BENCHMARK TEST GPU CODE

This is an advanced benchmarking app that aims to put modern GPUs through the toughest set of rendering algorithms, managing to extract the last possible percentage of available performance, fill up the last megabyte of its onboard video memory, and then require the GPU to remain in this state from prolonged periods of time. While many other benchmarking apps are focused on providing “realistic” GPU load that will easily showcase the gaming capabilities of the GPU chip, its memory system, bandwidth, and latencies, FurMark stands apart from them by focusing only on testing the stability and prolonged peak performance of modern GPUs. Metric to monitor is again accuracy.FurMark is one of the most popular benchmarking tools for your GPU. Finally, we compile our model with adam as optimizer and sparce_categorical_crossentropy as loss fn. In the above code, we store layers as a list and then append those hidden layers as provided in the hidden_layers. If this seems unfamiliar, let me break it down: Layers.append((10, activation='sigmoid')) This will allow for a tuning model architecture in case of model overfits. Train_images_scaled = train_images / 255.0įinally, let’s change our model to define how many hidden layers we need for our work and set remote units to be 500. You can check if TensorFlow is running on GPU by listing all the physical devices as: _physical_devices() Also, former background setting tensorflow_gpu(link in reference) and Jupyter notebook line magic is required. Let’s now move on to the 2nd part of the discussion – Comparing Performance For Both Devices Practically.įor simplicity, I have divided this part into two sections, each covering details of a separate test. I hope this section gave a bit of understanding. Thanks to GPU, adding one can dramatically increase the computing time. HPC: Most companies can spread their computing among the multiple cluster/nodes/cloud servers and get their job done significantly faster. VDI(Virtual Desktop Infrastructure): One can access the applications from the cloud(CAD), and GPUs can process them in real-time with very low latency.ĪI: Due to the ability to process heavy computation, one can teach a machine to mimic humans using neural nets and ml algorithms that primarily work with complex math calculations behind the scenes.

Low Latency: Due to the ability to process high intensive calculations, one can expect to get results without delay(i.e., fast computations).

BENCHMARK TEST GPU CODE

Parallelism: One can run code and get a result concisely as all processes are performed in a parallel manner. There are many reasons to use work on these devices, two most commons are: In a nutshell, one can think of GPU as an extra brain that was always present, but now the power is being harnessed by tech giants like Nvidia & AMD. This feature is ideal for performing massive mathematical calculations like calculating image matrices, calculating eigenvalues, determinants, and a lot more. GPUs or Graphical Processing Units are similar to their counterpart but have a lot of cores that allow them for faster computation simultaneously( parallelisim 1). With the goal, set let’s now look at what GPUs are, why to USE them, and their USE CASES. We will also compare the performance of both by training 2 NN’s to recognize digits and pieces of clothing each. Today, we will understand what these GPUs have to offer and how they can increase our productivity. Their impacts can be seen everywhere, from performing scientific calculations to launching rockets and even personal devices such as PCs and Laptops. This arrival led us to a new era of computing called AI(Artificial Intelligence) due to the computation power it has to offer. In the past few decades, many revolutions have changed the world we live in, one of them being GPUs. This article was published as a part of the Data Science Blogathon.

Benchmark test gpu