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kandi X-RAY | gpu.rocks Summary

gpu.rocks is a JavaScript library. gpu.rocks has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

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gpu.rocks has a low active ecosystem.

It has 19 star(s) with 7 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 5 have been closed. On average issues are closed in 82 days. There are 1 open pull requests and 0 closed requests.

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Top functions reviewed by kandi - BETA

kandi has reviewed gpu.rocks and discovered the below as its top functions. This is intended to give you an instant insight into gpu.rocks implemented functionality, and help decide if they suit your requirements.

Set up the executor with the custom executor
Creates a new AST node .
Recursively build an AST node with parsed ast .
Generates a for for loop statements .
Adds a function to a CallExpression
Registers a new SWF service .
Add a function to the AST tree
Creates a kernel instance .
Initialize the canvas
Register a service worker

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Community Discussions

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Does cudaMallocManaged() create a synchronized buffer in RAM and VRAM?

QUESTION

Does cudaMallocManaged() create a synchronized buffer in RAM and VRAM?

Asked 2020-Sep-16 at 15:13

In an Nvidia developer blog: An Even Easier Introduction to CUDA the writer explains:

To compute on the GPU, I need to allocate memory accessible by the GPU. Unified Memory in CUDA makes this easy by providing a single memory space accessible by all GPUs and CPUs in your system. To allocate data in unified memory, call cudaMallocManaged(), which returns a pointer that you can access from host (CPU) code or device (GPU) code.

I found this both interesting (since it seems potentially convenient) and confusing:

returns a pointer that you can access from host (CPU) code or device (GPU) code.

For this to be true, it seems like cudaMallocManaged() must be syncing 2 buffers across VRAM and RAM. Is this the case? Or is my understanding lacking?

In my work so far with GPU acceleration on top of the WebGL abstraction layer via GPU.js, I learned the distinct performance difference between passing VRAM based buffers (textures in WebGL) from kernel to kernel (keeping the buffer on the GPU, highly performant) and retrieving the buffer value outside of the kernels to access it in RAM through JavaScript (pulling the buffer off the GPU, taking a performance hit since buffers in VRAM on the GPU don't magically move to RAM).

Forgive my highly abstracted understanding / description of the topic, since I know most CUDA / C++ devs have a much more granular understanding of the process.

So is cudaMallocManaged() creating synchronized buffers in both RAM and VRAM for convenience of the developer?
If so, wouldn't doing so come with an unnecessary cost in cases where we might never need to touch that buffer with the CPU?
Does the compiler perhaps just check if we ever reference that buffer from CPU and never create the CPU side of the synced buffer if it's not needed?
Or do I have it all wrong? Are we not even talking VRAM? How does this work?

...

ANSWER

Answered 2020-Sep-16 at 15:13

So is cudaMallocManaged() creating synchronized buffers in both RAM and VRAM for convenience of the developer?

Yes, more or less. The "synchronization" is referred to in the managed memory model as migration of data. Virtual address carveouts are made for all visible processors, and the data is migrated (i.e. moved to, and provided a physical allocation for) the processor that attempts to access it.

If so, wouldn't doing so come with an unnecessary cost in cases where we might never need to touch that buffer with the CPU?

If you never need to touch the buffer on the CPU, then what will happen is that the VA carveout will be made in the CPU VA space, but no physical allocation will be made for it. When the GPU attempts to actually access the data, it will cause the allocation to "appear" and use up GPU memory. Although there are "costs" to be sure, there is no usage of CPU (physical) memory in this case. Furthermore, once instantiated in GPU memory, there should be no ongoing additional cost for the GPU to access it; it should run at "full" speed. The instantiation/migration process is a complex one, and what I am describing here is what I would consider the "principal" modality or behavior. There are many factors that could affect this.

Does the compiler perhaps just check if we ever reference that buffer from CPU and never create the CPU side of the synced buffer if it's not needed?

No, this is managed by the runtime, not compile time.

Or do I have it all wrong? Are we not even talking VRAM? How does this work?

No you don't have it all wrong. Yes we are talking about VRAM.

The blog you reference barely touches on managed memory, which is a fairly involved subject. There are numerous online resources to learn more about it. You might want to review some of them. here is one. There are good GTC presentations on managed memory, including here. There is also an entire section of the CUDA programming guide covering managed memory.

Source https://stackoverflow.com/questions/63922776

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src Components: All react components Benchmark (/#/benchmark): Definitely needs improvement Content (/#/) Example: Example code on the landing page Examples (/#/examples) Header: The page header Install (/#/install): Installation instructions, can be removed and be linked to the README JellyOnFayyah: Glictchy Jellyfish GIF on landing page Main (/#/): Header + Content Nav: Navbar OldVersions: Info about the first release etc., displayed at the bottom of the landing page PageFooter: Footer ScrollButton: A scroll-to-top btn ServerBenchmarks: Graphs with gpu.js benchmarks on a server, displayed on the landing page Strength: Features of gpu.js (like node.js compatibility), need to add more features (like expoGL support) too Syntax: Supported Syntax in gpu.js, displayed on the landing page Util: Common components Code: Syntax Highlighted Codeblock Graph: Graphs Materialicon: easy to use single-component materialIcon Data: All the graphs data db: May want to move the graph data etc. to firebase in the future img: Images and GIFs scss: Global SASS files apart from the ones per component.Single line code is ended with a ; but multiline without it.All components are put inside separate directories with the same name as the componentThe main component file starts with a capital letter and is camelcased.Each component directory may have a scss file specific to that component, and should have the same name as the component.The component directories can have anything else.

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