Best GPUs For Bitcoin mining
Deep learning is a field with intense computational requirements and the choice of your GPU will fundamentally determine your deep learning experience. With no GPU this might look like months of waiting for an experiment to finish, or running an experiment for a day or more only to see that the chosen parameters were off. With a good, solid GPU, one can quickly iterate over deep learning networks, and run experiments in days instead of months, hours instead of days, minutes instead of hours. So making the right choice when it comes to buying a GPU is critical. So how do you select the GPU which is right for you? This blog post will delve into that question and will lend you advice which will help you to make choice that is right for you.
Having a fast GPU is a very important aspect when one begins to learn deep learning as this allows for rapid gain in practical experience which is key to building the expertise with which you will be able to apply deep learning to new problems. Without this rapid feedback it just takes too much time to learn from one’s mistakes and it can be discouraging and frustrating to go on with deep learning. With GPUs I quickly learned how to apply deep learning on a range of Kaggle competitions and I managed to earn second place in the Partly Sunny with a Chance of Hashtags Kaggle competition using a deep learning approach, where it was the task to predict weather ratings for a given tweet. In the competition I used a rather large two layered deep neural network with rectified linear units and dropout for regularization and this deep net fitted barely into my 6GB GPU memory.
Should I get multiple GPUs?
Excited by what deep learning can do with GPUs I plunged myself into multi-GPU territory by assembling a small GPU cluster with InfiniBand 40Gbit/s interconnect. I was thrilled to see if even better results can be obtained with multiple GPUs.
I quickly found that it is not only very difficult to parallelize neural networks on multiple GPUs efficiently, but also that the speedup was only mediocre for dense neural networks. Small neural networks could be parallelized rather efficiently using data parallelism, but larger neural networks like I used in the Partly Sunny with a Chance of Hashtags Kaggle competition received almost no speedup.
However, I also found that parallelization can be horribly frustrating. I naively optimized parallel algorithms for a range of problems, only to find that even with optimized custom code parallelism on multiple GPUs does not work well, given the effort that you have to put in . You need to be very aware of your hardware and how it interacts with deep learning algorithms to gauge if you can benefit from parallelization in the first place.
Since then parallelism support for GPUs is more common, but still far off from universally available and efficient. The only deep learning library which currently implements efficient algorithms across GPUs and across computers is CNTK which uses Microsoft’s special parallelization algorithms of 1-bit quantization (efficient) and block momentum (very efficient). With CNTK and a cluster of 96 GPUs you can expect a new linear speed of about 90x-95x. Pytorch might be the next library which supports efficient parallelism across machines, but the library is not there yet. If you want to parallelize on one machine then your options are mainly CNTK, Torch, Pytorch. These library yield good speedups (3.6x-3.8x) and have predefined algorithms for parallelism on one machine across up to 4 GPUs. There are other libraries which support parallelism, but these are either slow (like TensorFlow with 2x-3x) or difficult to use for multiple GPUs (Theano) or both.
If you put value on parallelism I recommend using either Pytorch or CNTK.
Using Multiple GPUs Without Parallelism
Another advantage of using multiple GPUs, even if you do not parallelize algorithms, is that you can run multiple algorithms or experiments separately on each GPU. You gain no speedups, but you get more information of your performance by using different algorithms or parameters at once. This is highly useful if your main goal is to gain deep learning experience as quickly as possible and also it is very useful for researchers, who want try multiple versions of a new algorithm at the same time.
This is psychologically important if you want to learn deep learning. The shorter the intervals for performing a task and receiving feedback for that task, the better the brain able to integrate relevant memory pieces for that task into a coherent picture. If you train two convolutional nets on separate GPUs on small datasets you will more quickly get a feel for what is important to perform well; you will more readily be able to detect patterns in the cross validation error and interpret them correctly. You will be able to detect patterns which give you hints to what parameter or layer needs to be added, removed, or adjusted.
So overall, one can say that one GPU should be sufficient for almost any task but that multiple GPUs are becoming more and more important to accelerate your deep learning models. Multiple cheap GPUs are also excellent if you want to learn deep learning quickly. I personally have rather many small GPUs than one big one, even for my research experiments.
