Moved to: https://github.com/UniHD-CEG/gpugraph500
Table of Contents
- Requirements
- Download
- Build
- Run
- Scripts
- Profiling
- Current Limitations
- Troubleshooting
- Author
- License
- C compiler. C++ Compiler with c++11 support.
- To use CUDA-BFS or CUDA-compression: CUDA 6+ support.
- To use SIMD compression: SSE4 support.
- To use SIMD+ compression SSE2 support.
- Create an account in https://github.com
Using git
-
Use your Bitbucket username account name as Your_GITHUB
-
install
git -
Fork the main repository, into your account
-
Clone from your account
$ # REPLACE __Your_GITHUB__
$ git clone https://github.com/__Your_GITHUB__/UniHD-CEG/graph500.git
$ cd bfs_multinode
Using tar
-
Download from: latest .gz
-
Decompress:
$ tar -xzvf architectural_tuning.tar.gz
$ cd architectural_tuning
The code to compile is in the folder cpu_2d/. to build the binary:
./configure
make
for options, run ./configure --help
Change to folder eval/
The tests are (valid with SLURM)
- o4p2n-roptim.rsh [SCALE_FACTOR]
- o4p2n-coptim.rsh [SCALE_FACTOR]
- o4p2n-noptim.rsh [SCALE_FACTOR]
- o9p8n.rsh [SCALE_FACTOR]
- o16p8n.rsh [SCALE_FACTOR]
sbatch o16p8n.rsh 21
runs a test with 16 proccesses in 8 nodes, using Scale Factor 21
- r-ify.sh
- r-compare.sh
Scripts are located under the scripts/ folder
To install:
change to the eval/ directory
$ ln -s ../scripts/r-ify.sh r-ify.sh
$ ln -s ../scripts/r-compare.sh r-compare.sh
$ chmod u+x *.sh
Script r-ify.sh
It uses the execution traces to generate R-code. This R-code (once run) shows the time measurements of the Phases of the BFS code. This script uses result files with same Scale Factor. The results are represented as a Barplot.
$ ./r-ify.sh 423 424 425
-> File slurm-423.out. Validation passed (Tasks: 4, GPUS/Task: 1, Scale Factor: 21).
-> File slurm-424.out. Validation passed (Tasks: 4, GPUS/Task: 1, Scale Factor: 21).
-> File slurm-425.out. Validation passed (Tasks: 4, GPUS/Task: 1, Scale Factor: 21).
Enter new labels for the X-Axe? (y/n) [n] y
Enter a total 3 label(s) between quoutes. Separate them with spaces: "4p2n-Roptim" "4p2n-Coptim" "4p2n-Noptim"
-> Created file "file-423-424-425.r".
-> R-Code successfully generated.
Open the file file-423-424-425.r with your R editor and run the code.
Script r-compare.sh
As the previous script, this also uses the execution traces to generate R-code. This differs from the previous one in that it can compare several files from several Scale Factors. Results are visualized as a Lineplot.
$ ./r-compre.sh JOBID1 JOBID2 ...
Script check-all.sh (requires SLURM)
This script automatizes the execution of tests for different Scale Factors.
$ check-all.sh 15 30
This will run the tests with format o*.rsh in the eval/ folder for Scale Factors 15 to 30. Process is shown in ncurses-like format.
This BFS application allows the code to be instrumented in Zones using Score-P with very low overhead. This requires Score-P and Scalasca to be installed in the system. The results may be analyzed either visually (using CUBE) or through console using scorep-score.
These tools may be installed locally (no priviledged user is needed) using the external-apps-installer.sh aforementioned.
The names of the instrumentable Zones are listed below. Other Zones may be added if needed.
BFSRUN_region_vertexBroadcast
BFSRUN_region_nodesTest
BFSRUN_region_localExpansion
BFSRUN_region_testSomethingHasBeenDone
BFSRUN_region_columnCommunication
BFSRUN_region_rowCommunication
The first step is to update the system variables. This may be done either on .bashrc or in a separate script.
Update the paths in the variables below
$ cat >> ~/.bashrc << EOF
export G500_ENABLE_RUNTIME_SCALASCA=yes
export SCOREP_CUDA_BUFFER=48M
export SCOREP_CUDA_ENABLE=no
export SCOREP_ENABLE_PROFILING=true
export SCOREP_ENABLE_TRACING=false
export SCOREP_PROFILING_FORMAT=CUBE4
export SCOREP_TOTAL_MEMORY=12M
export SCOREP_VERBOSE=no
export SCOREP_PROFILING_MAX_CALLPATH_DEPTH=330
export LD_LIBRARY_PATH=$HOME/cube/lib:$LD_LIBRARY_PATH
export PATH=$HOME/cube/bin:$PATH
export LD_LIBRARY_PATH=$HOME/scorep/lib:$LD_LIBRARY_PATH
export PATH=$HOME/scorep/bin:$PATH
export LD_LIBRARY_PATH=$HOME/scalasca/lib:$LD_LIBRARY_PATH
export PATH=$HOME/scalasca/bin:$PATH
export MPI_PATH=/home/jromera/openmpi
export PATH=$MPI_PATH/bin:$CUDA_PATH/bin:$PATH
export LD_LIBRARY_PATH=$MPI_PATH/lib:$CUDA_PATHo/lib64:$CUDA_PATHo/lib64/stubs:$CUDA_PATHo/lib:$CUDA_PATHo/extras/CUPTI/lib64:$CUDA_PATHo/extras/CUPTI/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=$HOME/scorep/lib:$LD_LIBRARY_PATH
export PATH=$HOME/scorep/bin:$PATH
EOF
The variable G500_ENABLE_RUNTIME_SCALASCA set to yes will enable the required runtime instrumentor of Scalasca.
Results will be stored on a folder with format scorep-* in the /eval folder.
To instrument graphically with CUBE run:
$ cd eval/scorep-____FOLDER_NAME____
$ cube profile.cubex
To instrument through the console run:
$ cd eval/scorep-____FOLDER_NAME____
$ scorep-score -r profile.cubexOut-Of-Memory errors and CUDA memory size limitations:
For some high Score-Factors (e.g: 22, as of the day of writing this guide), the resulting Slurm trace will be:
Using SCALE-FACTOR 22
Tue Jul 14 15:42:51 CEST 2015
======================== JOB MAP ========================
Data for node: creek01 Num procs: 2
Process OMPI jobid: [1404,1] Process rank: 0
Process OMPI jobid: [1404,1] Process rank: 2
Data for node: creek02 Num procs: 2
Process OMPI jobid: [1404,1] Process rank: 1
Process OMPI jobid: [1404,1] Process rank: 3
=============================================================
row slices: 2, column slices: 2
graph_generation: 7.749108 s
Input list of edges genereted.
6.710886e+07 edge(s) generated in 8.499692s (7.895447 Medges/s on 4 processor(s))
Adjacency Matrix setup.
2.956432e+06 edge(s) removed, because they are duplicates or self loops.
1.283049e+08 unique edge(s) processed in 18.308235s (7.008041 Medges/s on 4 processor(s))
[../b40c/graph/bfs/csr_problem_2d.cuh, 697] CsrProblem cudaMalloc frontier_queues.d_values failed (CUDA error 2: out of memory)
[cuda/cuda_bfs.cu, 486] Reset error. (CUDA error 2: out of memory)
MPI_ABORT was invoked on rank 0 in communicator MPI_COMM_WORLD
with errorcode 1.
- Problem: In the .out file of Slurm/ Sbatch execution I get the text:
S=C=A=N: Abort: No SCOREP instrumentation found in target ../cpu_2d/g500
- Solution:
The instrumentation is activated for the runtime execution (i.e: the binary is being run prefixed with scalasca).
Disable it with:
$ export G500_ENABLE_RUNTIME_SCALASCA=no
Computer Engineering Group - Ruprecht-Karls University of Heidelberg
Copyright (c) 2016, Computer Engineering Group at Ruprecht-Karls University of Heidelberg, Germany. All rights reserved.
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