9.1 GPU architectures and GPGPU computing

GPUs are powerhouses for parallel processing, packing thousands of simple cores to crunch data simultaneously. Unlike CPUs, which excel at sequential tasks, GPUs shine in graphics rendering and number-crunching for scientific simulations.

GPGPU computing harnesses GPU muscle for non-graphics tasks, supercharging fields like machine learning and data analysis. With specialized programming models like CUDA and OpenCL, developers can tap into GPU power to accelerate complex computations and push the boundaries of AI and scientific research.

CPU vs GPU Architecture

Architectural Differences and Optimization Focus

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• CPUs are designed for general-purpose computing, while GPUs are optimized for parallel processing of graphics and other data-intensive tasks
• CPUs have fewer cores (typically 2-64) with higher clock speeds, while GPUs have hundreds or thousands of simpler cores operating at lower clock speeds
  • Example: A high-end CPU may have 16 cores with a clock speed of 4 GHz, while a GPU may have 4,000 cores with a clock speed of 1.5 GHz
• CPUs have larger caches and more control logic for branch prediction and out-of-order execution, while GPUs have smaller caches and simpler control logic
  • CPUs dedicate more transistors to caches and complex control logic to optimize single-threaded performance
  • GPUs allocate more transistors to arithmetic logic units (ALUs) to maximize parallel processing throughput
• CPUs excel at sequential, latency-sensitive tasks, while GPUs are better suited for throughput-oriented, parallel workloads
  • Example: CPUs are well-suited for running operating systems and interactive applications, while GPUs are ideal for rendering graphics and running scientific simulations

Processor Core Characteristics and Memory Subsystems

• CPUs have more sophisticated cores with features like out-of-order execution, branch prediction, and larger caches to reduce latency and improve single-threaded performance
  • Example: CPUs may have multi-level caches (L1, L2, L3) to minimize memory access latency
• GPUs have simpler cores that are optimized for parallel execution and have smaller caches to save chip area for more ALUs
  • GPUs rely on high memory bandwidth and parallel access patterns to compensate for smaller caches
• CPUs have lower memory bandwidth compared to GPUs but have larger memory capacities and can handle more complex memory hierarchies
  • CPUs typically have memory capacities in the order of tens or hundreds of gigabytes
• GPUs have higher memory bandwidth to feed their numerous cores but have smaller memory capacities compared to CPUs
  • Example: A high-end GPU may have memory bandwidth of 1 TB/s but only 16 GB of memory capacity

Parallel Processing in GPUs

Streaming Multiprocessors and SIMD Units

• GPUs consist of many simple processing cores organized into streaming multiprocessors (SMs) or compute units (CUs), enabling massive parallelism
  • Example: NVIDIA's Ampere architecture has up to 128 SMs, each containing 64 CUDA cores
• Each SM/CU contains multiple SIMD (Single Instruction, Multiple Data) units that execute the same instruction on different data elements simultaneously
  • SIMD units exploit data-level parallelism by performing the same operation on multiple data points in parallel
• GPUs employ a SIMT (Single Instruction, Multiple Thread) execution model, where multiple threads execute the same instruction on different data
  • SIMT allows GPUs to efficiently schedule and execute large numbers of threads in parallel

Memory Access Patterns and Fine-Grained Parallelism

• GPUs have high memory bandwidth and can efficiently access memory in a coalesced manner, minimizing memory latency for parallel workloads
  • Coalesced memory access involves grouping memory transactions from multiple threads into a single transaction to minimize memory latency
• GPUs support fine-grained parallelism through the use of lightweight threads and fast context switching between threads
  • GPUs can quickly switch between threads to hide memory latency and keep the cores busy
  • Example: NVIDIA GPUs use a hardware scheduler to efficiently manage and switch between thousands of threads
• GPUs have specialized memory hierarchies, including shared memory and texture caches, to optimize memory access patterns for parallel workloads
  • Shared memory enables low-latency communication and data sharing between threads within an SM/CU
  • Texture caches optimize memory access patterns for spatial locality, commonly found in graphics and image processing workloads

GPGPU Computing and Applications

GPGPU Concept and Programming Models

• GPGPU (General-Purpose computing on Graphics Processing Units) refers to the use of GPUs for non-graphics computations, leveraging their parallel processing capabilities
  • GPGPU computing harnesses the massive parallelism of GPUs to accelerate computationally intensive tasks
• GPGPU computing enables the acceleration of data-parallel, computationally intensive tasks by offloading them from the CPU to the GPU
  • Example: Matrix multiplication, which involves many independent multiply-accumulate operations, can be efficiently parallelized on GPUs
• GPGPU programming models, such as CUDA (Compute Unified Device Architecture) and OpenCL (Open Computing Language), provide APIs and frameworks for writing parallel code that can be executed on GPUs
  • CUDA is a proprietary programming model developed by NVIDIA for their GPUs
  • OpenCL is an open standard for parallel programming across different devices, including GPUs, CPUs, and FPGAs

Application Domains and Deep Learning Acceleration

• GPGPU computing finds applications in various domains, including scientific simulations, machine learning, data analytics, image and video processing, and cryptography
  • Example: GPUs are used in computational fluid dynamics simulations to parallelize the solving of complex mathematical equations
  • Example: GPUs accelerate the training of deep neural networks by parallelizing matrix operations and gradient calculations
• GPGPU computing has been instrumental in accelerating deep learning workloads, enabling faster training and inference of neural networks
  • GPUs have become the de facto standard for training and deploying deep learning models due to their ability to efficiently perform matrix operations and parallelized computations
  • Example: GPUs have enabled breakthroughs in computer vision, natural language processing, and other AI domains by accelerating the training of large-scale neural networks

Performance of GPU Computing

Performance Benefits and Arithmetic Intensity

• GPU computing can provide significant speedups for data-parallel workloads compared to CPU-only implementations, often achieving orders of magnitude performance improvements
  • Example: GPUs can achieve speedups of 10x to 100x for suitable parallel workloads compared to CPUs
• GPUs excel at tasks that exhibit high arithmetic intensity, meaning they perform a large number of arithmetic operations per memory access
  • High arithmetic intensity allows GPUs to efficiently utilize their ALUs and hide memory latency through parallel execution
  • Example: Matrix multiplication has high arithmetic intensity as it performs many multiply-accumulate operations per memory access

Memory Optimization and Problem Size Considerations

• GPU performance is sensitive to memory access patterns, and optimizing memory coalescing and minimizing data transfers between CPU and GPU memory is crucial for achieving high performance
  • Coalesced memory access patterns enable efficient utilization of GPU memory bandwidth
  • Minimizing data transfers between CPU and GPU memory reduces overhead and improves overall performance
• GPUs have limited memory capacity compared to CPUs, which can constrain the size of problems that can be solved on a single GPU
  • Large problems may need to be partitioned and processed in batches or distributed across multiple GPUs
  • Example: Training deep neural networks with large datasets may require using multiple GPUs or employing memory-efficient techniques like gradient checkpointing

Limitations and Considerations

• Not all algorithms are well-suited for GPU acceleration, and some tasks may have limited parallelism or require frequent branching, which can hinder GPU performance
  • Algorithms with complex control flow or irregular memory access patterns may not benefit significantly from GPU acceleration
  • Example: Algorithms with many conditional statements or those that require frequent synchronization between threads may perform poorly on GPUs
• GPU programming requires specialized knowledge and tools, and porting existing code to GPUs can involve significant development efforts
  • Developers need to be familiar with GPGPU programming models and optimize their code for GPU architectures
  • Legacy code may require substantial modifications to leverage GPU acceleration effectively
• The performance gains of GPU computing may be limited by the overhead of data transfers between CPU and GPU memory, especially for smaller workloads or those with frequent data dependencies
  • Data transfer overhead can dominate the execution time for small workloads, diminishing the benefits of GPU acceleration
  • Algorithms with frequent data dependencies between iterations may require frequent synchronization and data transfers, limiting the achievable speedup on GPUs