L3 GPUs

Graphics Processing Units (GPUs) were originally designed to handle graphics rendering, but they are now used in many areas beyond gaming, including machine learning, simulations, and scientific computing. Unlike Central Processing Units (CPUs), which are optimized for complex tasks, GPUs excel at processing many simple tasks in parallel.

Understanding GPUs is important because they power everything from realistic video game graphics to AI-driven facial recognition and cryptocurrency mining.

Learning Objectives

By the end of this lesson, I can:

Define GPUs and their primary function.
Compare GPUs and CPUs.
Explain real-world applications of GPUs beyond graphics rendering.

Key Terms

GPU (Graphics Processing Unit) 🎮 – A specialized processor designed for parallel computation, primarily for rendering graphics but now used in many other applications.
Parallel Processing ⚡ – A technique where multiple calculations are performed simultaneously, dividing a task into smaller sub-tasks.
SIMD (Single Instruction Multiple Data) 🏭 – A processing technique where the same operation is applied to multiple pieces of data at once, enhancing performance in tasks like image processing.
GPGPU (General-Purpose Computing on GPUs) 🔍 – The use of GPUs for tasks beyond graphics rendering, such as scientific simulations, AI, and machine learning.
CUDA Cores ⚙️ – Processing units within a GPU designed for executing parallel operations in tasks like deep learning and scientific computations.
FLOPS (Floating Point Operations Per Second) 💻 – A measure of a GPU’s performance, indicating how many floating point operations it can perform per second.
Memory Bandwidth 📊 – The rate at which data can be transferred between the GPU’s memory and its processing cores, essential for high-performance tasks.
Task Parallelism 🛠️ – A type of parallel processing where different tasks are executed simultaneously, often used in complex simulations.
Data Parallelism 📐 – A type of parallel processing where the same operation is applied to multiple pieces of data at the same time, commonly used in tasks like matrix multiplication.
OpenCL 🔗 – An open standard for parallel programming that allows code to run on different platforms and hardware, enabling the use of GPUs for general-purpose computing.
Tensor Processing Units (TPUs) ⚡ – Specialized accelerators designed to handle machine learning tasks more efficiently than traditional GPUs.
Quantum Computing 🧬 – An emerging field that leverages quantum mechanics to perform computations, with GPUs playing a role in simulating quantum systems.

GPUs (Graphics Processing Units)

While CPUs handle complex sequential tasks, GPUs (Graphics Processing Units) are optimized for highly parallel tasks. Originally designed for rendering 3D graphics, GPUs now play a crucial role in machine learning, AI, scientific simulations, and cryptocurrency mining. Unlike CPUs, which have a few powerful cores designed for sequential processing, GPUs have thousands of smaller cores that can execute the same operation on multiple pieces of data simultaneously. This parallel processing capability is known as SIMD (Single Instruction, Multiple Data).

GPU Architecture and Design

The architecture of a GPU is significantly different from that of a CPU. GPUs consist of many smaller cores that work in parallel to process large volumes of data quickly. These cores are typically less powerful individually than CPU cores, but their parallelism allows them to outperform CPUs in tasks that can be parallelized. GPUs also have a specialized memory hierarchy to efficiently handle high-throughput data.

CUDA Cores: These are the processing units within a GPU, designed for executing parallel operations.
Memory: GPUs often have high-bandwidth memory (e.g., GDDR), which allows them to move data faster, an essential feature for handling large datasets.

Parallel Processing in GPUs

GPUs excel in tasks that can be divided into smaller, independent operations. The SIMD model allows the same operation to be applied to multiple data points simultaneously. For example, in matrix multiplication, each element of the result matrix can be computed independently, making it a perfect task for a GPU.

Parallelism in GPUs can be categorized into:

Data Parallelism: The same operation is applied to multiple pieces of data (e.g., in image processing).
Task Parallelism: Different tasks are executed simultaneously (e.g., different parts of a simulation running at the same time).

Performance Metrics

GPUs are measured by their ability to perform FLOPS (Floating Point Operations Per Second), which indicates the number of operations the GPU can execute in a second. This metric is crucial in fields like machine learning, where large-scale computations are needed. Memory bandwidth is also a key factor, as it determines how quickly data can be transferred between the GPU's memory and its processing cores.

GPGPU (General-Purpose Computing on GPUs)

While GPUs were initially designed for rendering graphics, they are now used for General-Purpose Computing on GPUs (GPGPU). This allows them to accelerate tasks outside of graphics, such as scientific simulations, AI, and machine learning. Frameworks like CUDA (Compute Unified Device Architecture) and OpenCL enable programmers to leverage GPU power for non-graphical tasks.

CUDA: A programming model developed by NVIDIA that allows developers to write programs that can run on GPUs for general-purpose computation.
OpenCL: An open standard for parallel programming that works across different platforms and hardware.

Applications and Use Cases

GPUs have revolutionized various fields by accelerating large-scale computations:

Deep Learning: GPUs are essential in training neural networks, where large datasets and complex computations require massive parallelism.
Weather Forecasting: GPUs simulate climate models faster than CPUs, improving prediction accuracy and speed.
Medical Imaging: GPUs accelerate image processing in applications like MRI and CT scans, enabling faster diagnostics.
Cryptocurrency Mining: GPUs are used in mining cryptocurrencies like Bitcoin, as they can perform the necessary calculations much faster than CPUs.

Additionally, GPUs are used in video rendering, physics simulations, and blockchain technology, where rapid computations are essential.

Challenges and Limitations

Despite their power, GPUs have limitations:

Not suitable for all tasks: GPUs are most effective for parallel tasks but are less efficient for tasks that require sequential processing, which CPUs handle better.
Energy consumption: GPUs can consume significant amounts of power, which is a concern for large-scale applications.
Heat management: The high number of cores in a GPU generates substantial heat, requiring effective cooling solutions.

Future Trends

The future of GPU technology looks promising:

AI and Self-Driving Cars: GPUs continue to play a key role in the development of AI and self-driving car technologies, processing vast amounts of data in real time.
Tensor Processing Units (TPUs): These are specialized accelerators designed for machine learning tasks, complementing GPUs in AI applications.
Quantum Computing: Researchers are exploring the use of GPUs in quantum computing, particularly for simulating quantum systems.

CPU vs GPU

While GPUs are not a replacement for CPUs, they work alongside CPUs to optimize performance in high-performance computing tasks. CPUs are designed for general-purpose tasks and handle the "thinking" of a computer, whereas GPUs excel in processing parallel tasks. Together, they form a complementary system that boosts computational efficiency in fields requiring both general-purpose and parallel processing capabilities.

CPUs vs GPUs: What's the difference?

Summary: CPUs handle complex, varied tasks but are slower at massively parallel workloads, while GPUs excel at repetitive calculations by processing thousands of data points at once.

Applications of GPUs

GPUs are used in a variety of fields, including:

Gaming 🎮 – Rendering realistic 3D environments.
Machine Learning 🤖 – Training AI models by processing vast datasets.
Cryptocurrency Mining ⛏️ – Solving complex mathematical problems to validate blockchain transactions.
Medical Imaging: GPUs power MRI and CT scan processing, improving image clarity and speed.
Weather Forecasting: Supercomputers with GPU acceleration simulate climate models at a massive scale.
Scientific Computing: GPUs are used in genome sequencing, physics simulations, and AI research.

Parallel Processing (SIMD vs. MIMD)

GPUs are designed for highly parallel tasks using SIMD (Single Instruction, Multiple Data) processing, meaning:

The same operation is performed on multiple pieces of data simultaneously.
This is ideal for tasks like rendering graphics, deep learning, and physics simulations.
CPUs, in contrast, use MIMD (Multiple Instruction, Multiple Data), allowing more flexibility but less efficiency for massive, repeated calculations.

Tips & Mistakes to Avoid

✅ Tip: GPUs are not a replacement for CPUs; they work together to optimize performance.

🚫 Mistake: Assuming that GPUs are only for gaming—they are widely used in AI, finance, and scientific research.

Video: GPUs and their uses.

Explains the architecture of GPUs and their applications.
Demonstrates how GPUs handle parallel processing.

Remember to add to your workbook notes when the blue icon appears in the video.

Case Study

GPU-Accelerated AI in Self-Driving Cars 🚗🤖

Self-driving cars rely on real-time image processing and deep learning models to navigate roads safely. Tesla, Waymo, and other autonomous vehicle companies use GPUs to power their AI-driven perception systems. These vehicles are equipped with multiple cameras, LiDAR sensors, and radar, generating massive amounts of data every second. GPUs process millions of images and sensor inputs in parallel, identifying objects like pedestrians, traffic lights, and road signs in real time.

Unlike CPUs, which would process these images sequentially, GPUs enable instant decision-making, allowing the vehicle to react to its environment within milliseconds. Without high-performance GPU acceleration, self-driving technology would be too slow and inefficient to operate safely on roads.

Companies like Tesla, Waymo, and NVIDIA use GPUs to process real-time sensor data in autonomous vehicles.

A self-driving car has multiple sensors (cameras, LiDAR, radar) collecting data simultaneously.
A CPU would struggle to process all this data at once because it handles fewer threads.
A GPU, with thousands of cores, can analyze images, detect objects, and make driving decisions all in parallel, allowing real-time reaction.

Lesson Flashcards

RED TASKS

Use your G-Slides workbook to complete the red tasks for this lesson. Everything you need is on the lesson webpage—only do online research if specifically told to.

Title Slide
1. Add a new section titled "GPUs and Their Uses".
2. Choose an image or icon representing GPUs or parallel processing.
Key Terms & Definitions
1. Create a slide defining:
  - GPU (Graphics Processing Unit)
  - Parallel Processing
  - SIMD (Single Instruction Multiple Data)
  - GPGPU (General-Purpose Computing on GPUs)
2. Keep definitions clear and detailed for revision.
Comparison Table: CPUs vs. GPUs
1. Make a table comparing CPUs and GPUs.
2. Include at least six key differences, such as core count, task type, speed, and best use cases.
How GPUs Process Data
1. Add a brief explanation of SIMD and why GPUs are faster for certain tasks.

ACTIVE LEARNING

Case Study: GPU-Accelerated AI in Self-Driving Cars - complete this in pairs.

Explain how Tesla and other companies use GPUs to process real-time data from sensors and cameras.
Describe how millions of images are processed in parallel to detect objects and make driving decisions.
Explain why CPUs alone would be too slow for real-time decision-making.

Exam-Style Questions
- Answer these two questions to your slides:
  - Explain two advantages of using a GPU over a CPU for parallel processing. (4 marks)
  - Describe how SIMD allows GPUs to process large amounts of data efficiently. (6 marks)
Real-World Reflection
- Summarize the different fields where GPUs are used outside of gaming.
- Answer this question in a full, structured response:
  - If you were designing a supercomputer for AI research, what type of processor (CPU, GPU, or both) would you choose? Justify your decision based on performance, efficiency, and cost.

EXTENSION TASKS

Pair Activity: GPU vs. CPU Speed Test Simulation

Each pair gets a list of simple calculations (e.g., multiplying numbers by 2). In Round 1, they complete the task one step at a time (CPU). In Round 2, one student calls out instructions while the other processes all data at once (GPU). Compare results and discuss why GPUs are better at parallel tasks.

Small Group Activity: Industry Use Case Challenge

Each group researches a real-world application of GPUs outside of gaming (e.g., AI, weather modeling, cryptocurrency). Groups present findings in 2 minutes, explaining why GPUs are better than CPUs for the task.

Large Group / Whole Class Activity: Live Data Processing Race

Each student represents a processing core. The instructor calls out a massive dataset problem (e.g., counting occurrences of a letter in text). In Round 1 (CPU Mode), one person does it all. In Round 2 (GPU Mode), each student counts a different part simultaneously. Compare speeds and discuss why parallel processing is crucial for large-scale computing.

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