How to overcome the memory limitations of the GPU?
Let’s now see how we could break the GPU memory boundaries and still use larger batch sizes. One way to overcome the GPU memory limitations and run large batch sizes is to split the batch of samples into smaller mini-batches, where each mini-batch requires an amount of GPU memory that can be satisfied.
How much memory does a GPU card have?
The GPU offers a 112GB/s memory bandwidth, and many believe that this narrow interface will not provide enough memory bandwidth for games. This card is primarily aimed at the midrange crowd, wanting to run modern titles (both AAA and independent), at a native resolution of 1080p.
Is the memory bandwidth of a GPU accurate?
NVidia has said themselves that Bus usage is wholly inaccurate, and most of us are aware that Memory Controller Load (%) cannot represent the exact bandwidth usage (GB/s) with total precision. All loads are different. All of the following benchmarks were run 4 times for each game on each resolution for accuracy.
How to calculate the peak performance of a GPU?
Instead, it starts solely from hardware metrics, letting us know how well the whole GPU is utilized, what hardware units and sub-units are limiting the performance, and how close they are running to their respective maximum throughputs (also known as “Speed Of Light” or ”SOL”).
What’s the best way to increase GPU usage?
If you’re training on one GPU and not maxing out your utilization, I have a quick recommendation: double your batch size and double your learning rate. You can dive into more data from my runs in the Batch Size Report.
How to monitor GPU usage for training models?
The danger of taking a single measurement is that GPU usage can change over time. This is a common pattern we see where our user Boris is training an RNN; mid-training, his usage plummets from 80 percent to around 25 percent.
Why is GPU usage so low in deep learning?
Average GPU memory usage is quite similar. Our users tend to be experienced deep learning practitioners and GPUs are an expensive resource so I was surprised to see such low average usage. Here’s a few easy, concrete suggestions for improving GPU usage that apply to almost everyone: