rCUDA v18.10 successfully used in remote hands-on lab sessions in University of Malaga, Spain

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Due to COVID-19, Spanish universities had to switch from in-class to online teaching. In this context, lab works had to be organized in such a way that students could practice from home. In the subject "Signal and Multimedia Processors" of fourth year of the Degree in Electronic Systems Engineering (taught by the Electronic Technology Department of University of Malaga at the Higher Technical School of Telecommunications Engineering), students had to practice with CUDA. However, most students did not have the required GPU at home. In order to overcome this concern, the teacher, Francisco Javier González Cañete (fgc -(@)- uma -(.)- es), decided to install rCUDA in a server so that students could practice with CUDA without having a CUDA GPU at home. That is, students wrote their own CUDA programs in their home computer, compiled them at their computer, and executed them in a remote GPU by using rCUDA. In this way, the client side of rCUDA was used in the students' computers whereas the server side of rCUDA was running in a shared server providing service to all the students. Communication between home computers and the shared server was carried out across Internet. The experience has been a great success: students were able to practice with CUDA from home at the same time that rCUDA provided service without a single failure. Notice that each student had to devote at least 20 hours over several weeks in order to complete these lab works. The rCUDA Team is very happy about this successful experience. Additionally, given that the rCUDA Team is being much more demanding during the design and implementation of the new rCUDA version, we expect the new version of rCUDA to be very robust.

TensorFlow is starting to work with the new rCUDA version

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The rCUDA Team is very happy to inform that we continue making progress with the new version of rCUDA. Our latest achievement is that TensorFlow is starting to work with rCUDA. We have been able to successfully execute a few samples, such as Inception or Cifar10, using one remote GPU, two remote GPUs located in the same server, and two remote GPUs located at different servers. More work and testing is still required to make TensorFlow work with rCUDA reliably. In any case, we tried with TensorFlow 1.12, which is the latest version of TensorFlow using CUDA 9.0 (we are focusing on completing the development of rCUDA for CUDA 9.0 before trying newer CUDA versions). We are confident that newer versions of TensorFlow, such as 1.13 or 1.14, will work once we upgrade rCUDA to support CUDA 10.x. Final step will be making TensorFlow 2.x work with rCUDA. This final step is still far away in the horizon.

The new rCUDA version is getting more and more complete

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The rCUDA Team is glad to announce that the new rCUDA version is now able to perform data copies between GPUs located in different remote servers. That is, if an application using rCUDA is assigned two GPUs, each of them located in a different server, now it is possible to directly copy data among them. This can be done for all the data copy functions included in CUDA (1D, 2D, 3D and Array versions of these functions). Also, this can be done regardless of the exact GPU models involved in the data copy (GPUs in both servers can be different).

The new version of rCUDA is born

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The rCUDA Team is glad to announce that the new version of rCUDA was born a few days ago. In addition to many bug fixes and minor improvements, the new version of rCUDA integrates three big developments: (1) a new internal architecture intended to provide better support to CUDA applications as well as close-to-native performance; (2) a new communications layer able to get all the bandwidth from the underlying network fabric; (3) support for Slurm. As every baby, the new rCUDA version is small right now. However it is quickly growing. We have been already able to execute a dozen NVIDIA samples. This number grows and grows every day.

The new rCUDA release is closer

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The rCUDA Team is happy to inform that the development of the new rCUDA version is making good progress. By using the new rCUDA version, we have been able to reliably execute our entire set of almost 300 synthetic samples designed to stress specific features of CUDA in extreme conditions. Also, we have been able to reliably execute 88 of the samples included in the CUDA package, such as BiCGStab, BlackScholes, MonteCarloMultiGPU, etc. These tests have been conducted using one remote GPU and also, for those samples able to use more than one GPU, we have used two remote GPUs located either in one or in two different servers. Different GPU generations were used in the tests (K20, K40, K80, P100, V100). Moreover, some applications are starting to work with rCUDA. For instance, we have been able to execute the Gromacs application. We expect the list of applications to grow in the next weeks.

rCUDA listed in the TOP 100 Spain Influencers, Blogs, Podcasts & Youtubers in 2020

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According to Feedspot, rCUDA is one of the TOP 100 Influencers, Blogs, Podcasts & Youtubers in Spain. We thank Feedspot for these great news!

The new rCUDA version is able to ...

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The new version of rCUDA is easier to be used.  Now, the server side of rCUDA can automatically adapt to the network fabric used by the client side. That is, once the rCUDA server is up and running, it can concurrently accept clients that use Ethernet and clients that use InfiniBand. In previous versions of rCUDA, the rCUDA server could only accept either Ethernet clients or InfiniBand clients, depending on the configuration specified when launching the rCUDA server. We will provide more details about the new rCUDA version in next posts. 

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