In this article, we are going to look at the best GPU for deep learning in 2022.
We will also be covering the most important factors that you should consider when buying a GPU for your deep learning project.
What is Deep Learning?
Deep learning is an area of machine learning which involves training neural networks to perform specific tasks. The term “deep” refers to the number of layers in the network, and the more layers there are, the better the results.
The first major breakthroughs in this field were made by Geoffrey Hinton, who developed what he called the neural network back in the 1990s.
The basic idea behind neural networks is to use computers to mimic how our brains work. In other words, they learn from experience.
Neural networks can be used to solve problems such as image recognition, speech recognition, natural language processing, and even playing video games.
How do GPU work?
GPUs are great devices for doing a lot of things quickly. They excel at running many calculations simultaneously.
They are used heavily in machine learning because of the way they perform matrix operations.
GPU servers are great because they allow us to perform calculations much faster than we could ever hope to on our CPU alone. They make it possible to train neural networks — models that mimic human brain function — very quickly. Because of this, there is a lot of interest in training such models on GPUs.
A GPU consists of many small processors called cores. Each core performs one specific task. For example, a single core might handle floating-point arithmetic while another handles integer operations. There are multiple cores per chip, and each core runs independently of the others.
Each core has access to memory that stores data and instructions. Memory is divided into registers and cache. Registers store temporary values used during computation; cache holds recently accessed data. A typical modern GPU contains hundreds of millions of registers and cache.
The basic operation of a GPU is simple: it reads data from memory, processes it, and writes the results back to memory. But this process doesn’t always happen sequentially. Instead, the processor executes instructions out of order. This allows the computer to take advantage of parallelism, where different parts of the program execute simultaneously.
When a programmer uses CUDA, the programming interface offered by NVIDIA, the developer must tell the compiler how to map the code to the architecture. This mapping tells the compiler what data goes where. When the compiler generates machine language for the GPU, it does this mapping automatically.
This is why most programmers don’t need to worry about the specifics of how a GPU works. They simply write the code and let the compiler figure things out.
Tensor Cores
The Tensor Core is a technology developed by Intel designed to speed up deep learning training. Deep learning refers to neural networks that learn to recognize patterns in large amounts of data. This type of network is useful for tasks like image recognition or speech processing.
Deep learning is becoming increasingly important because it allows computers to perform some tasks better than humans. For instance, self-driving cars use deep learning to understand traffic signs and recognize pedestrians. In fact, there are already deep learning models that can identify objects in images.
Intel announced the Tensor Core at the International Solid-State Circuits Conference (ISSCC). A few days later, Nvidia followed suit with its Volta architecture. Both companies claim that their chips offer the best performance per watt ratio.
Matrix multiplication without Tensor Cores
In this article I describe how to implement matrix multiplication without Tensor cores. This technique uses a combination of SIMD instructions and data prefetching to accelerate matrix multiplications.
The main idea behind this approach is to use multiple threads to simultaneously read from different parts of the shared memory array, while keeping the computation local to one thread. This allows us to avoid the overhead associated with loading data from global memory. In addition, it reduces the number of cache misses since the data is already present in the L1 cache.
We start off by defining our matrix sizes. Here we define matrices A and B as having dimensions. We also define vectors X and Y as being of dimension and Z as being of dimension.
Next we declare some variables. First, we declare a variable called mem_tile_size, which represents the size of a shared memory tile. Second, we declare a variable named num_threads, which represents the number of threads used in the algorithm. Finally, we declare a struct named tile_info, which contains information about the current tile.
Now we define a function called mmul() that performs the matrix multiplication. The function starts off by initializing all tiles to zero. Next, we loop over the entire set of tiles, reading from one tile and writing to another. For each tile, we compute the dot product of the corresponding elements of the vectors X and Y and write the result to the output register C.
Finally, we return the value stored in C.
Matrix multiplication with Tensor Cores
Tensor cores are a way to improve performance while maintaining high energy efficiency. They allow us to execute multiple instructions per clock cycle. In our case, we want to calculate a 32×32 matrix product. We need to load data from global memory, compute the product, and write it back. There are many ways to implement this algorithm, but we wanted to show how it could be done with Tensor Cores. If you look closely, there’s a lot of parallelism here. We load four matrices, each one loaded in one cycle. Then we multiply eight 16×16 matrices together, in one cycle. Finally, we output the final answer.
To make sure everything works correctly, we wrote a test program that loads up different numbers of matrices, multiplies them together, and checks the result against what we expected. This process takes about 20 seconds on average. On a single Tesla P100 GPU, we achieve a speedup factor of 5.7 compared to a regular CPU intensive implementation.
When is it better to use the cloud vs a dedicated GPU desktop/server?
If you expect to do deep neural network training for longer than a year it is generally cheaper to rent a powerful GPU server rather than buying one.
Otherwise, cloud instances are preferable due to the ease of scalability and elasticity. However, depending on the type of machine you need, you might find that the cost of renting a GPU instance is actually less than purchasing a similar machine.
For example, let’s say you want to buy a 2-way NVIDIA Tesla P40 card. This is the most powerful consumer grade GPU available today. You could purchase a barebones system with a single GTX 1080 Ti graphics card for about $1,800. Alternatively, you could rent a 4-node V100 instance with 8 V100 cards for around $3,600. Assuming that you don’t need to scale out later, you could keep this setup running 24 hours per day, 7 days a week for 12 months straight.
In addition, if you live outside of North America, you will likely incur additional charges for power usage. In my case, Amazon uses New York as a proxy location to charge me 0.12 USD /kWh for electricity. So, a comparable configuration of a 1-way Tesla P40 card plus an Intel Xeon E5-2620v4 CPU with 32 GB RAM would cost around $7,700. A 4-node V100 GPU instance with 16 V100 cards, however, would only cost $6,900.
So, what does this mean? Well, if you are planning to train a model for longer than a year you will save money by renting a GPU instance over building your own. On the flip side, if you are trying to build something quickly, buying a GPU will probably end up being cheaper overall.
