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.
Tested on Ubuntu 12.04 and 14.04 LTS
Configure the path for llvm_gcc binary (LLVMGCC) and Trident checkout (ROOT) based on your environment in ./test/Tridnet/config.py and ./inst_count/run_sampling.sh file.
Put the compiled LLVM IR file of the program in the ./test/Trident and ./inst_count folder directories. 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/Trident and ./inst_count directories.
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 (./inst-count/run_sampling.sh script according to the program being tested.
Profile the dynamic footprint of the instructions of the program. This can be done in ./inst_count folder. Run the command bash run_sampling.sh. A log file called fi_breakdown.txt is generated.
Copy the log file fi_breakdown.txt to the ./test folder. 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/Trident folder. For example for blackscholes bench mark use the command python 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.results after 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.
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.txt for each input as input_n-fi.txt where n is 0 and increments for each input.
Place all the produced input_n-fi.txt files in ./test/vTrident/data directory.
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/vTrident and 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/