high performance computing

This post will take you through starting and configuring an Amazon EC2 instance to use the Multi GPU features of CUDA 4.0.

Motivation

CUDA 4.0 comes with some new exciting features such as:

• the ability to share GPUs across multiple threads;
• or use all GPUs in the system concurrently from a single host thread;
• and unified virtual addressing for faster multi GPU programming;

and many more.

The ability to access all the GPUs in a system is particularly nice on Amazon, since the large GPU enabled instances come with two Tesla M2050 Fermi boards, each capable of 1030 GFlops theoretical peak performance with 448 cores and 3GB of memory.

Getting started

Signing up to Amazon’s AWS is easy enough with a Credit Card, and once you are logged in, go to the EC2 tab of your console which should look something like this:

The EC2 console page.

Now press the Launch Instance button and in the Community AMIs tab set the Viewing option to Amazon Images and search for gpu and Select the CentOS 5.5 GPU HVM AMI and press Continue:

Choose the CentOS 5.5 GPU HVM AMI (bottom one).

Next we need to select the Instance Type and its important here to select the Cluster GPU type, and then press Continue:

Select the Cluster GPU Instance Type.

Next we need to Create a New Key Pair, by giving it a name like amazon-gpu and press Create & Download your Key Pair to download it to your local computer as a file called amazon-gpu.pem:

Create and download Key Pair.

We press Continue to go to the Firewall setting. Here we Create a new Security Group, give it a name and description, and then Create a new rule for ssh so that we can log into our instance once its up and running, and press Continue:

Create a new Security Group and a new ssh rule.

And finally we can review our settings and Launch it:

Review and Launch instance.

Back in our EC2 console we can go to our Instances and see our new AMI’s Status. It should be booting or running, rather than stopped as in the case below:

AMI Instance's Status and Description.

The Description tab will also contain the Public DNS which we can use together with the Key Pair we downloaded locally to ssh into our instance:

$ chmod 400 amazon-gpu.pem
$ ssh root@ec2-50-16-170-159.compute-1.amazonaws.com -i amazon-gpu.pem

__| __|_ ) CentOS
_| ( / v5.5
___|\___|___| HVMx64 GPU

Welcome to an EC2 Public Image
Please view /root/README


[root@ip-10-16-7-119 ~]#


Updating CUDA to 4.0

Now we need to update the CUDA driver and toolkit on our instance, so the first thing we do is to update the Linux Kernel and reboot the instance via the web console:

[root@ip-10-16-7-119 ~]# yum update kernel kernel-devel kernel-headers
Loaded plugins: fastestmirror
Determining fastest mirrors
* addons: mirror.cogentco.com
* base: mirror.umoss.org
* extras: mirror.symnds.com
* updates: mirror.umoss.org
addons | 951 B 00:00
base | 2.1 kB 00:00
base/primary_db | 2.2 MB 00:00
extras | 2.1 kB 00:00
extras/primary_db | 260 kB 00:00
updates | 1.9 kB 00:00
updates/primary_db | 635 kB 00:00
Setting up Update Process
Resolving Dependencies
--> Running transaction check
---> Package kernel.x86_64 0:2.6.18-238.12.1.el5 set to be installed
---> Package kernel-devel.x86_64 0:2.6.18-238.12.1.el5 set to be installed
---> Package kernel-headers.x86_64 0:2.6.18-238.12.1.el5 set to be updated
--> Finished Dependency Resolution

Dependencies Resolved

================================================================================
Package Arch Version Repository Size
================================================================================
Installing:
kernel x86_64 2.6.18-238.12.1.el5 updates 19 M
kernel-devel x86_64 2.6.18-238.12.1.el5 updates 5.5 M
Updating:
kernel-headers x86_64 2.6.18-238.12.1.el5 updates 1.2 M

Transaction Summary
================================================================================
Install 2 Package(s)
Upgrade 1 Package(s)

Complete!


I leave it as an exercise to figure out how to reboot the instance from the console, but once its back up and running, we can ssh back into it to download and install the CUDA 4.0 drivers, toolkit and SDK. For example:

[root@ip-10-16-7-119 ~]# wget http://developer.download.nvidia.com/compute/cuda
/4_0/toolkit/cudatoolkit_4.0.17_linux_64_rhel5.5.run
--2011-06-23 04:47:05-- http://developer.download.nvidia.com/compute/cuda/4_0/toolkit/cudatoolkit_4.0.17_linux_64_rhel5.5.run
Resolving developer.download.nvidia.com... 168.143.242.144, 168.143.242.203
Connecting to developer.download.nvidia.com|168.143.242.144|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 212338897 (203M) [application/octet-stream]
Saving to: `cudatoolkit_4.0.17_linux_64_rhel5.5.run'

100%[======================================>] 212,338,897 33.2M/s in 6.3s

2011-06-23 04:47:12 (32.0 MB/s) - `cudatoolkit_4.0.17_linux_64_rhel5.5.run' saved [212338897/212338897]

[root@ip-10-16-7-119 ~]# chmod +x cudatoolkit_4.0.17_linux_64_rhel5.5.run
[root@ip-10-16-7-119 ~]# ./cudatoolkit_4.0.17_linux_64_rhel5.5.run


will install the CUDA toolkit. Similarly install the drivers and SDK and finally check everything is working by typing:

[root@ip-10-16-7-119 ~]# nvidia-smi -a -q

GPU 0:0:3
Product Name : Tesla M2050
Display Mode : Disabled
Persistence Mode : Disabled
Driver Model
...
GPU 0:0:4
....


MultiGPU example

Once CUDA 4.0 is installed and working, we can test out the MultiGPU example that comes with the SDK installed earlier. Firstly we will need to install the C++ compiler:

[root@ip-10-16-7-119 simpleMultiGPU]# yum install gcc-c++

and then we need to set our LD_LIBRARY_PATH to include the CUDA libraries:

[root@ip-10-16-7-119 release]# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/lib

After that, we can go to the NVIDIA_GPU_Computing_SDK/C/ folder and type make. The binaries will be installed in the NVIDIA_GPU_Computing_SDK/C/bin/linux/release/ directory and if we go there, we can run the simpleMultiGPU example:

[root@ip-10-16-7-119 release]# ./simpleMultiGPU
[simpleMultiGPU] starting...
CUDA-capable device count: 2
Generating input data...

Press ENTER to exit...


MultiGPU Cluster Setup

Using the above setup and this video, it is also possible to configure an 8 node cluster of GPU instances as described here for high performance computing applications. I will try to do a MultiGPU and Open MPI example in another blog post so stay tuned.