Understanding GPU Programming for Generative AI
Generative AI is coming at a rapid pace now. All the estimate and projections are nowhere close to what we’re witnessing nowadays.
New Day, New LLM
If you followed DeepSeek’s open-source week last week, one interesting pattern you would have observed is that the team is highly focused towards the infrastructure and low level programming and not just training a new LLM with some new data.
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I also assume that in coming times, GPU programming might be a hot topic.
https://medium.com/media/5665489f62eb9a9a82d9b5a60d8d895a/href
And hence, let’s learn GPU Programming using CUDA
This post will serve as an introductory section where we will talk about just basics.
Hope you already know what is a GPU
What is a GPU?
A GPU (Graphics Processing Unit) is a specialized processor designed to handle graphics rendering and parallel computation tasks, such as those used in gaming, video editing, and machine learning. It is optimized for executing many operations simultaneously, thanks to its large number of smaller, simpler cores.
In contrast, a CPU (Central Processing Unit) is a general-purpose processor that handles most of a computer’s tasks, such as running programs, managing system resources, and performing calculations. A CPU typically has fewer, more powerful cores optimized for tasks that require complex, sequential processing.
What is a Core?
GPU Core: A smaller, simpler processing unit designed for parallel execution, handling tasks simultaneously across many cores. Ideal for tasks that can be split into many smaller, identical operations (e.g., graphics rendering).
CPU Core: A more powerful, complex unit optimized for sequential tasks and general-purpose processing. CPUs typically have fewer cores but are capable of handling more intricate and varied computations.
Now as you know what is a GPU,
What is GPU Programming?
GPU Programming refers to the process of writing software that leverages the power of a Graphics Processing Unit (GPU) to perform computation-heavy tasks. As we’ve languages for for CPU based programming (say Python, Java), we also have frameworks for GPU based programming
How is GPU Programming Different from Normal Programming (like Python or Java)?
Parallelism:
- GPU Programming: It is inherently designed to leverage parallelism. GPUs have thousands of small cores that can execute the same instruction on multiple pieces of data at the same time (SIMD — Single Instruction, Multiple Data).
- Normal Programming (Python/Java): Typically runs on CPUs. CPUs have fewer cores (4–64), and traditional programs run sequentially, one instruction at a time (though parallelism can be achieved through multithreading, it is not as efficient or as inherently designed for parallelism as GPUs).
Normal programming is way slower than GPU programming
Memory Architecture:
- GPU Programming: In GPU programming, you need to manage memory explicitly. GPUs have different types of memory (global, shared, constant) that are optimized for different use cases.
- Normal Programming (Python/Java): Memory management is usually handled automatically, especially in high-level languages like Python or Java. The developer doesn’t need to worry about how data is stored in CPU registers.
Memory handling is manual in GPU programming
Execution Model:
- GPU Programming: Involves writing code that runs on thousands of threads simultaneously. These threads are grouped into blocks, which are further grouped into grids. The threads in a block can share data through shared memory, but threads in different blocks can’t easily communicate.
- Normal Programming (Python/Java): Typically, the program runs on a single thread (or a few threads if multithreading is used), and the execution model is sequential.
Parallelism is at the heart of GPU programming
Languages & Frameworks:
GPU Programming: Typically involves using specialized programming languages or libraries that allow interaction with the GPU hardware. Examples of these are:
CUDA (Compute Unified Device Architecture): A parallel computing platform and API model created by NVIDIA for general computing on GPUs.
OpenCL (Open Computing Language): An open standard for parallel programming across CPUs, GPUs, and other processors.
TensorFlow/PyTorch: Frameworks for machine learning that support GPU acceleration.
- Normal Programming (Python/Java): Traditional programming languages like Python or Java are designed to run on the CPU. They do not have built-in support for running code on the GPU, though frameworks like NumPy (using libraries like CuPy) or TensorFlow/PyTorch (for machine learning) allow GPU acceleration.
So, why should I learn GPU programming?
Generative AI is here to stay. And one thing any LLM requires the most is GPUs and lot’s of GPUs.
You need to learn to use GPUs efficiently
Learning GPU programming is crucial for working with Generative AI (GenAI) because of the massive computational demands involved in training and running large AI models. Here’s why:
- Speed and Efficiency: Training Generative AI models, like LLMs or generative image models, requires immense parallel computation. GPUs, with their thousands of cores, can perform the same operation on many data points simultaneously, making them much faster than CPUs. This reduces training time from weeks to days or even hours.
- Scalability: As GenAI models become larger and more complex, the need for parallelism increases. GPUs are designed to handle large-scale data processing, allowing you to scale up models and process large datasets efficiently.
- Optimized Model Inference: Once a GenAI model is trained, running inference (making predictions) requires significant computation. GPUs enable faster model inference, which is essential for real-time applications like conversational AI, image generation, or recommendation systems.
- Cost-effectiveness: Training GenAI models on CPUs can be prohibitively expensive and time-consuming. GPUs, especially when used in cloud environments, offer a more cost-effective solution by drastically reducing the time needed for training and inference.
So, I hope, I was able to motivate you enough to get started with GPU Programming. But
GPU Programming basic concepts
We will start off with understanding some basic units of GPU Programming
1. Kernel:
- A kernel is a function that runs on the GPU. It is a GPU-specific function that you write in a language like CUDA C/C++ or OpenCL, and it is executed in parallel across many threads. Each thread runs the kernel independently, which allows the GPU to perform parallel computation on many data elements simultaneously.
Example: A matrix multiplication operation can be written as a kernel to process each element of the resulting matrix in parallel.
2. Threads:
- A thread is the smallest unit of execution in GPU programming. Each thread executes a kernel independently, processing a small portion of the data. Threads are lightweight and can execute in parallel, enabling GPUs to perform a large number of operations simultaneously.
3. Blocks:
- Threads are grouped into blocks. A block is a collection of threads that can cooperate with each other by sharing data in shared memory and synchronizing their execution.
- Each block runs independently and can be scheduled on different parts of the GPU. Blocks allow the GPU to efficiently manage and organize parallel tasks.
4. Grids:
- A grid is a collection of blocks. Grids organize the blocks into a two- or three-dimensional structure, allowing the GPU to manage large datasets in parallel.
5. Threads Per Block:
- The number of threads in each block is an important design choice. CUDA, for instance, allows you to define how many threads each block should have. A block can contain up to 1024 threads, and this number is often chosen based on the problem and the architecture of the GPU.
6. Shared Memory:
- Shared memory is a special type of memory that is shared by all threads within a block. It is much faster than global memory and can be used to store temporary data that threads in a block need to access frequently.
- Efficient use of shared memory is crucial for performance, as it allows threads in the same block to cooperate and share data more efficiently than using global memory.
7. Global Memory:
- Global memory is the largest and most general form of memory on the GPU. It is accessible by all threads, blocks, and grids, but it has higher latency and lower bandwidth compared to shared memory.
- In GPU programming, it’s important to minimize global memory access whenever possible to avoid performance bottlenecks.
8. CUDA Threads Hierarchy:
- In CUDA programming, threads are organized hierarchically in the following structure:
Grid: The top-level container, which is made of multiple blocks.
Block: A group of threads that execute the same kernel.
Thread: The smallest unit of execution, which processes a piece of data independently.
9. Synchronization:
- Synchronization is the process of ensuring that threads execute in a coordinated manner. Within a block, threads can synchronize using barriers to ensure that all threads reach the same point in execution before proceeding. However, synchronization between threads in different blocks is not straightforward and often requires other mechanisms, like atomic operations.
10. Memory Hierarchy:
Global Memory: Shared across all blocks and threads but has higher latency and lower bandwidth.
Shared Memory: Shared within a block and provides faster access.
Local Memory: Private to each thread but also stored in global memory.
Constant and Texture Memory: Specialized forms of memory for constant values or read-only data that can be cached efficiently.
11. Warps:
- A warp is a group of 32 threads in CUDA (or another architecture-specific size), and they are executed together in a SIMD (Single Instruction, Multiple Data) fashion on the GPU. A warp is the smallest unit of execution that the GPU scheduler works with. Efficient GPU performance often depends on how well your code maps to warp execution.
12. Compute Capability:
- Compute capability refers to the features and specifications of a specific GPU architecture. Different versions of NVIDIA GPUs (such as Kepler, Volta, Ampere) have different compute capabilities, which can affect the availability of certain hardware features like warp size, memory configurations, and CUDA instructions.
Pheww, such a long post
Concluding,
GPU programming is essential for working with Generative AI and computationally intensive tasks. In this post, we’ve covered the basics, from understanding GPUs to key concepts like parallelism and memory management. As AI models grow, mastering GPU programming — especially with CUDA — becomes crucial. Stay tuned for the next post, where we’ll dive deeper into CUDA programming and explore practical techniques!
GPU Programming for beginners was originally published in Data Science in your pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
