What Is GPU Programming?

Graphics Processing Units (GPUs) are increasingly prevalent in modern computing. Scientists and developers are often faced with the challenge of optimizing their programs to leverage the power of these advanced GPU architectures, a crucial step in unlocking their full potential.

This article provides a fundamental understanding of GPU programming, addressing the question, “What is GPU programming?” We will also explore various GPU programming approaches, frameworks, and the significance of CUDA, a popular GPU programming platform, especially within the Python ecosystem.

What Is a GPU?

A GPU is a specialized electronic circuit designed to accelerate the creation of images for display. They achieve this by rapidly manipulating memory and processing data in parallel. GPUs are now commonplace in a wide array of devices, including personal computers, gaming consoles, professional workstations, and smartphones.

GPUs have evolved significantly, surpassing CPUs in performance due to their higher transistor counts and superior parallel computing capabilities. They have become complex devices with their own memory, buses, and processors, essentially acting as a supplemental processor for the computer system.

What Is GPU Programming?

GPU programming involves utilizing GPU accelerators to perform general-purpose computing tasks in parallel. While initially designed for computer graphics, GPUs are now employed for a broad range of applications. Beyond graphics rendering, they excel in areas like scientific modeling, machine learning, and other computationally intensive tasks that benefit from parallel processing.

What Is the Difference Between GPU vs. CPU?

The key difference between GPUs and CPUs lies in their architecture. GPUs are designed for highly parallel processing, making them more efficient than CPUs for data that can be divided and processed concurrently. They are specifically optimized for computationally intensive tasks like matrix and floating-point arithmetic.

This difference in processing power stems from the fact that GPUs are tailored for parallel, compute-intensive workloads, which are precisely what graphics rendering demands.

GPU architecture prioritizes data processing over features like data caching and intricate flow control. This approach offers benefits when tackling problems in parallel: each element faces the same problem, minimizing the need for complex flow control. Furthermore, handling large datasets with high arithmetic intensity reduces the demand for low-latency memory.

What Different Approaches or GPU Programming APIs Are Used?

There are several methods for leveraging GPUs in computations. Depending on the situation, this can range from simply adjusting parameters in existing code to utilizing specialized libraries for the most demanding tasks. More complex scenarios might require custom programming.

GPU programming environments and APIs can be broadly categorized into directive-based models, non-portable kernel-based models, and portable kernel-based models, as well as high-level frameworks and libraries.

Standard C++/Fortran

Standard C++ and Fortran programs can utilize NVIDIA GPUs without the need for external libraries. The NVIDIA SDK compilers optimize the code for execution on the GPU, as outlined in their documentation.

Directive-Based Programming

Directive-based programming involves adding annotations to existing serial code to designate loops and regions for execution on the GPU. This approach, exemplified by OpenACC and OpenMP, focuses on productivity and ease of use, potentially at the expense of optimal performance.

OpenACC: Founded in 2010, OpenACC aims to offer a standard, portable, and scalable programming model for accelerators, including GPUs. While initially focused on NVIDIA GPUs, its support is expanding to encompass a broader range of devices and architectures.

OpenMP: Originally designed as a shared-memory parallel programming API for multi-core CPUs, OpenMP now includes support for GPU offloading, targeting various types of GPUs.

Non-Portable Kernel-Based Models (Native Programming Models)

Direct GPU programming allows for greater control with low-level code interacting directly with the GPU. Examples include CUDA (NVIDIA) and HIP (AMD), with HIP engineered for cross-compatibility between NVIDIA and AMD GPUs.

Portable Kernel-Based Models (Cross-Platform Portability Ecosystems)

Cross-platform portability ecosystems, such as Alpaka, Kokkos, OpenCL, RAJA, and SYCL, provide higher-level abstractions that facilitate convenient and portable GPU programming. They aim to enable performance portability with a single-source application.

Kokkos: Developed at Sandia National Laboratories, Kokkos is a C++-based ecosystem supporting efficient and scalable parallel applications on CPUs, GPUs, and FPGAs.

OpenCL: An open-standard API for general-purpose parallel computing on diverse hardware, OpenCL provides a low-level programming interface suitable for programming GPUs, CPUs, and FPGAs from various vendors.

SYCL: SYCL is a royalty-free, open-standard C++ programming model that enables single-source programming of heterogeneous systems, including GPUs. It offers a high-level abstraction and can be implemented using OpenCL, CUDA, or HIP as backends.

High-Level Language Support

Python offers robust support for GPU programming through several specialized libraries, each catering to different requirements:

CuPy

• A GPU-accelerated data array library.
• Provides seamless integration with NumPy and SciPy.
• Enables easy transition to GPU computing by simply replacing ‘numpy’ and ‘scipy’ with ‘cupy’ and ‘cupyx.scipy’ in existing Python code.

cuDF (RAPIDS)

• Part of the RAPIDS suite, cuDF offers CUDA functionalities with Python bindings.
• Specifically designed for data frame manipulation tasks on the GPU.
• Provides a pandas-like API, allowing users familiar with Pandas to accelerate their workflows without needing extensive CUDA knowledge.

PyCUDA

• A Python environment for CUDA programming.
• Allows direct access to NVIDIA’s CUDA API from Python.
• Limited to NVIDIA GPUs and requires proficiency in CUDA programming.

Numba

• Enables Just-In-Time (JIT) compilation of Python code for GPU execution.
• Currently supports NVIDIA GPUs, with plans to include support for AMD GPUs in the future.

These libraries provide a diverse range of options for programming GPUs in Python, accommodating various user requirements and preferences. Whether transitioning from NumPy/SciPy with CuPy or using cu