💻Parallel and Distributed Computing Unit 12 – GPU Computing and CUDA

GPU Architecture Basics

• GPUs designed for highly parallel processing with thousands of cores optimized for floating-point operations
• Streaming Multiprocessors (SMs) form the main building blocks of GPU architecture, each containing multiple CUDA cores, shared memory, and cache
• SIMT (Single Instruction, Multiple Thread) execution model enables GPUs to efficiently process large amounts of data in parallel
  • Threads grouped into warps (typically 32 threads) execute the same instruction simultaneously on different data
• Memory hierarchy includes global memory (large, high-latency), shared memory (fast, low-latency), and registers (fastest, private to each thread)
• Compute-to-memory ratio higher in GPUs compared to CPUs, emphasizing the importance of optimizing memory access patterns
• Unified memory architecture introduced in newer GPU generations simplifies memory management by providing a single address space for CPU and GPU
• Tensor Cores accelerate matrix multiplication and convolution operations, particularly beneficial for deep learning workloads (NVIDIA Volta, Turing, and Ampere architectures)

Introduction to CUDA

• CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA
• Enables developers to harness the power of NVIDIA GPUs for general-purpose computing (GPGPU)
• CUDA extends C/C++ with additional keywords and constructs to express parallelism and manage GPU resources
• Key concepts in CUDA include:
  • Kernels: Functions executed in parallel on the GPU by multiple threads
  • Threads: Lightweight execution units organized into blocks and grids
  • Blocks: Groups of threads that can cooperate via shared memory and synchronize using barriers
  • Grids: Collections of blocks that execute the same kernel
• CUDA runtime API handles memory allocation, data transfer, and kernel launches, abstracting low-level details
• CUDA libraries (cuBLAS, cuDNN, Thrust) provide optimized implementations of common parallel algorithms and operations
• Interoperability with other APIs (OpenGL, DirectX) allows integration of CUDA with graphics and visualization tasks

CUDA Programming Model

• CUDA follows a heterogeneous programming model, where the CPU (host) and GPU (device) work together
• Serial portions of the code execute on the CPU, while parallel portions (kernels) execute on the GPU
• Kernels are defined using the

  __global__

  keyword and invoked with a specific grid and block configuration
• Threads within a block can communicate through shared memory and synchronize using the

  __syncthreads()

  function
• Global memory accessible to all threads, but accesses are expensive due to high latency
• Shared memory provides fast, low-latency access for threads within the same block
• Registers are private to each thread and offer the fastest memory access
• Memory coalescing improves global memory bandwidth by combining multiple memory accesses into a single transaction
• Streams allow overlapping of data transfers and kernel executions, enabling concurrent execution on the GPU
• Asynchronous memory copies and kernel launches enable the CPU to perform other tasks while the GPU is busy

Memory Hierarchy in GPU Computing

• GPUs employ a multi-level memory hierarchy to balance capacity, bandwidth, and latency
• Global memory:
  • Largest memory space on the GPU, accessible by all threads
  • High latency (hundreds of clock cycles) due to off-chip location
  • Optimizing global memory accesses (coalescing, caching) is crucial for performance
• Shared memory:
  • On-chip memory shared by threads within a block
  • Low latency (few clock cycles) and high bandwidth
  • Enables communication and data sharing among threads in a block
  • Careful management (avoiding bank conflicts) is necessary for optimal performance
• Registers:
  • Fastest memory on the GPU, private to each thread
  • Automatically managed by the compiler, used for storing local variables
  • Limited in number, excessive register usage can lead to register spilling and performance degradation
• Constant memory:
  • Read-only memory space cached for efficient access by the GPU
  • Suitable for storing constants and lookup tables that do not change during kernel execution
• Texture memory:
  • Specialized memory space optimized for 2D spatial locality
  • Provides caching and hardware interpolation for efficient access to texture data
  • Useful for image processing, computer vision, and graphics applications

Parallel Algorithms for GPUs

• GPUs excel at data-parallel algorithms, where the same operation is applied to multiple data elements simultaneously
• Parallel reduction:
  • Computes a single value from an array of elements (sum, maximum, minimum)
  • Employs a tree-based approach, gradually reducing the number of active threads until the final result is obtained
• Parallel prefix sum (scan):
  • Calculates the cumulative sum of elements in an array
  • Efficient implementations utilize shared memory and multiple kernel invocations
• Parallel sorting:
  • Radix sort and merge sort are well-suited for GPU parallelization
  • Radix sort sorts elements based on their binary representation, while merge sort recursively divides and merges sorted sub-arrays
• Parallel matrix multiplication:
  • Decomposes the matrix multiplication into smaller sub-problems that can be computed independently
  • Utilizes shared memory to cache frequently accessed elements and reduce global memory traffic
• Parallel graph algorithms:
  • Breadth-First Search (BFS) and Single-Source Shortest Path (SSSP) can be parallelized on GPUs
  • Efficient implementations leverage the massive parallelism and high memory bandwidth of GPUs
• Parallel numerical algorithms:
  • Solving systems of linear equations, eigenvalue problems, and numerical integration can be accelerated on GPUs
  • Libraries like cuBLAS and cuSPARSE provide optimized routines for linear algebra operations

Performance Optimization Techniques

• Maximizing GPU utilization and minimizing data transfer overhead are key to achieving high performance
• Occupancy optimization:
  • Ensure sufficient number of active warps per SM to hide memory latency
  • Adjust block size and shared memory usage to find the optimal balance
• Memory access optimization:
  • Coalesce global memory accesses to maximize bandwidth utilization
  • Use shared memory to cache frequently accessed data and reduce global memory traffic
  • Minimize bank conflicts in shared memory to avoid serialization of accesses
• Instruction-level parallelism:
  • Interleave independent instructions to hide latencies and improve instruction throughput
  • Avoid branch divergence within warps to maintain SIMT efficiency
• Data layout and structure:
  • Organize data in memory to facilitate coalesced accesses and minimize strided or irregular access patterns
  • Use structures of arrays (SoA) instead of arrays of structures (AoS) for better memory coalescing
• Kernel launch configuration:
  • Choose appropriate grid and block dimensions based on problem size and GPU capabilities
  • Experiment with different configurations to find the optimal balance between parallelism and resource utilization
• Overlapping data transfer and computation:
  • Utilize CUDA streams and asynchronous memory copies to overlap data transfer with kernel execution
  • Enables the GPU to perform computations while data is being transferred, hiding transfer latency
• Profiling and performance analysis:
  • Use CUDA profiling tools (NVIDIA Visual Profiler, nvprof) to identify performance bottlenecks and optimize accordingly
  • Analyze metrics such as occupancy, memory throughput, and instruction efficiency to guide optimization efforts

Advanced CUDA Features

• Dynamic Parallelism:
  • Enables kernels to launch new kernels directly from the GPU, without CPU intervention
  • Facilitates the creation of recursive and adaptive algorithms that can generate work dynamically
• Unified Memory:
  • Provides a single address space for CPU and GPU memory, simplifying memory management
  • Automatically handles data movement between CPU and GPU, reducing the need for explicit memory copies
  • Enables easier porting of existing CPU code to GPUs and facilitates incremental optimization
• CUDA Graphs:
  • Allows the creation of a graph of CUDA operations (kernel launches, memory copies) that can be executed efficiently
  • Reduces CPU overhead and enables optimizations like kernel fusion and concurrent execution
• CUDA-Aware MPI:
  • Enables direct communication between GPUs across multiple nodes in a cluster using MPI (Message Passing Interface)
  • Eliminates the need for explicit data movement between GPU and CPU memory during MPI communication
• CUDA IPC (Inter-Process Communication):
  • Allows sharing of GPU memory and resources between different processes on the same system
  • Enables efficient communication and collaboration between multiple GPU-accelerated applications
• CUDA Streams and Events:
  • Streams provide a mechanism for concurrent execution of kernels and memory operations on the GPU
  • Events allow synchronization and coordination between different streams and the CPU
  • Enable fine-grained control over the execution order and dependencies of CUDA operations

Real-world Applications and Case Studies

• Deep Learning and Artificial Intelligence:
  • GPUs have revolutionized the field of deep learning, enabling the training of large neural networks in reasonable timeframes
  • Frameworks like TensorFlow, PyTorch, and Caffe leverage CUDA to accelerate training and inference on NVIDIA GPUs
• Scientific Simulations:
  • GPUs are widely used in scientific computing for simulating complex physical phenomena (fluid dynamics, molecular dynamics)
  • CUDA enables the parallelization of computationally intensive algorithms, reducing simulation times from days to hours
• Medical Imaging and Analysis:
  • GPUs accelerate medical image processing tasks (segmentation, registration, reconstruction)
  • Real-time image analysis during surgical procedures and faster diagnosis in medical research benefit from GPU acceleration
• Computational Finance:
  • GPUs accelerate financial simulations, risk analysis, and option pricing models
  • CUDA enables faster execution of Monte Carlo simulations and other computationally intensive financial algorithms
• Computer Vision and Image Processing:
  • GPUs are well-suited for image and video processing tasks (filtering, feature extraction, object detection)
  • CUDA accelerates computer vision algorithms, enabling real-time processing in applications like autonomous vehicles and surveillance systems
• Blockchain and Cryptocurrency:
  • GPUs are used for mining cryptocurrencies like Bitcoin and Ethereum, leveraging their parallel processing capabilities
  • CUDA enables the efficient implementation of cryptographic hash functions and consensus algorithms used in blockchain technologies