pl-matrixmultiply

• pl-matrixmultiply
  • Description
  • Usage
    • Requirements
    • Docker run on x86_64
    • Docker run on PowerPC
  • Example
  • Research and Development References
    • Workflow
  • Troubleshoot
  • Related Links
    • Docker Images
  • Contributor

Description

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                | | | | | | (_| | |_| |  | |>  <| | | | | | |_| | | |_| | |_) | | |_| |
                |_| |_| |_|\__,_|\__|_|  |_/_/\_\_| |_| |_|\__,_|_|\__|_| .__/|_|\__, |
                                                                        | |       __/ |
                                                                        |_|      |___/ 

This is a benchmarking plugin for ChRIS platform of Boston Children's Hospital (What is ChRIS?)on both x86_64 and PowerPC MOC using matrix multiplication.

Plugin Info	Content	Description
Input	matrix parameters	COE value to indicate matrix size, given by command line
Output	one .csv file	Contains matrix size and running time for multiplication task

Usage

Requirements

Your host computer should be a linux os and installed CUDA 10.1 && nvidia container.

Docker run on x86_64

First pull docker image to local environment:

docker pull fnndsc/pl-matrixmultiply_moc_x86_64

Then you can run it with parameters:

docker run --runtime=nvidia                                         \
            -e NVIDIA_VISIBLE_DEVICES=1                             \
            -v $(pwd)/in:/incoming -v $(pwd)/out:/outgoing          \
            fnndsc/pl-matrixmultiply                                \
            matmultiply.py                                          \
            -c 32,32,128                                            \
            /incoming /outgoing

Parameters and meaning below in the table:

docker run parameters for x86_64
parameters	function	example
--runtime=nvidia	tells the docker to use the nvidia docker	--runtime=nvidia
-e	specifies the visible graphic device id	-e NVIDIA_VISIBLE_DEVICES=1
-v	specify input and outgoing folder, check docker volume bind	-v $(pwd)/in:/incoming -v $(pwd)/out:/outgoing
image_name	specify docker image name	fnndsc/pl-matrixmultiply
script_name	specify script file to run	matmultiply.py
-c	specify COE value to define matrix size, format like: start_value, gap_value, end_value	-c 32,32,128

Docker run on PowerPC

First pull docker image to local environment:

docker pull fnndsc/pl-matrixmultiply_moc_ppc64

Then you can run it with parameters:

docker run  --security-opt label=type:nvidia_container_t            \
            -v $(pwd):/incoming:z -v $(pwd)/out:/outgoing:z         \
            fnndsc/pl-matrixmultiply_moc_ppc64                      \
            matmultiply.py                                          \
            -c 32,32,128                                            \
            /incoming /outgoing

Parameters and meaning below in the table:

docker run parameters for PowerPC
parameters	function	example
--security-opt label=type:nvidia_container_t	tells the docker to use the nvidia docker	--security-opt label=type:nvidia_container_t
-v	specify input and outgoing folder, check docker volume bind	-v $(pwd):/incoming:z -v $(pwd)/out:/outgoing:z
image_name	specify docker image name	fnndsc/pl-matrixmultiply_moc_ppc64
script_name	specify script file to run	matmultiply.py
-c	specify COE value to define matrix size, format like: start_value, gap_value, end_value	-c 32,32,128

Build Instructions

For ppc64le image, we cannot use the automatic build on docker hub. We have to build this conatiner locally and push it into docker hub.

cd path/to/this/repo
docker login docker.io -u [your docker.io username]
docker build -f Dockerfile -t docker.io/fnndsc/pl-matrixmultiply_moc_ppc64 
docker push "docker.io/fnndsc/pl-matrixmultiply_moc_ppc64"

Example

x86_64

docker run --runtime=nvidia                                         \
            -e NVIDIA_VISIBLE_DEVICES=1                             \
            -v $(pwd)/in:/incoming -v $(pwd)/out:/outgoing          \
            fnndsc/pl-matrixmultiply                                \
            matmultiply.py                                          \
            -c 32,32,128                                            \
            /incoming /outgoing

PowerPC

docker run  --security-opt label=type:nvidia_container_t            \
            -v $(pwd):/incoming:z -v $(pwd)/out:/outgoing:z         \
            fnndsc/pl-matrixmultiply_moc_ppc64                      \
            matmultiply.py                                          \
            -c 32,32,128                                            \
            /incoming /outgoing

Research and Development References

Workflow

What this plugin simply does is, when assigned with the COE parameter (-c), it will generate a list of COE values, for example, -c 32,32,128 will generate four parameters of COE as: [32, 64, 96, 128], and it is not necessary to assign the end value as exactly divisible by gap value. (which means you can go with -c 32,32,100.

For each COE value in this list, it will generate square matrix with size to be (COE x TPB) ^2, where TPB value is determined as 32 in the program, and therefore you have different sizes of matrix to do multiplication.

The program will record this running time along with start time, end time and matrix size, and generate a .csv file in your output folder, that's what you can use for benchmarking purpose.

Troubleshoot

Make sure your /out directory has corresponding authorization, you can try:

mkdir in out && chmod 777 out                                       

Related Links

https://medium.com/datathings/benchmarking-blas-libraries-b57fb1c6dc7

Docker Images

PowerPC: https://hub.docker.com/r/fnndsc/pl-matrixmultiply_moc_ppc64

x86_64: https://hub.docker.com/r/fnndsc/pl-matrixmultiply_moc_x86_64

Contributor

Haoyang Wang haoyangw@bu.edu

Kefan Zhang kefan29@bu.edu

Feel free to reach out us if you have any question. :)