Introduction To GPUs
It does not matter who you are, any data scientist or machine learning enthusiast that has been trying to create a training model that will perform at scale will at some point hit a cap and start lagging. Things that used to take minutes start taking hours or longer as the datasets get larger. This is where a GPU comes in.
Machine learning is the ability of computer systems to learn to make decisions and predictions from observations and data. A GPU is a specialized processing unit with enhanced mathematical computation capability, making it ideal for deep learning models. With a GPU the time it takes for the model to run is significantly smaller.
Why Not Just Use My?CPU?
If you look at specifications it will seem like a CPU is better than a GPU. Some even go as far as to say that the difference between a CPU and a GPU is that GPUs support better processing for high-resolution video games and movies, but that is not true. When it comes to handling specific workloads their differences are more pronounced.
A CPU handles the majority of the processing tasks for a computer and have to be fast and versatile to do so. CPUs are built to handle any required tasks that a typical computer might perform: accessing hard drive storage, logging inputs, moving data from cache to memory, and so on. That means that CPUs can bounce between multiple tasks quickly to support the more generalized operations of a workstation or even a supercomputer. A GPU is designed to render high-resolution images and graphics almost exclusively which doesn’t require a lot of context switching. GPUs instead focus on concurrency, or breaking down complex tasks (like identical computations used to create effects for lighting, shading, and textures) into smaller subtasks that can be continuously performed in tandem. This means that a GPU will work working faster than a CPU on deep learning tasks because it is focused on a single task.
Why Use A GPU For Modelling
GPUs are optimized for training artificial intelligence and deep learning models because they can process multiple computations simultaneously. Having a GPU will also make it so that you don’t overclock your CPU and makes it capable of going over large amounts of datasets. This also makes it so that the model can be done faster than without a GPU.
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What You Should Look For In A?GPU
High Memory Bandwidth
GPUs take data in parallel operations so they need to have a high memory bandwidth. Higher bandwidth with a higher VRAM is usually better, depending on your job.
Tensor Cores
Tensor cores allow for faster matrix multiplication in the core. Not all GPUs come with tensor cores but they are more common, even in consumer-grade GPUs.
More Significant Shared?Memory
GPUs with higher L1 caches can increase data processing speed by making data more available — but it is more expensive. GPUs with more caches are generally preferable, but it is a trade-off between cost and performance.
Interconnection
A cloud or on-premise solution utilizing GPUs for high-performance workloads have several units interconnected with one another. Unfortunately, not all GPUs are compatible with one another, so make sure that the GPUs that you use are compatible with each other.
GPUs can also be used on the cloud. Skip all the hassle of having to know what is what and have Eden AI help you accomplish what you distributed processing goals. Send us an email and we will be happy to assist where possible. Contact us at [email protected]