor computations efficiently, reducing training times from weeks to hours. CUDA supports distributed training across multiple GPUs, making it indispensable for scaling foundation models and large language models. Beyond AI, CUDA accelerates scientific simulations, video rendering, and cryptographic operations. Understanding CUDA optimization is a key skill for engineers working on high-performance machine learning infrastructure.
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AI Infrastructure
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AI Chip
An AI chip is a specialized processor designed specifically for artificial intelligence workloads like neural network training and inference. Examples include NVIDIA's GPUs, Google's TPUs, and custom ASICs.
API
An API (Application Programming Interface) is a set of protocols and tools that allows different software systems to communicate. AI APIs enable developers to integrate machine learning capabilities like text generation, image recognition, and speech processing into applications.
Data Lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format. AI systems often draw training data from data lakes that store structured, semi-structured, and unstructured information.
Data Pipeline
A data pipeline is an automated series of data processing steps that moves and transforms data from source systems to a destination. ML data pipelines handle ingestion, cleaning, feature engineering, and model training workflows.
Data Warehouse
A data warehouse is a centralized repository for structured, processed data optimized for analysis and reporting. AI and ML systems often source their training data from enterprise data warehouses.
Distributed Training
Distributed training is the practice of splitting model training across multiple GPUs or machines to handle large models and datasets. It uses data parallelism or model parallelism to accelerate training.
Edge AI
Edge AI refers to running artificial intelligence algorithms locally on hardware devices rather than in the cloud. Edge AI enables real-time inference with lower latency, better privacy, and reduced bandwidth requirements.
Feature Store
A feature store is a centralized repository for storing, managing, and serving machine learning features. It enables feature reuse, consistency between training and serving, and collaboration across ML teams.