GPU Programming

GPU programming is a specialized field of programming focused on writing code that executes on a Graphics Processing Unit (GPU), leveraging its massively parallel architecture to accelerate computationally intensive tasks. Unlike a CPU, which typically has a few powerful cores optimized for sequential and complex operations, a GPU contains thousands of simpler cores designed to perform the same operation on multiple data points simultaneously. This approach, known as parallel computing, is exceptionally effective for problems that can be broken down into many independent, repetitive calculations, making it indispensable for applications in machine learning, scientific simulation, data analysis, and real-time graphics rendering.

1. Introduction to Parallel Computing and GPU Architecture
   1. The Need for Parallelism
      1. Limitations of Sequential Computing
         1. Serial Execution Bottlenecks
         2. Diminishing Returns from Clock Speed Increases
         3. Memory Wall Problem
         4. Instruction-Level Parallelism Limits
      2. Moore's Law and its Evolution
         1. Historical Trends in Transistor Density
         2. Impact on Processor Design
         3. Transition to Multi-core and Many-core Architectures
         4. End of Dennard Scaling
      3. Power and Thermal Constraints
         1. Power Wall
         2. Heat Dissipation Challenges
         3. Energy Efficiency Considerations
         4. Dark Silicon Problem
   2. Fundamentals of Parallel Computing
      1. Types of Parallelism
         1. Data Parallelism
         2. Task Parallelism
         3. Pipeline Parallelism
         4. Instruction-Level Parallelism
      2. Flynn's Taxonomy
         1. SISD (Single Instruction, Single Data)
         2. SIMD (Single Instruction, Multiple Data)
         3. MISD (Multiple Instruction, Single Data)
         4. MIMD (Multiple Instruction, Multiple Data)
      3. Performance Laws and Metrics
         1. Amdahl's Law
         2. Gustafson's Law
         3. Speedup and Efficiency
         4. Scalability Analysis
      4. Parallel Programming Challenges
         1. Race Conditions
         2. Deadlocks
         3. Load Balancing
         4. Communication Overhead
   3. CPU vs. GPU Architecture
      1. Central Processing Unit (CPU) Design Philosophy
         1. Latency-Optimized Cores
         2. Large Caches
         3. Complex Control Logic
         4. Branch Prediction and Out-of-Order Execution
         5. Superscalar Architecture
      2. Graphics Processing Unit (GPU) Design Philosophy
         1. Throughput-Optimized Cores
         2. Massively Parallel Structure
         3. Simple Control Logic
         4. SIMD and SIMT Execution Models
         5. High Memory Bandwidth
         6. Many-core Architecture
   4. GPU Hardware Architecture
      1. GPU Evolution and History
         1. Fixed-Function Graphics Pipelines
         2. Programmable Shaders
         3. General-Purpose GPU Computing
      2. Modern GPU Architecture
         1. Streaming Multiprocessors (SMs)
         2. Processing Cores
         3. Warp Schedulers
         4. Load/Store Units
      3. Memory Hierarchy
         1. Global Memory (VRAM)
         2. L1/L2 Caches
         3. Shared Memory
         4. Registers
         5. Constant Memory
         6. Texture Memory
      4. Interconnect and Communication
         1. Memory Controllers
         2. Crossbar Networks
         3. PCIe Interface