Two new tutorials on rCUDA planned for 2021
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The rCUDA Team is glad to announce that two new tutorials on rCUDA will be held during the first months of 2021. The first of the tutorials, titled "rCUDA: Going Further in Remote GPU Virtualization", will be held on January 21st 2021 at the 2020 International Conference on High Performance Computing & Simulation (HPCS 2020). The second of the tutorials, titled "rCUDA Goes Containers: Another Step towards Remote GPU Virtualization", will be held at Principles and Practice of Parallel Programming (PPoPP 2021) on February 28th, 2021. Both tutorials will be online events due to COVID restrictions.
rCUDA trying to support unified memory. Will succeed?
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As part of the next rCUDA release, the rCUDA Team is trying to provide support for the unified memory in CUDA. This would allow rCUDA to provide much better support to some applications. We have some ideas about how to provide such support. We also have some ideas about how to make that support more efficient. Will we succeed? For sure we will learn a lot in the attempt.
Nice discussion about GPU virtualization at HPC-AI Advisory Council 2020 UK Conference
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The rCUDA Team presented several virtualization technologies at the HPC-AI Advisory Council 2020 UK Conference. The discussion included the GPU virtualization technologies created by NVIDIA, in addition to rCUDA. You can access a copy of the slides at this link.
Back from vacation; back to rCUDA development
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After summer vacations, the rCUDA Team is back to work. Just before vacations we released our new rCUDA version (v20.07), which has been very well welcome. Now, our immediate goal is improving the new version of rCUDA so that it provides support for CUDA 10.0. We are also working on providing support for multitenancy.
rCUDA goes containers
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The rCUDA Team is pleased to announce that we are creating some containers in order to distribute rCUDA with specific applications. That is, the next release of rCUDA, in addition to be distributed in the usual way (a tarball) will also be available in container form, distributed with HPC and Deep Learning applications. As we are a very small team, we will begin with a few containers and will progressively enlarge the collection of applications available.
New version of rCUDA released
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The rCUDA Team is happy to announce that the new version of the rCUDA middleware has been released. The new version, v20.07, is the result of our hard work during the last year and a half. The new version of rCUDA includes a completely new and disruptive internal architecture both at clients and servers. This new architecture is intended to provide improved performance at the same time that CUDA applications are much better supported. Moreover, the new version of rCUDA also includes a completely new communications layer, which is intended to provide much better performance than previous versions of rCUDA. This new communications layer allows that the rCUDA server can simultaneously provide service across TCP and InfiniBand (RDMA) networks. That is, the rCUDA server can provide service to some applications by using TCP/IP at the same time that other applications are served using the InfiniBand RDMA-based network. This is done transparently to the users. Additionally, support for functions in the CUDA Driver API has been noticeably improved. Also, the use of P2P data copies among GPUs located in different remote nodes has been noticeably simplified, making it fully transparent to users. We hope that rCUDA users enjoy this new version as much as we enjoyed creating it!!
rCUDA continues improving
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The rCUDA Team continues improving the rCUDA middleware. We are very happy about having recently accomplished a new milestone: the LAMMPS Molecular Dynamics Simulator is now fully working with rCUDA. This achievement has been done thanks to a thorough debugging process, which has allowed us to find several hidden bugs in the rCUDA source code. The next release of rCUDA will include this bug fixing, thus making rCUDA even more robust. The next version of rCUDA will also include additional features.
