Choosing the Right Hardware Accelerator: CPU vs GPU vs TPU

Choosing the Right Hardware Accelerator: CPU vs GPU vs TPU

Table of Contents:

I. Introduction II. CPU A. General Purpose Processor B. Flexibility C. Limitations D. Use Cases III. GPU A. Thousands of ALUs B. General Purpose Processor C. Limitations D. Use Cases IV. TPU A. Matrix Processor B. Multiply and Accumulate Operations C. Systolic Array Architecture D. High Computational Throughput E. Use Cases V. Pros and Cons VI. Conclusion VII. FAQ

Article:

Introduction: Machine learning tasks require the use of specialized hardware to ensure maximum performance and efficiency. The most commonly used hardware accelerators are Central Processing Units (CPU), Graphics Processing Units (GPU), and Tensor Processing Units (TPU). In this article, we will explore the functionalities of each hardware accelerator, their advantages, limitations, and suitable use-cases.

CPU: A CPU is a general-purpose processor based on the Von Newman architecture used in every computer. The CPU functions with great flexibility, making it compatible with a variety of applications. However, when dealing with complex mathematical calculations, such as those required in neural networks, the hardware of a CPU struggles to keep up with the speed and memory requirements. The CPU needs to read instructions, execute them one by one, and access memory every single time, leading to low throughput.

Although the CPU struggles with complex mathematical calculations, it is still suitable for quick prototyping that requires maximum flexibility or simple models that do not take long to train.

GPU: A GPU uses thousands of Arithmetic Logic Units (ALUs) to execute huge computations, such as matrix multiplication, in parallel. GPUs suffer from the same limitations as CPUs, as they are still general-purpose processors that require reading instructions and accessing memory for every set of calculations, increasing complexity.

However, GPUs are still very good for models where the source does not exist or is too onerous to change. If You have models with a significant number of custom TensorFlow operations that must run at least partially on CPUs or models with TensorFlow operations that are not available on Cloud TPU and medium to large models with larger effective batch sizes, a GPU may be the way to go.

TPU: Google designed Cloud TPUs as a matrix processor specialized for neural network workloads. TPUs can handle massive Matrix operations used in neural networks at incredibly fast speeds. TPUs contain thousands of multiply-accumulators that are directly connected to each other to form a large physical matrix, known as a systolic array architecture. During the matrix multiplication process, no memory access is required, resulting in high computational throughput on neural network calculations.

TPUs are ideal for models that are dominated by matrix computations or have no custom TensorFlow or PyTorch or Jack's operations inside the main training loop. Additionally, TPUs are suitable for models that train for weeks or months at a time.

Pros and Cons: CPU: Pros:

• Flexible
• Compatible with a variety of applications
• Suitable for quick prototyping
• Great for simple models

Cons:

• Slow for complex mathematical calculations
• Low throughput

GPU: Pros:

• Thousands of ALUs for Parallel computation
• Suitable for models that require a significant number of custom TensorFlow operations
• Suitable for medium to large models with larger effective batch sizes

Cons:

• General-purpose processor
• Limited computational throughput due to memory access and reading instruction

TPU: Pros:

• Specialized matrix processor
• High computational throughput
• Suitable for models dominated by matrix computations
• Ideal for long-term training

Cons:

• Limited to matrix operations
• Expensive

Conclusion: In conclusion, each hardware accelerator has its strengths and limitations. CPUs are flexible and compatible with various applications, while GPUs offer parallel computation and are suitable for models that require a significant number of custom TensorFlow operations. TPUs are specialized matrix processors that offer high computational throughput and are suitable for models dominated by matrix computations or long-term training.

FAQ:

Q: Which hardware accelerator is the most suitable for quick prototyping? A: CPUs are the most suitable for quick prototyping.

Q: Which hardware accelerator is the most suitable for models that require a significant number of custom TensorFlow operations? A: GPUs are the most suitable for models that require a significant number of custom TensorFlow operations.

Q: Which hardware accelerator is the most suitable for long-term training? A: TPUs are the most suitable for long-term training.