Virtual reality mac os x

I did not observe any performance problem. ChimeraX does not continuously stream large amounts of data to the graphics card -- most data is loaded onto the card when molecular data sets are opened or their display changed, so it is not likely to stress the eGPU bandwidth. The eGPU was always as quiet as the laptop and stays cool. Laptop charging. With the Thunderbolt 3 laptop the eGPU charges the laptop when plugged in which is a nice convenience. The Thunderbolt 1 laptop is not charged by the eGPU.

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I installed Steam on macOS SteamVR version. Other versions failed to start, with a wide array of different error messages such as could not launch compositor, headset not found, headset not connected properly, "Hmm that shouldn't have happened ," and often just the SteamVR window and dock icon appearing for a split second then disappearing with no error reported. In some versions the compositor started but the rendered graphics was just an occassional flash right at your face depending on hand-controller positions.

Vive works. I normally use a display port cable the Vive link box has both HDMI and mini-DisplaPort connections and display port did not work giving an error when starting SteamVR saying "Headset not connected properly". The SteamVR app does not crash.

First the vrmonitor window that shows green icons for headset, hand-controllers and base stations, flashes for a split second at startup as does its dock icon then vanishes, apparently crashed. The vrcompositor does start as do the other normal SteamVR processes. The image at right from Activity monitor shows the normal SteamVR processes when using the Vive, and with Vive Pro all start except vrmonitor.

Only the display port cable worked. I did not try seated setup. November 15, : Room setup is done from the vrmonitor window a pull-down menu. To work around it crashing on startup I ran room setup from a Terminal using:. Shutting down SteamVR with no vrmonitor is a pain. This is on a iMac with eGPU.

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The mirror window flashes briefly on the screen before the crash. After this restarting SteamVR fails because the compositor crashes. This is because SteamVR remembers that you enabled the mirror window and now will try to show it every time it starts. To fix this edit the SteamVR configuration file. Ways to launch SteamVR. Both ways worked.

Why HTC Vive in macOS?

I believe this setting is only available on macOS Mojave The VR view flickered and was not usable if ChimeraX mirrored the headset view to its desktop window, probably because that required the rendered image to be sent to the iMac GPU for rendering on the iMac screen which was too slow. ChimeraX can disable mirror rendering by starting vr with command "vr on display blank", and then headset rendering was smooth. It would be a slightly simpler setup if that worked but seems unlikely to work.

I did some of the testing in a small 2 meter by 1. Tracking was poor with both Vive generation 1 base stations and Vive Pro generation 2 base stations. The setup is shown in the image. After that, frames scanned out from memory to [inaudible] in the headset. This transfer takes additional frame as all pixels need to be updated before image can be presented.

Once all pixels are updated, [inaudible] and user can see a frame. So as you can see from the moment the application receives pauses, to the moment image is really projected, it takes about 25 milliseconds. That is why application receives pauses that are already predicted into the future, to the moment when photons will be emitted, so that the rendered image is matching the user pause.

Tested equipment

And this cascade of events overlapping with previous and next frame is creating our frame basing diagram. As you can see, in case of the single-threaded application, GPU is idle most of the time. So let's see if we can do anything about that. We are now switching to multi-threaded application, which separates simulation of its visual environment from encoding operations to the GPU.

Encoding of those operations will now happen on separate rendering threads. Because we've separated simulation from encoding, simulation for our frame can happen in parallel to previous frame encoding of GPU operations. This means that encoding is now shifted area in time, and starts immediately after we receive predicted pauses. This means that your application will now have more time to encode the GPU [inaudible] and GPU will have more time to process it. So, as a result, your application can have better visualize. But there is one trick.

Because simulation is now happening one frame in advance, it requires separate set of predicted pauses.

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This set is predicted 56 milliseconds into the future so that it will match the set predicted for rendering thread and both will match the moment when photons are emitted. As you can see, now our example application is encoding all these GPU [inaudible] for the whole frame into a single common buffer, so unless this common buffer is complete, GPU is waiting idle. So we can benefit from this fact, and split our encoding operation into a few common buffers while a few common buffer will be encoded very fast, with just few operations, and submitted to GPU as fast as possible.

This way, now our encoding is processing in parallel to GPU already processing our frame, and as you can see, we've just extended the time when GPU is doing its work, and as a result, further increase amount of work that you can submit in a frame. Now, let's get back to our diagram, and see how it all looks together.

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So [inaudible] application is already very good example of your application, but there are still few things we can do. If you will notice, rendering thread is still waiting with encoding of any type of GPU work before it will receive predicted pauses. But not all [inaudible] in the frame requires those pauses. So let's analyze in more detail to pick our frame workloads. Here, you can see a list of workloads that may be executed in each frame. Part of them happen in screen space or require general knowledge about pause for which frame is rendered.

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We call such workloads pause-dependent ones. At the same time, there are workloads that are generic and can be executed without knowledge about pauses immediately. We call those workloads pause independent ones. So currently, our application was waiting for pauses to encode any type of work to GPU. But if we split those workloads in half, we can encode pause independent workloads immediately and then wait for pauses to continue with encoding pause-dependent ones.

In this slide, we've already separated pause independent workloads from pause dependent ones. Pause independent workloads is now encoded in [inaudible] common buffer, and is marked with a little bit darker shade than pause-dependent workload following it. Because pause-independent workload can be encoded immediately, we will do exactly that.

We will encode it as soon as the previous frame workload is encoded.

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As soon as previous frame is finished, GPU can start with the next one. The last subsection is a multi-GPU workload distribution. We can scale our workload across multiple GPUs. Current Mac Book Pro has two GPU on board, and while they have different performance characteristics, there is nothing preventing us from using them.

Similarly, if each GPU is connected, application can use it for rendering to the headset while using Mac's primary GPU to offload some work. So we've just separated pause-independent work and moved it to a secondary GPU. We could do that because it was already encoded much earlier in our frame, and now this pause-independent workload is executing in parallel to pause-dependent workload of previous frame. As a result, we further increased the amount of GPU time that you had for your frame. But, by splitting this work into multiple GPUs, we now get to the point where we need a way to synchronize those workloads with each other.

So today we introduce new synchronization parameters to deal exactly with such situation.

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So here we will go through the simple code example. And we will use shared event to synchronize workloads of both GPUs. Event initial value is zero, so it's important to start synchronization counter from 1. That's because when we would wait on just initialized event, its counter of zero will cause it to return immediately, so there would be no synchronization. So our rendering thread now starts encoding work for our supporting GPU immediately.

It will encode pause-independent work that will happen on our supporting GPU course, and once this work is complete, its results will be stored in locker memory.