Choosing the right hardware for arivis Vision4D

This article explores some of the hardware considerations when configuring a system for use with arivis Vision4D.

Introduction

arivis Vision4D has been built from the ground up for the visualization and processing of very large datasets while being RAM independent. This, consequently, has a significant effect on how the software is implemented and affects how we deal with the most common bottlenecks in computing.

CPUs

Because CPUs are generally limited by their clock speeds, a common workaround for this limitation is to use parallelization. However, not all processes can be parallelized,  but what can be parallelized generally has been in Vision4D. Therefore, using a computer with more cores is generally preferable to using a computer with a higher clock rate. Buying computers with 2 or more multi-core CPUs can be a more cost-effective way to increase your core count than buying a single CPU with more cores. However, CPU speed and core count may also be limited by memory usage.

We recommend a minimum of 4 CPU cores (though Vision4D will work with fewer if that is what is available), but 8 or more cores are preferable for most image analysis operations. 

Memory

Generally speaking, everything that needs to go through the CPU must first be transferred to the RAM. Furthermore, the results of any CPU processing will also typically be stored in RAM, at least temporarily. This means that you generally need twice as much RAM as the data you are trying to process.

Vision4D has been designed to work around this limitation by using blocking and temporary documents.

Blocking involves just-in-time loading of portions of the dataset within the available RAM so that it is never necessary to hold the whole dataset in RAM. This means that arivis Vision4D can process multi-terabyte-sized datasets on a computer with no more than 16GB of RAM or even less.

Having stated this, Vision4D will use whatever memory is available, and having more RAM may speed up some RAM-intensive processes (e.g. segmentation of very large complex objects).

But the other implication is that if we are not holding the whole data in RAM, we need to create temporary documents on the hard disk to store the result of image processing operations which affects how much storage you will need.

Because of this architecture, Vision4D will allow you to process datasets larger than 500GB with as little as 4GB of RAM, but we recommend a minimum of 8GB and 16GB s preferable. Because  RAM gets a lot more expensive the more you add and Vision4D is not dependent on RAM availability to such an extent, we do not typically recommend that you buy more than 128GB of RAM, except for systems intended for multi-user concurrent usage. 

Storage

Local storage is key to arivis Vision4D's performance. Through extensive use of Just-in-Time loading and temporary documents, Vision4D relies heavily on local storage. In many ways, more so than it relies on RAM. Therefore, it's important to have plenty of local storage, both to store documents locally while working with them to enable fast just-in-time loading, and to allow for the creation of temporary documents to store the results of any image processing operations.

Using fast solid-state drives can provide marginal gain improvements for many read/write-intensive applications, but generally getting more storage is preferable to buying faster storage. We generally recommend having at least 5x as much free disk space as the documents you are trying to process.

Buying RAID storage where multiple relatively slow spinning disk drives run in parallel to increase speed and reliability can be a more cost-effective way to add a lot of fast storage.

Network-based or removable storage is generally not recommended due to the possibility that the connection may be intermittent, and the overheads in data transfer under such conditions can lead to significant slow-downs in performance.

Graphics Cards

Graphics cards are essential to 3/4D visualization. Because of the high demands that Vision4D places on such hardware for 3D rendering there are a few things to bear in mind.

Typically, integrated graphics are generally not recommended. Computers without a dedicated GPU commonly use these integrated graphics as a low cost and low power consumption alternative to dedicated cards but make several sacrifices in the process that can prevent the 4D Viewer from working optimally, or even at all in some cases. Aside from integrated graphics, there are not many GPUs that we do not recommend, but the following considerations may make your choice easier.

First, arivis Vision4D requires OpenGL 4.3 support or higher. Most GPUs made after 2012 support it, but more recent GPUs are almost certain to provide significantly better performance. In any case, it is usually a good idea to ensure your graphics card drivers are up to date. 

Second, gaming GPUs generally provide a much better price/performance balance (EUR800 for a high-end gaming GPU Vs EUR4500 for an equivalent Professional card). But "Pro" cards do offer some features that can be useful depending on your usage. 

For example, gaming cards often do not provide good levels of support for use with Remote Desktop Protocols or Virtual Machines. Some specialized drivers may be available for some cards, but generally using 3D rendering over RDP is not recommended. However, since having access to a machine with enough local storage for processing very large data may only be possible with server-style machines that aren't typically well suited to an office environment, using RDP or VMs in such cases is a preferable compromise. If that is your case, we recommend Pro Server cards because they provide better support for shared usage. 

Also, Pro cards often add features that we cannot make use of that are intended specifically for CAD applications. This adds a lot of cost for few benefits that arivis Vision4D can actually leverage. However, Pro cards can also typically be configured with a lot more video memory, and while we typically won't use this additional memory for pure 3D visualization and navigation since it would most likely be beyond the capabilities of the GPU to handle such amounts of data smoothly for these applications, the extra memory can be useful to speed up the rendering of high-resolution snapshots and animations of large datasets.

Finally, NVIDIA pro graphics cards (Quadro series) generally offer better CUDA performance which can be used in Machine Learning applications.

For general usage, a mid-range gaming GPU will prove more than adequate. However, for highly demanding applications like VR we recommend a minimum of RTX 2080 Ti or better.

Operating System

Vision4D is a program for Windows 64bit operating systems. There is no MacOS or Linux version. Please check this article for further information on running Vision4D on Apple hardware. The minimum requirement for support is Windows 10. The software may work on earlier versions, but note that versions anterior to windows 7 are not supported.

Also, while the low memory requirements of Vision4D make it theoretically capable of processing large datasets in 32bit environments, the high cost of maintenance of two separate builds of the software and the relative scarcity of 32bit systems means we no longer offer a 32bit version. 

Conclusions

So, which hardware configuration should you choose?

The computer you already have, as long as it runs Windows 10 or better, has 100GB or more of free hard disk space,  and has a dedicated GPU that supports OpenGL 4.3 will probably be fine for most image analysis tasks and visualization. But if you want to buy a new machine dedicated to arivis Vision4D then the following recommendations are a useful guideline.

Minimum requirements

  • Windows 10 Pro 64-bit
  • 4-Core X86 compatible CPU
  • 8GB RAM
  • GPU with support for OpenGL 4.3 or above and 2GB of VRAM (e.g. NVidia GeForce 1050 / ATI Radeon RX560)
  • 100GB spare local storage

Recommended system specifications

Single user with datasets typically up to 500GB datasets

Desktop

  • 8-Core CPU or better (e.g. Intel Core i7-10870H or AMD Ryzen 7 Pro 3700)
  • 32GB RAM
  •  1x2TB M.2 SSD for OS and temporary documents
  • 2TB HD for storage
  • NVIDIA RTX 2080 Ti / AMD Radeon RX Vega 64 or better graphics

Laptop

  • Dell Vostro 7500 equivalent or better

      Single user with datasets frequently larger than 500GB

      Desktop

      • 24-Core CPU or better (e.g. Intel Xeon Gold 6212U or AMD Ryzen Threadripper 3960X)
      • 128GB RAM
      • 1 x 512B SSD for OS and documents
      • 1 x 2TB M.2 SSD for temporary document storage
      • 8TB+ RAID for storage
      • NVIDIA TITAN RTX / AMD Radeon VII or better

      Laptop

      • Dell XPS 17 or better
      • 8TB+ Thunderbolt RAID for storage

        Multiple concurrent users with individual workstations, typically up to 500GB datasets

        Hardware

        • As above for single users with small datasets

        License 

        • soft floating license (recommended at least 1 license per 5 users depending on usage)

        Networking

        • local network or VPN

        Multiple concurrent users with datasets frequently larger than 500GB

        Hardware

        • Aquifer Hive with 8TB+ RAID storage and GPU module or equivalent
        • Network storage for permanent storage of datasets not currently in use

        License 

        • soft floating license (recommended at least 1 license per 5 users depending on usage)

        Networking

        • local network or VPN
        • RDP

         

        Download System Requirements PDF

        System Requirements PDF