A Data Scientist’s Guide to GPU Part 1

6 min readOct 15, 2024

Introduction

As a child, I always dreamed of owning a powerful graphics card to enhance my gaming experience. Little did I know that this same hardware would become indispensable for my career as a data scientist, particularly in the realm of deep learning (DL) and large language models (LLMs).

This article aims to provide a concise overview of GPUs, their role in accelerating DL and LLM development, and key considerations for data scientists selecting the right GPU for their needs.

In recent weeks, I’ve made significant strides in optimizing my GPU training practices. Previously, I relied heavily on just the to_device('cuda') function in PyTorch (it works well in most cases), but I've since discovered more efficient ways to utilize GPU resources. These insights inspired me to write this article. I’m committed to deepening my understanding of GPU computing and fine-tuning/pre-training LLMs. Join me on this journey by following my future content.

1. Definition:

A Graphics Processing Unit (GPU) is a specialized piece of hardware originally designed for rendering graphics, primarily in video games. However, its parallel processing capabilities have made it invaluable for data scientists, particularly in the field of deep learning.

Parallelism: GPUs possess thousands of smaller cores, each capable of performing simple calculations simultaneously. This is ideal for the matrix operations and neural network computations common in machine learning.
Synchronization: GPUs employ efficient synchronization mechanisms to ensure data consistency and prevent race conditions in parallel computations.

2. Nvidia’s Compute Unified Device Architecture (CUDA)

CUDA is a parallel computing platform and API model created by NVIDIA. It allows developers to write C++ code that can be executed on NVIDIA GPUs, harnessing their parallel processing power.

Key Features:

Parallel Processing: CUDA enables the simultaneous execution of thousands of threads on a GPU, significantly accelerating computationally intensive tasks.
Kernel Functions: These functions are the building blocks of CUDA programs. They are executed on the GPU and can operate on large arrays of data in parallel.
Memory Management: CUDA provides mechanisms for managing GPU memory, including allocating, copying, and freeing memory.
Interoperability: CUDA can be used with popular programming languages like C++, Python, and Java.

3. Nvidia’s cuDNN

CUDA Deep Neural Network Library: cuDNN is a GPU-accelerated library optimized for deep neural network training and inference. It provides highly optimized implementations of common deep learning primitives, such as convolutional layers, recurrent layers, and pooling layers.

Key Benefits:

Performance: cuDNN offers significant performance improvements over CPU-based implementations, especially for large-scale deep learning models.
Ease of Use: It provides a high-level API that abstracts away the complexities of GPU programming, making it easier for developers to build and train deep learning models.
Compatibility: cuDNN is compatible with popular deep learning frameworks like TensorFlow, PyTorch, and Caffe.

4. How CUDA and cuDNN Work Together

CUDA provides the foundation: It enables the execution of code on the GPU and manages memory allocation and transfer.
cuDNN leverages CUDA: It uses CUDA to efficiently perform deep learning operations on the GPU.
Deep Learning Frameworks: Popular frameworks like TensorFlow and PyTorch utilize cuDNN to accelerate their operations, making it possible to train large-scale deep learning models in a reasonable amount of time.

5. How GPU and Transformer Architecture accelerated LLM development :

LSTM vs Transformers: Transformers and Long Short-Term Memory (LSTM) networks are both popular architectures for sequential data processing, particularly in natural language processing. While both have their strengths, Transformers offer a significant advantage in terms of parallelism, which can lead to faster training and inference times. The core computations in attention layers involve matrix multiplications, which are highly parallelizable operations. Modern GPUs and TPUs are optimized for these matrix operations. It’s this Transformer architecture that is at the core of most recent LLMs like LaMA/GPT.

The transformer’s N-heads work in parallel on a GPU and then the results are merged/synchorized. The diagram below shows working of the n-heads. This is opposite to how LSTM process data in sequence.

Image from https://towardsdatascience.com/transformers-explained-visually-part-3-multi-head-attention-deep-dive-1c1ff1024853

GPU Implementation of Transformers

Head-Level Parallelism: Each attention head can be processed independently, allowing for parallel execution.
Matrix Multiplication Optimization: GPUs are optimized for matrix operations, making them well-suited for the computations involved in attention layers.

6. Key Considerations for GPU Selection

When choosing a GPU for deep learning and LLMs, consider the following factors:

Budget: GPUs can vary significantly in price. Determine your budget before starting your search.
Model Complexity: The size and complexity of your models will influence the required GPU performance.
Data Volume: The amount of data you’ll be training on will also impact GPU requirements.
Framework Compatibility: Ensure your preferred deep learning framework (e.g., TensorFlow, PyTorch) is compatible with the GPU.

Tip for beginners:

Google Colab offers a convenient and free introduction to GPU computing, but its free tier often has short runtimes (typically 2–3 hours). Additionally, sessions can disconnect unexpectedly, even during training loops or when loading larger models like 8-bit Quantized Laama2 for inference.

7. Recommendation for GPUs:

While specific recommendations can change over time, here are some popular choices for deep learning as of now :

NVIDIA GeForce RTX Series: Known for their excellent performance in both gaming and deep learning, especially those with higher-end models like the RTX 3080 / 4090.
NVIDIA Tesla Series: Specifically designed for data centers and scientific computing, offering exceptional performance and reliability for demanding deep learning workloads.
Cloud-Based Solutions: If you have limited hardware resources, consider cloud-based platforms like Google Colab (Free or Learn more about paid version here), Amazon SageMaker, or Microsoft Azure Machine Learning, which provide GPU-accelerated instances.
Used GPUs: You might find good deals on used GPUs, but be mindful of their condition and potential performance degradation.
Future-Proofing: If you anticipate scaling your deep learning projects, invest in a GPU with sufficient headroom to handle future demands.

8. Best Practices for Training Deep Learning Models with GPUs:

1. Utilize Mixed Precision Training: FP16/BF16: Employ mixed-precision training (FP16 or BF16) to reduce computational costs without sacrificing accuracy. This is especially beneficial for large models.

2. Optimize Data Loading and Preprocessing: GPU-Based Preprocessing: Perform data preprocessing operations directly on the GPU to minimize data transfer overhead. Use asynchronous data loading and preprocessing to overlap these tasks with model training. PyTorch makes it easy to leverage GPU acceleration. You can quickly transfer data loaders and models to the GPU using to_device('cuda') if you have an NVIDIA GPU.

3. Select Appropriate Batch Sizes: Experiment with different batch sizes to find the optimal balance between training speed and memory usage. Larger batch sizes can often improve performance, but excessive sizes may lead to memory constraints.

4. Monitor GPU Utilization and Memory Usage: nvidia-smi provides valuable information about GPU performance by tracking utilization and memory usage. While not always a definitive measure, these metrics can often help identify performance issues.

5. Consider Distributed Training with Multiple GPUs: For extremely large models or datasets, consider distributed training across multiple GPUs or even multiple machines to accelerate training. Pytorch lighting makes it easier with Data Parallelism in PyTorch Lightning (DDP): https://lightning.ai/docs/pytorch/stable/accelerators/gpu_intermediate.html .

6. Allocate Memory Efficiently: Allocate GPU memory as needed and release it when no longer required.

If there’s interest, I’ll provide more details on different GPU options and architectures you can choose from for local or cloud-based GPU computing.(as mentioned briefily in the Cloud-Based Solutions)

Thank you for reading, that’s all for this article. More content to follow. Please clap if the article was helpful to you and comment if you have any questions. If you want to connect with me, learn and grow with me or collaborate you can reach me at any of the following:

Linkedin:- https://www.linkedin.com/in/virajdatt-kohir/
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