Trident analytically predicts the SDC probability of a given program and its instructions without any fault injection. It is implemented as a set of LLVM compiler passes driven by python scripts. These passes are located in ./llvm-2.9/lib/Transforms/.
These instructions will get you a copy of the project up and running on your local machine.
- The packages and dependencies required by LLFI(v2.9) and LLVM(v2.9)
- LLVM-GCC 4.2.1 (LLVM build 2.9)
- Python 2.7
Tested on Ubuntu 12.04 and 14.04 LTS
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Configure the path for llvm_gcc binary (LLVMGCC) and Trident checkout (ROOT) based on your environment in
./test/Tridnet/config.pyand./inst_count/run_sampling.shfile. -
Put the compiled LLVM IR file of the program in the
./test/Tridentand./inst_count folderdirectories. The instructions should be indexed. For example, using instrument in LLFI. The file name should have following pattern<program_name>-profile_linked.ll -
Put the program input file (if any) in
./test/Tridentand./inst_countdirectories. -
Choose the LLVM IR instructions that are considered as the program output in
./test/Trident/getStoreMaskingRate.py(line:297). -
Update the paths, indexed LLVM IR file name (<program_name>), input command (<program_input>) and output file name (<output_file>) if any in
./inst-count/run_sampling.shscript according to the program being tested.
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Profile the dynamic footprint of the instructions of the program. This can be done in
./inst_countfolder. Run the command bashrun_sampling.sh. A log file calledfi_breakdown.txtis generated. -
Copy the log file
fi_breakdown.txtto the./testfolder. Trident will read and predict the SDC probabilities of the instructions provided in this log file. -
To execute Trident, run the command
python prepare.py <program_name> "input command"in./test/Tridentfolder. For example for blackscholes bench mark use the commandpython prepare.py blackscholes "1 in_4.txt output.txt". -
Trident will aggregate the overall SDC probability of the program based on their profiled dynamic footprint and store the result in
./test/Trident/prediction.resultsafter command completion.
vTrident is an extension of Trident. It simplifies the memory dependency sub-model of Trident to identify the variation of the SDC probablity of a given program with multiple program inputs. As a result, vTrident is much faster than either fault injection or Trident to bound the SDC probability of the program with multiple inputs.
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Profile the dynamic footprint of all the instruction of the program for all the inputs following steps provided for Trident. Rename the produced
fi_breakdown.txtfor each input asinput_n-fi.txtwhere n is 0 and increments for each input. -
Place all the produced
input_n-fi.txtfiles in./test/vTrident/datadirectory. -
Put the compiled LLVM IR file of the program in the
./test/vTrident. The instructions should be indexed. For example, using instrument in LLFI. The file name should have following pattern<program_name>-llfi_index.ll -
If there are any program input files that are used in input command, place them in
./test/vTridentand if they have .txt extension, set the input_txt variable to 1 in./test/vTrident/config.py. -
Choose the LLVM IR instructions that are considered as the program output in
./test/vTrident/getStoreMaskingRate.py(line:297). -
Configure the path for llvm_gcc binary (LLVMGCC),Trident checkout (ROOT), prgram_name used in step 3 (bmName) and input commands for all the inputs (inputList) in
./test/vTrident/config.py.
To use vTrident execute the file ./test/vTrident/driver.py. After the execution of vTrident is complete, range of SDC and the input that can be used for finding median SDC using FI are provided in console and ./test/vTrident/results/variation.results.
Modeling Soft Error Propagation in Programs
http://blogs.ubc.ca/karthik/2018/03/04/modeling-soft-error-propagation-in-programs/
Modeling Input Dependent Error Propagation in Programs
http://blogs.ubc.ca/karthik/2018/03/04/modeling-input-dependent-error-propagation-in-programs/