Fortunately, you can use Systems Manager to remotely run commands, like update packages, on your EC2 instances. To complicate this normally simple admin task, your security team does not allow you to direct access production servers via SSH or allow you to use bastion hosts. In our example scenario, as a System Administrator, you need to update the packages on your EC2 instances. Using the run command, one of the automation features of Systems Manager, you can simplify management tasks by eliminating the need to use bastion hosts, SSH, or remote PowerShell. Systems Manager is a management tool that enables you to gain operational insights and take action on AWS resources safely and at scale.
#Run multiple instances of calctape how to#
have MPI rank 0 process length1-data and MPI rank 1 process length2-data etc.In this hands-on tutorial, you will learn how to use AWS Systems Manager to remotely run commands on your Amazon EC2 instances. This would also help in applications like yours that have different datasets to distribute. Other ideas are: Start a MPI-application with the same number of processes per rank as there are GPUs and use the same device number as the local rank number. If you don't have enough data of one "batch" you may want the opposite: Multiple process submit work to one GPU. Note: You may need to set the GPUs to exclusive-mode so a GPU can only be used by one process. This check could involve test-creating a stream due to some bug in CUDA which made the setDevice succeed but all later calls failed as the device was actually in use. There is a better (more automatic) way, which we use in PIConGPU that is run on huge (and different) clusters.īasically: Call cudaGetDeviceCount to get the number of GPUs, iterate over them and call cudaSetDevice to set this as the current device and check, if that worked. Otherwise you need some form of job scheduler (outside the scope of this question, IMO) to be able to query unused GPUs and "reserve" one before another app tries to do so, in an orderly fashion. So the best advice (IMO) is to manage the GPUs explicitly. Most important, a GPU that is not "in use" could become "in use" asynchronously, meaning you are exposed to race conditions.A GPU that was in use at a particular instant may not be in use immediately after that.Selecting a GPU that "isn't in use" is problematic because: In my opinion the above approach is the easiest. Means the GPUs that would ordinarily enumerate as 2 and 4 would now be the only GPUs "visible" in that session and they would enumerate as 0 and 1.
You can also extend this to make multiple GPUs available, for example: export CUDA_VISIBLE_DEVICES="2,4" This means you don't have to make any changes to your application for this method, assuming your app uses the default GPU or GPU 0. Thereafter, all CUDA applications run in that session will observe only the third enumerated GPU (enumeration starts at 0), and they will observe that GPU as if it were device 0 in their session. The second instance will run on the second GPU (only), and so on.Īssuming bash, and for a given terminal session, you can make this "permanent" by exporting the variable: export CUDA_VISIBLE_DEVICES="2" Then the first instance will run on the first GPU enumerated by CUDA. In the next terminal: CUDA_VISIBLE_DEVICES="1". Then in one terminal, you could do: CUDA_VISIBLE_DEVICES="0". Let's say your application is called myexe I assume you have as many terminals open as you have GPUs. The environment variable CUDA_VISIBLE_DEVICES is your friend.
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