Run CUDA programs on Bacalhau
What is CUDA
In this tutorial, we will look at how to run CUDA programs on Bacalhau. CUDA (Compute Unified Device Architecture) is an extension of C/C++ programming. It is a parallel computing platform and programming model created by NVIDIA. It helps developers speed up their applications by harnessing the power of GPU accelerators.
In addition to accelerating high-performance computing (HPC) and research applications, CUDA has also been widely adopted across consumer and industrial ecosystems. CUDA also makes it easy for developers to take advantage of all the latest GPU architecture innovations
Advantage of GPU over CPU
Architecturally, the CPU is composed of just a few cores with lots of cache memory that can handle a few software threads at a time. In contrast, a GPU is composed of hundreds of cores that can handle thousands of threads simultaneously.
Computations like matrix multiplication could be done much faster on GPU than on CPU
TD;LR
Running CUDA programs on Bacalhau
Prerequisite
To get started, you need to install the Bacalhau client, see more information here
Running CUDA locally
You'll need to have the following installed:
- NVIDIA GPU
- CUDA drivers installed
- nvcc installed
Checking if nvcc is installed
!nvcc --version
Downloading the programs
%%bash
mkdir inputs outputs
wget -P inputs https://raw.githubusercontent.com/tristanpenman/cuda-examples/master/00-hello-world.cu
wget -P inputs https://raw.githubusercontent.com/tristanpenman/cuda-examples/master/02-cuda-hello-world-faster.cu
Viewing the programs
%%bash
cat inputs/00-hello-world.cu
This is a standard C++ program, which uses loops that are not parallelizable so it doesn't use the most processing power of the GPU
%%timeit
!nvcc -o ./outputs/hello ./inputs/00-hello-world.cu; ./outputs/hello
!cat inputs/02-cuda-hello-world-faster.cu
Instead of looping we use Vector addition using CUDA and allocate the memory in advance and copy the memory to the GPU using cudaMemcpy so that it can utilize the HBM (High Bandwidth memory of the GPU)
!rm -rf outputs/hello
%%timeit
!nvcc --expt-relaxed-constexpr -o ./outputs/hello ./inputs/02-cuda-hello-world-faster.cu; ./outputs/hello
It takes around 8.67s to run 00-hello-world.cu while it takes 1.39s to run 02-cuda-hello-world-faster.cu
Running a Bacalhau Job
To submit a job, run the following Bacalhau command:
%%bash --out job_id
bacalhau docker run \
--gpu 1 \
--timeout 3600 \
--wait-timeout-secs 3600 \
-i https://raw.githubusercontent.com/tristanpenman/cuda-examples/master/02-cuda-hello-world-faster.cu \
--id-only \
--wait \
nvidia/cuda:11.2.0-cudnn8-devel-ubuntu18.04 \
-- /bin/bash -c 'nvcc --expt-relaxed-constexpr -o ./outputs/hello ./inputs/02-cuda-hello-world-faster.cu; ./outputs/hello '
Structure of Bacalhau Commands
Let's look closely at the command above:
-
-i < Link-To-The-Program >
: The program is mounted by using the-i
flag you can specify the link there -
nvidia/cuda:11.2.0-cudnn8-devel-ubuntu18.04
: Docker container for executing CUDA programs you need to choose the right CUDA docker container the container should have the tag of devel in them -
nvcc --expt-relaxed-constexpr -o ./outputs/hello ./inputs/02-cuda-hello-world-faster.cu
: Compilation using the nvcc compiler and save it to the outputs directory as hello -
./outputs/hello
: Execution hello binary: -
You can combine compilation and execution commands. Note that there is
;
between the commands:-- /bin/bash -c 'nvcc --expt-relaxed-constexpr -o ./outputs/hello ./inputs/02-cuda-hello-world-faster.cu; ./outputs/hello
When a job is submitted, Bacalhau prints out the related job_id
. We store that in an environment variable so that we can reuse it later on.
Checking the State of your Jobs
- Job status: You can check the status of the job using
bacalhau list
.
%%bash
bacalhau list --id-filter ${JOB_ID} --wide
When it says Published
or Completed
, that means the job is done, and we can get the results.
- Job information: You can find out more information about your job by using
bacalhau describe
.
%%bash
bacalhau describe ${JOB_ID}
- Job download: You can download your job results directly by using
bacalhau get
. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory and downloaded our job output to be stored in that directory.
%%bash
rm -rf results && mkdir -p results
bacalhau get $JOB_ID --output-dir results
Viewing your Job Output
To view the file, run the following command:
%%bash
cat results/stdout # displays the contents of the file