Modern realistic computer graphics applications, such as physically accurate lighting simulation systems, require a lot of computer power for images generation. Usage of the resources of cloud computing platforms for such calculations allows to avoid additional expenses for purchase and maintenance of own computer farms. However often such simulation systems use OpenGL for 3D images display, for example during scene preparation and modification. Since cloud-based virtual machines had only software (that is, slow) OpenGL display support, it was not convenient for the users to work with their habitual computer graphics applications in such environments, and typical workflow was to prepare all data on local computer and then execute simulation in the cloud service (usually using distributed processing). Recently several cloud computer service providers started to suggest users the option of hardware (GPU-based) OpenGL support in their cloud virtual machines. This article is devoted to the investigation of hardware OpenGL display options, suggested by various providers of cloud computing services, and their comparison. Available types of hardware GPU were checked and compared, along with the conditions for their usage. Cloud computing, computer graphics, hardware OpenGL.
Modern realistic computer graphics is based on physically accurate lighting simulation in different
media. The simulation requires a lot of computing power, and time of image generation for large scenes
having millions of objects with complex optical properties, as well as many light sources, can be as
large as tens hours. Naturally, users are looking for additional computational resources to be used for
their goals. The ideas to provide such resources via Internet were elaborated in the past [
However, users of lighting simulation and realistic visualization software would prefer to not use separate services for each time-consuming operation, but to stay in the workbench they have accustomed to, and use its habitual thus convenient user interface. This becomes possible, if to arrange Virtual Machine in cloud service with all necessary software, and perform all operations in that machine. But most of modern visualization systems intensively use OpenGL for 3D scene preview, interactive navigation and scene modelling, and sometimes even for displaying of user interface objects. For smooth interactive operations with such systems, a GPU with hardware OpenGL support is necessary.
Recently several providers of cloud computing services started to suggest their clients the virtual computing platforms with the support of hardware OpenGL. The goal of our study was to investigate what services can be really obtained from them and for what cost (under cost we consider not only money but the complexity of usage as well). This article compares hardware OpenGL features, suggested by several famous cloud computing platforms. Such comparison may help users to select proper platform, which maximally fits their needs.
2021 Copyright for this paper by its authors. The following cloud computer service platforms are compared below: Google Cloud Platform; Microsoft Azure; Amazon Web Services.
In spite various visualization services are already provided in the cloud computing platforms, for
example on Amazon [
All three above services provide option to add various GPUs (NVIDIA and/or AMD) to their Virtual Machines (VM). Depending on user needs, there are cheap (for accelerated 3D rendering) or expensive (for GPU calculations) options. It is necessary to note, that only visualization by OpenGL is considered below; other visualization techniques (such as DirectX), as well as GPU calculations, are not considered.
In the cloud computing the speed of remote display naturally depends on network speed, which in turn depends on location of (distance to) the VM. Those services have different datacenters throughout the world (with different prices depending on location). To achieve the best visualization performance, it is necessary to select location of VM to be nearest to the user.
Google suggests various types of VM in frames of their «Compute Engine» service [
All set of available resources in Google Cloud Platform [
Only VMs of «N1» family support the visualization from GPU (i.e. hardware OpenGL). They are based on Skylake, Broadwell, Haswell, Sandy Bridge, and Ivy Bridge Intel CPU platforms; specific platform can be selected during VM creation. They are available as both predefined VMs and custom VMs. Custom VMs can be created within a wide range of memory-to-core ratio, ranging from 0.95 GB per core to 6.5 GB per core. During creation of predefined VM user can select among three possible variants: «standard» (balanced), «high memory» (more RAM), «high cpu» (less RAM). Table 1 shows all possible predefined variants for «N1» family of VMs.
Cost of these VMs includes 50 GB standard HDD, Windows Server 2019 (with license) and GPU
NVIDIA Tesla T4 (with NVIDIA GRID license). Instances with one GPU can have at most 24 cores;
with two GPUs – at most 48 cores; for more cores four GPUs are required. All prices are available in
the price list [
The following configuration of «standard N1» VM was created for testing in the «Tokyo» region: 4 cores (Intel Xeon 2.20 GHz), 15 GB RAM, NVIDIA Tesla T4 attached to it, and Windows Server 2019 installed. For Windows VMs only Windows Servers are readily available, though user can provide and install own OS of any version; then its licensing/registration is up to user as well.
Only NVIDIA GPUs are available in Google Cloud Platform. After VM creation GPU must be configured - proper drivers must be installed into the guest OS. Whole procedure is well described in user manual, including link to drivers themselves. There was no any problems with it, and after drivers installation applications could successfully recognize the presence of hardware OpenGL accelerator and could use it for 3D display. Speed of GUI operations and OpenGL drawing looks satisfying taking into account remote nature of operation with VM.
Initially connection is possible via standard Windows Remote Desktop only. After first connection user can install other tools for remote access, such as Chrome Remote Desktop, Team Viewer, etc.
Below is a quote from actual billing for VM in Google Cloud. VM runtime was 221 hours (9.2 days), total sum $224 (about $1 per hour). This includes cost of CPU cores, RAM, GPU, Windows license, NVIDIA GRID license (necessary for usage of GPU for visualization in VM), disk space, network traffic, etc. Cost breakdown is presented on the Figure 1.
«GPU license» here is «NVIDIA GRID license» necessary for display of hardware OpenGL from GPU. This «display from GPU» option is activated and paid separately from GPU itself. If one does not need display from GPU (e.g. GPU is used for GPU calculations only), then he does not activate «display from GPU», hardware OpenGL visualization is not used and «GPU license» is not charged. It is remarkable that cost of hardware OpenGL visualization support is more than half of total sum.
Here are several comments on the way of resources charging: GPU, GPU license, CPU, RAM are charged for VM working hours. Windows license is charged for working hours * cores. I.e. for 4-cores VM one has to pay 4 times more for Windows license than for 1-core VM. HDD is charged round the clock (even when VM is turned off).
Network traffic is charged for actual data transferred to/from VM.
Microsoft suggests various types of VM in frames of their «Azure» service [
All set of available resources in Microsoft Azure is split into «regions». Not all regions have GPUs. There is no regions in Russia. In Germany there are four regions, but none of them has GPUs. In Japan there are two regions, «Japan East» and «Japan West». «Japan West» has no GPUs, «Japan East» has it, and so VM was created there.
The complete list of available VM configurations (they call it «VM sizes») is available in [
There is no possibility to get «more RAM» or «less RAM» (as in Google Cloud Platform); size of RAM is fixed for every VM size.
For testing purpose the AMD-based VM of «NV4as_v4» size (4 cores, 14 GB RAM) was selected, for several reasons, among them are: to compare cost of VM with performance similar to one used in Google Cloud Platform, and to test visualization by AMD GPU this time.
Again after VM creation GPU must be configured properly. Procedure is well described in documentations. Proper drivers can be installed into the VM using Microsoft Azure Portal. Here also no any problems were found, and OpenGL visualization worked as expected. Everything looks the same as it was in Google Cloud Platform.
Cost of that VM is ~$0.6 per hour. It is necessary to note that the cheapest NVIDIA-based VM
(6cores one) costs 3 times more, ~$1.83 per hour; the same NVIDIA GRID technology is used (as in
Google Cloud Platform). All prices are available in online pricing calculator [
Like in Google Cloud Platform, connection is provided via standard Windows Remote Desktop protocol, but other remote access programs can be installed into the VM.
Amazon suggests Virtual Machines in their «Elastic Computer Cloud» service (EC2, [
All VM instance types available in EC2 are listed in [
Table 3
Num of cores
However, besides classic GPUs Amazon Web Services suggests also another approach for hardware OpenGL visualization: «Amazon Elastic Graphics». Amazon Elastic Graphics provides graphics acceleration for Windows instances. Elastic Graphics accelerators are available in multiple sizes and are a low-cost alternative to using GPU graphics instance types. A user can choose an instance type that meets the compute, memory, and storage needs of his / her application. Then, add the accelerator to the instance that meets the graphics requirements. An Elastic Graphics accelerator does not form part of the hardware of the cloud service instance. Instead, it is network-attached through a network interface. An Amazon-optimized OpenGL library detects the attached accelerator. It directs OpenGL API calls from user’s instance to the accelerator, which then processes the requests and returns the results.
By other words, «Amazon Elastic Graphics» can be attached to ordinary VM instances (without
GPU). Cost of «Amazon Elastic Graphics» is presented in Table 4. All prices for all VM types are listed
in [
Thus «Amazon Elastic Graphics» can be attached e.g. to instance of general purpose «t3.xlarge» type (4 cores, 16 GB RAM) which is a cheapest among 4-cores instances with cost $0.2912 per hour, and give total cost $0.3842 per hour, which is 2.3 times less than cheapest 4-cores instance with GPU.
«Amazon Elastic Graphics» can be used only for OpenGL acceleration; no DirectX support, no GPU-based calculations. This option might be interesting as a mean to minimize cost of VM with hardware OpenGL visualization support.
1 GB 2 GB 4 GB 8 GB
$0.093 $0.186 $0.372 $0.744
It seems selection of appropriate cloud computing service with GPU supporting hardware OpenGL is not an easy task, and typically, such feature seriously increases cost of service. Therefore perhaps such approach is justified for short-term projects requiring a lot of computing power (besides visualization), or when it is unprofitable to make serious initial investments (purchase of powerful computers). The convenience of cloud computing is that for every particular project it is possible to select most optimal configuration in cost-performance terms. Meanwhile when purchasing own PC, one cannot be sure its capabilities (e.g. RAM size), which seems to be enough for current project, will be enough for future projects as well.