Banking & Financial Audits through parallel computing DPC++ ( Process Millions of records in seconds)
Design scalable and flexible parallel data processing and audit systems with Intel DPC++.
We show you how you can utilize DPC++ to efficiently process millions of records in parallel in under 60 seconds .
Eg :Auditing interest paid to each user based on the users dynamic account balances .
Entry for codeproject.com and INTEL Devmesh DPC++
Copy the entire structure including all files to Intel dev cloud .
Ensure that the Python 3.7 (Intel OneApi) kernal is running
Ensure that you are using the q file ,run_audit.sh and Makefile that is provided with this sourcecode.
Ensure that file exist in lab/simple_interest_audit.cpp
Run the following jupyter notebook Interest-on-account-audit-onemillion_records.ipynb
! chmod 755 q; chmod 755 run_audit.sh;if [ -x "$(command -v qsub)" ]; then ./q run_audit.sh; else ./run_audit.sh; fi
This code can Run on both on CPU and GPU of below specs
if you select a GPU device than Device: Intel(R) Graphics Gen9 [0x3e96] will process in 4 to 6 seconds for this example
queue q(gpu_selector{});
if you select a CPU device than Device: Intel(R) Xeon(R) E-2176G CPU @ 3.70GHz will process in 4 to 9 seconds for this example
queue q(=cpu_selector{});
On successfull run you would have processed simple interest calculations on One million records at around 4 to 6 seconds
This same calulation in php or msql will take ages to complete :)
You should be able to download the file containing the out put from the left hand side in jupyter notebook named parallel_interest_on_account_audit.txt containing 1 million records at around 180 mb .
