The Accelerator's Build System
This post describes the basics of the Accelerator’s build system, how it works, why it is designed the way it is, and how it helps to automate your work while reducing both execution time and probability of error.
This is an overview article. Please see the Accelerator’s User’s Reference for details.
The Accelerator Job in Brief
The job is the atomic unit of program execution on the Accelerator. Jobs are powerful, they can execute parallel code, read and store large amounts of data very efficiently, and much more. In fact, the job is a topic of its own, and we will not go into details here. See references for more information.
The things we need to know about jobs at this point relates to code execution. “Building a job” means executing a piece of code. When a job has completed execution, all information associated with it is stored in a job specific directory to be used at any time. The returned job instance contains convenience functions that wraps and operates on the job data.
Job Build is Conditional
A job is only built if it has not been built before, see figure below.
A job build operation always return a job instance. The job instance is returned immediately if it already exists. Otherwise, it is returned as soon as it finishes execution.
To know if the job has been built before, the Accelerator keeps a database containing information on all successfully completed build requests. This information includes a hash digest of each job’s source code, and is sufficient to uniquely define any job. Each build request is compared to this database.
Inputs to the Build Call
There are three types of input to a job at build time:
- the program source code,
- references to input data, and
- input parameters (i.e. options).
All of these are used to check if a job has been built already.
Job Building Process
When a job is being built, the following happens:
- A new job directory is created.
- Everything needed to execute the job is copied into the job directory.
- New processes are forked that learns what to do by reading the information stored in the job directory.
- The new job is executing and output is stored in the job directory.
- When the job completes, additional profiling information is stored in the job directory.
Thus, everything related to a job be found in its job directory. For this reason, jobs are transparent and easily observable.
A complex processing task can be divided into a number of different jobs, making the task easier to write, test, and maintain. A job reference can be used as input parameter to another job, making it straightforward to have jobs depend on each other. Using job references it is possible to build any kind of directed job flow graph to solve complex problems, see example below.
In code, it may look like this
def main(urd): job1 = urd.build('method1') job2 = urd.build('method2', pred=job1) job3 = urd.build('method3', pred=job2) job4 = urd.build('method4', pred=job1) job5 = urd.build('method5', pred=job1) job6 = urd.build('method6', pred1=job4, pred2=job5) job7 = urd.build('method7', pred1=job3, pred2=job6)
The build system is somewhat similar to traditional “Makefiles”, with the big difference that all previous “makes” are stored in job directories to be fetched immediately when called for. This implies, for example, that a developer can switch back and forth between different versions, or input data for that matter, without re-executing any code.
Why this Build System?
The Accelerator’s build system is designed for reproducibility. The same input to the same program should lead to the same output – always. There is no need to build a job that shares the same inputs and source code as a job that is already built. Its output will be the same as the existing job. If nothing has been changed since the last run, old results could be looked up and returned immediately.
Reproducibility is key, and the build system brings even more important features that we’ll describe in the following sections.
Observability and Transparency
All Information About a Job is Stored Together in a Single Directory. Having all results, source code, parameters, and profiling information in one place is great for observability and transparency. Looking into a job directory makes it totally clear which program, parameter set, and input data that was used to generate a particular result.
Saving Execution Time
Completed jobs will be re-used if possible. This saves time and energy.
Also, a change in the source code will only affect the current job and the following jobs depending on it. Only parts that are affected by the change will be re-executed, and not the whole flow graph, which again saves time.
Reproducibility and Transparency
The build system guarantees that parts affected by a modification will be re-executed. Assuming the build script has been run after a modification, the output will be up to date with the modification. There is never any question about which input data and which source code that has been used to compute any result.
The Accelerator is designed both for various analysis tasks and to run as a live production system. The build script is an efficient way to record sequences of tasks that can be carried out automatically with a minimum of unneccessary re-executions.
A good development strategy is to design incrementally, adding one job after the other that share intermediate results. This fits perfectly with the Accelerator’s build system. At the current development point, all previous jobs have been built already, so execution is limited to the current part, while its inputs are automatically fetched directly from previous jobs. This is efficient and simple. No unnecessary code is executed and no time is spent manually book-keeping intermediate results.
There are several benefits with the Accelerator’s build system, of which the most important is that it brings reproducibility and transparency. This property typically also leads to reduced execution time. We are not likely to mess up combinations of input data, source code, and results, since the connections are automatic by design. No manual book-keeping is required. To this we should add that jobs provide efficient parallel processing as well as high speed data storage and retrieval capabilities, but these are topics for other posts.
The Accelerator’s Homepage (exax.org)
The Accelerator on Github/exaxorg
The Accelerator on PyPI