How to Compare the Time Cost of Training the Same Model via Different Hardware Architectures

• The article introduces a method to compare the time cost of training the same model via different hardware architectures using FLOPS, which is a measure of the computational performance of a hardware architecture.
• The article applies the method to a real-world example of training ChatGPT, which is a generative AI model that can generate realistic and engaging conversations based on user input, on a CPU and a GPU, and shows the significant difference in the time cost.
• The article also discusses some other metrics, factors, and methods that can affect or reduce the time cost of training generative AI models, such as throughput, efficiency, scalability, data loading, model optimization, parallelization, and acceleration.

Generative AI is a branch of artificial intelligence that can create new content, such as text, images, audio, video, and code, based on existing data. Generative AI models are often trained on large amounts of data using deep neural networks, which require significant computational resources and time. Therefore, choosing the right hardware architecture for training generative AI models is an important decision that can affect the performance, efficiency, and cost of the project.

However, comparing the time cost of training the same model via different hardware architectures is not a straightforward task, as there are many factors that can influence the training speed, such as the model size, the data size, the batch size, the learning rate, the optimization algorithm, the hardware specifications, the software frameworks, and the parallelization strategies. Moreover, different hardware architectures may have different advantages and disadvantages for different types of models and tasks, such as natural language processing, computer vision, or speech synthesis.

In this article, we will introduce a simple and effective method to compare the time cost of training the same model via different hardware architectures, using a common metric called FLOPS (floating-point operations per second). We will also show how to apply this method to a real-world example of training a generative AI model called ChatGPT, which is a chatbot that can generate realistic and engaging conversations based on user input.

What is FLOPS and why is it useful?

FLOPS is a measure of the computational performance of a hardware architecture, which indicates how many floating-point operations (such as addition, subtraction, multiplication, or division) it can perform per second. FLOPS is often used to compare the speed of different hardware architectures for scientific computing, machine learning, and artificial intelligence applications.

FLOPS is useful for comparing the time cost of training the same model via different hardware architectures, because it can capture the main bottleneck of the training process, which is the arithmetic intensity of the model. Arithmetic intensity is the ratio of the number of floating-point operations required by the model to the number of bytes of data that need to be transferred between the memory and the processor. The higher the arithmetic intensity, the more computation-intensive the model is, and the more FLOPS it needs to achieve a fast training speed.

Therefore, by calculating the FLOPS of the model and dividing it by the FLOPS of the hardware architecture, we can estimate the time cost of training the model on that hardware architecture, assuming that other factors are constant. For example, if a model requires 10^15 FLOPS to train, and a hardware architecture can provide 10^12 FLOPS, then the time cost of training the model on that hardware architecture is approximately 10^15 / 10^12 = 1000 seconds.

How to calculate the FLOPS of a model and a hardware architecture?

To calculate the FLOPS of a model, we need to know the number of floating-point operations required by each layer of the model, and the number of times each layer is executed during one iteration of the training process. For example, a fully connected layer with n inputs and m outputs requires n * m floating-point operations for the matrix multiplication, and n + m floating-point operations for the bias addition. If the layer is executed b times during one iteration, where b is the batch size, then the total number of floating-point operations required by the layer is (n * m + n + m) * b. By summing up the number of floating-point operations required by all the layers of the model, we can obtain the FLOPS of the model.

To calculate the FLOPS of a hardware architecture, we need to know the number of cores, the clock frequency, and the number of floating-point operations per cycle of the hardware architecture. For example, a CPU with c cores, f GHz clock frequency, and p floating-point operations per cycle can provide c * f * p * 10^9 FLOPS. Similarly, a GPU with c cores, f GHz clock frequency, and p floating-point operations per cycle can provide c * f * p * 10^9 FLOPS. However, GPUs often have higher values of c, f, and p than CPUs, which makes them more suitable for parallel and computation-intensive tasks, such as training generative AI models.

How to apply the method to a real-world example?

To illustrate how to apply the method to a real-world example, we will use ChatGPT as the model, and compare the time cost of training it on two hardware architectures: a CPU and a GPU. ChatGPT is a generative AI model that can generate realistic and engaging conversations based on user input. It is based on the GPT-2 model, which is a large-scale transformer-based language model with 1.5 billion parameters. ChatGPT is trained on a large corpus of Reddit conversations, and can handle various topics and tones of dialogue.

To calculate the FLOPS of ChatGPT, we need to know the number of floating-point operations required by each layer of the transformer, and the number of times each layer is executed during one iteration of the training process. According to the original paper of GPT-2, each layer of the transformer consists of a multi-head self-attention sublayer and a feed-forward sublayer, and each sublayer is followed by a layer normalization and a residual connection. The multi-head self-attention sublayer has h heads, each with d_k dimensionality, and the feed-forward sublayer has d_ff dimensionality. The input and output dimensionality of each layer is d_model. The number of floating-point operations required by each sublayer can be calculated as follows:

• Multi-head self-attention sublayer: 4 * h * d_k * d_model + 2 * h * d_k^2 + 2 * d_model^2
• Feed-forward sublayer: 2 * d_model * d_ff + 2 * d_ff
• Layer normalization: 4 * d_model
• Residual connection: 2 * d_model

The number of times each layer is executed during one iteration is determined by the batch size b and the sequence length l. Therefore, the total number of floating-point operations required by each layer is:

• Multi-head self-attention sublayer: (4 * h * d_k * d_model + 2 * h * d_k^2 + 2 * d_model^2) * b * l
• Feed-forward sublayer: (2 * d_model * d_ff + 2 * d_ff) * b * l
• Layer normalization: 4 * d_model * b * l
• Residual connection: 2 * d_model * b * l

By summing up the number of floating-point operations required by all the sublayers of all the layers of ChatGPT, we can obtain the FLOPS of ChatGPT. For simplicity, we will use the following values for the parameters:

• Number of layers: 48
• Number of heads: 16
• Input and output dimensionality: 1600
• Head dimensionality: 100
• Feed-forward dimensionality: 6400
• Batch size: 32
• Sequence length: 128

Using these values, we can calculate the FLOPS of ChatGPT as follows:

• Multi-head self-attention sublayer: (4 * 16 * 100 * 1600 + 2 * 16 * 100^2 + 2 * 1600^2) * 32 * 128 = 2.68 * 10^12
• Feed-forward sublayer: (2 * 1600 * 6400 + 2 * 6400) * 32 * 128 = 4.21 * 10^12
• Layer normalization: 4 * 1600 * 32 * 128 = 2.62 * 10^10
• Residual connection: 2 * 1600 * 32 * 128 = 1.31 * 10^10
• Total FLOPS of ChatGPT: (2.68 * 10^12 + 4.21 * 10^12 + 2.62 * 10^10 + 1.31 * 10^10) * 48 = 3.34 * 10^14

To calculate the FLOPS of the CPU and the GPU, we need to know the number of cores, the clock frequency, and the number of floating-point operations per cycle of each hardware architecture. For simplicity, we will use the following values for the parameters:

• CPU: 8 cores, 3 GHz clock frequency, 16 floating-point operations per cycle
• GPU: 2560 cores, 1.5 GHz clock frequency, 64 floating-point operations per cycle

Using these values, we can calculate the FLOPS of the CPU and the GPU as follows:

• CPU: 8 * 3 * 16 * 10^9 = 3.84 * 10^11
• GPU: 2560 * 1.5 * 64 * 10^9 = 2.46 * 10^14

To compare the time cost of training ChatGPT on the CPU and the GPU, we need to divide the FLOPS of ChatGPT by the FLOPS of each hardware architecture. Using the values calculated above, we can estimate the time cost of training ChatGPT on the CPU and the GPU as follows:

• CPU: 3.34 * 10^14 / 3.84 * 10^11 = 870 seconds
• GPU: 3.34 * 10^14 / 2.46 * 10^14 = 1.36 seconds

As we can see, the GPU is much faster than the CPU for training ChatGPT, as it can reduce the time cost by more than 600 times. This shows the importance of choosing the right hardware architecture for training generative AI models, especially for large-scale and computation-intensive models like ChatGPT.

How to compare the time cost of training different models via the same hardware architecture?

The method we introduced above can also be used to compare the time cost of training different models via the same hardware architecture, by calculating the FLOPS of each model and dividing it by the FLOPS of the hardware architecture. For example, if we want to compare the time cost of training ChatGPT and GPT-3 on the same GPU, we can use the following values for the parameters:

• ChatGPT: 1.5 billion parameters, 48 layers, 16 heads, 1600 input and output dimensionality, 100 head dimensionality, 6400 feed-forward dimensionality, 32 batch size, 128 sequence length
• GPT-3: 175 billion parameters, 96 layers, 96 heads, 12288 input and output dimensionality, 128 head dimensionality, 49152 feed-forward dimensionality, 8 batch size, 2048 sequence length
• GPU: 2560 cores, 1.5 GHz clock frequency, 64 floating-point operations per cycle

Using these values, we can calculate the FLOPS of ChatGPT and GPT-3 as follows:

• ChatGPT: (2.68 * 10^12 + 4.21 * 10^12 + 2.62 * 10^10 + 1.31 * 10^10) * 48 = 3.34 * 10^14
• GPT-3: (4 * 96 * 128 * 12288 + 2 * 96 * 128^2 + 2 * 12288^2) * 8 * 2048 + (2 * 12288 * 49152 + 2 * 49152) * 8 * 2048 + 4 * 12288 * 8 * 2048 + 2 * 12288 * 8 * 2048 = 1.15 * 10^18

To compare the time cost of training ChatGPT and GPT-3 on the same GPU, we need to divide the FLOPS of each model by the FLOPS of the GPU. Using the values calculated above, we can estimate the time cost of training ChatGPT and GPT-3 on the same GPU as follows:

• ChatGPT: 3.34 * 10^14 / 2.46 * 10^14 = 1.36 seconds
• GPT-3: 1.15 * 10^18 / 2.46 * 10^14 = 4673 seconds

As we can see, GPT-3 is much slower than ChatGPT for training on the same GPU, as it can increase the time cost by more than 3400 times. This shows the challenge of training very large-scale and computation-intensive models like GPT-3, which require enormous computational resources and time.

FAQs related to the topic

Question: What are some other metrics that can be used to compare the performance of different hardware architectures for training generative AI models?

Answer: Some other metrics that can be used to compare the performance of different hardware architectures for training generative AI models are:

• Throughput: the number of samples that can be processed per unit of time, such as samples per second or samples per hour.
• Efficiency: the ratio of the actual performance to the theoretical peak performance of the hardware architecture, such as FLOPS per watt or FLOPS per dollar.
• Scalability: the ability of the hardware architecture to maintain or increase the performance when the problem size or the number of devices increases, such as strong scaling or weak scaling.

Question: What are some other factors that can affect the time cost of training generative AI models, besides the FLOPS of the model and the hardware architecture?

Answer: Some other factors that can affect the time cost of training generative AI models, besides the FLOPS of the model and the hardware architecture, are:

• Data loading: the time required to load the data from the storage device to the memory or the device, which can depend on the data size, the data format, the data compression, the data augmentation, the data shuffling, the data prefetching, and the data pipeline optimization.
• Model loading: the time required to load the model from the storage device to the memory or the device, which can depend on the model size, the model format, the model compression, the model initialization, the model checkpointing, and the model restoration.
• Model saving: the time required to save the model from the memory or the device to the storage device, which can depend on the model size, the model format, the model compression, the model serialization, the model checkpointing, and the model backup.
• Communication: the time required to transfer the data or the model between different devices, which can depend on the communication protocol, the communication bandwidth, the communication latency, the communication topology, the communication synchronization, and the communication optimization.
• Overhead: the time required to perform other tasks that are not directly related to the computation of the model, such as the initialization, the configuration, the compilation, the profiling, the debugging, the logging, the monitoring, the evaluation, and the visualization.

Question: What are some other methods that can be used to reduce the time cost of training generative AI models, besides choosing the right hardware architecture?

Answer: Some other methods that can be used to reduce the time cost of training generative AI models, besides choosing the right hardware architecture, are:

• Model optimization: the process of modifying the model to improve its performance, such as reducing the model size, simplifying the model architecture, pruning the model parameters, quantizing the model weights, distilling the model knowledge, and sparsifying the model activations.
• Data optimization: the process of modifying the data to improve its quality, such as cleaning the data, filtering the data, balancing the data, augmenting the data, compressing the data, and synthesizing the data.
• Learning optimization: the process of modifying the learning algorithm to improve its efficiency, such as adjusting the learning rate, choosing the optimization method, applying the regularization technique, using the early stopping criterion, and adopting the curriculum learning strategy.
• Parallelization: the process of distributing the computation of the model across multiple devices, such as data parallelism, model parallelism, pipeline parallelism, and hybrid parallelism.
• Acceleration: the process of using specialized hardware or software to speed up the computation of the model, such as tensor processing units (TPUs), neural network accelerators (NNAs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and graph processing units (GPUs).

Summary

In this article, we have introduced a simple and effective method to compare the time cost of training the same model via different hardware architectures, using a common metric called FLOPS. We have also shown how to apply this method to a real-world example of training a generative AI model called ChatGPT, which is a chatbot that can generate realistic and engaging conversations based on user input. We have also discussed some other metrics, factors, and methods that can affect or reduce the time cost of training generative AI models. We hope that this article can help you to choose the right hardware architecture for your generative AI project, and to train your generative AI model faster and better.

Disclaimer: The information and opinions expressed in this article are for educational purposes only, and do not constitute any professional advice or endorsement. The author and the publisher are not responsible for any consequences or damages arising from the use or misuse of the information and opinions in this article. The reader should always consult a qualified expert before making any decision or taking any action related to the topic of this article.