Date Tags C / python

A few days ago I wrote about a few differences between using C and python to handle a process-based job management system. My discussion covered performance, development time, dependencies, and portability. I ended with the conclusion that despite C being vastly superior in performance, the relative difference in performance between various job management systems is irrelevant when considering the job processing time. Therefore, I recommended using python for such a task due to faster development times, built-in support for multiprocessing (no need for third-party libraries), and the portability one immediately gains by using python.

In spite of my continuing recommendation to use python for such a task, I was curious what a C solution might look like. Thus, I set out to write, and did write, a simple process-based job management system in C that offers similar functionality as python’s multiprocessing.Pool class. Take note that writing this system required more time than the equivalent python code would have, however, the point is moot because python has the multiprocessing.Pool class. Furthermore, whoever wants to use this system must manually include it in their project, thus introducing a third-party dependency in their code. Finally, this code will not compile in the typical Windows development environment as it relies on POSIX-based system calls. Hence, for all the reasons I previously mentioned, this C implementation is quite inferior to python’s multiprocessing.Pool class.

The source that I reference throughout the remainder of this post can be found and downloaded in entirety. On a POSIX-based machine the code can be compiled via gcc main.c job_management.c and the test program can be run via ./a.out.

The system, or library, is contained in two files: job_management.h and job_management.c. Anyone familiar with C or C++ development knows that job_management.h is a header file which contains the structures and function prototypes whereas job_management.c contains the implementation. The entire library functionality is exposed through three functions: manager_init, manager_run_jobs, and manager_destruct.

void manager_init(struct manager *manager, int num_workers,
          void (*input_callback)(char *),
          void (*output_callback)(char *),
          void (*work_function)(char *));

Aside from the pre-allocated manager structure that all the functions require as their first parameter, the manager_init function requires four other parameters: num_workers, input_callback, output_callback, and work_function. The num_workers parameter specifies an upper bound on how many worker processes to start. Each of the remaining parameters are function pointers to functions that must each have a single char pointer parameter and return no value. I will describe the purpose of these functions in the order that they execute when considering only a single job.

The input_callback function is called by the master process, once per job, in order to fetch the input for the job. The callback function stores the job input in the provided buffer before returning. The work_function function is the reason for the entire system as this function performs the job processing. In this function, the provided buffer serves both as job input and job output. On invocation, the buffer contains the result of a prior call to input_callback which has been sent over a pipe between the master process and worker process. On return, the buffer should contain the job output which will then be sent back to the master process. The output_callback function is called by the master process as each job is completed. In this case, the buffer contains the output of a completed job.

int manager_run_jobs(struct manager *manager, int num_jobs);

Once the manager has been properly set up, a number of jobs can be started via manager_run_jobs which takes one additional argument, num_jobs. Intuitively this parameter represents the number of jobs to process. This function contains the most complicated piece of the system as it is responsible for forking the processes, setting up the pipes between the master and workers, cleaning up the address space and file descriptor table for the workers, and properly using I/O multiplexing in order to both send job input and receive job output.

One additional complication that the manger_run_jobs function has to handle is the issue that started this entire series of blog posts; that is, the want to gracefully exit the job management system when a keyboard interrupt (SIGINT), occurs. Gracefully exiting means any jobs that are running at the time of the keyboard interrupt will successfully complete prior to exiting, thus giving the master process the chance to save partial results. This function handles the desired behavior through the combination of a global volatile value that’s periodically checked within this function’s primary loop, and a SIGINT handler function that updates the value. Worker processes are unaffected by SIGINT as they are set to ignore it.

void manager_destruct(struct manager *manager);

The last function, manager_destruct, conveniently frees the contents of the manager structure. It is important to note that this function is also called by each worker as part of its address space and file descriptor table clean up. For this reason, the manager_destruct function handles the closing of some FILE structures. Otherwise, this function is incredibly simple.

As previously mentioned, my job_management library is simple and thus is no where near as flexible as python’s multiprocessing.Pool class. Furthermore, my library has no support for data serialization as it relies on messages being no larger than MAX_MESSAGE bytes in size, and has a limitation that the message contents span only a single line. These restrictions were chosen for simplicity and are, by no means, a limitation of C. However, again, I must point out that object serialization is provided by default with python, and is built into the multiprocess.Pool class.

Below is an example program that demonstrates using my process-based job management system. Note that this code really isn’t that complicated, especially when compared to the python solution that gracefully handles the keyboard interrupt. Of course, the python solution is without dependencies and much more portable. :)

Happy coding!


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