Computing Concepts

Introduction

This page is intended for physicists who are just starting to work in the Mu2e computing environment. It explains a few jargon words in case this is your first exposure to computing high-energy physics. It supports ComputingTutorials

Events

Very briefly, the experiment is driven by a short burst of millions of protons hitting the primary target every 1695 ns. This cycle is properly called a proton pulse but the name microbunch is also frequently used. After the burst interacts, outgoing muons migrate to, and come to rest in, the stopping target. After this surge of particles dies down during the first part of the microbunch, the detector observes what happens to the stopped muons during the second part of the microbunch. The data recorded during this latter ~900 ns is written out (if it passes the trigger) in a data structure called an event. Many events can be written in one file. Events have unique identifying numbers. Short periods of data-taking (minutes) are grouped into subruns with a unique ID number, and longer periods of stable running (a few hours) will be grouped into a run with unique ID number.

EventIDs

Mu2e identifies each event using an EventID that is a 3-tuple of non-negative integers; the parts of the tuple are named run number, subrun number and event number. The Mu2e data acquisition system is designed to generate EventIDs that are monotonically increasing in the time; specifically if a one proton pulse arrived at the production target earlier than another proton pulse, then the earlier proton pulse has the lower EventID.

Similarly a SubRunID is defined as a 2-tuple of non-negative integers with parts named run number and subrun number. And a RunID is just a non-negative integer. Both of these IDs are also monotonically increasing in time in the same sense as EventIDs.

Sim and Reco

Since we do not have data yet, we analyze simulation or sim events which are based on our expectation of what data will look like. We draw randomly from expected and potential physics processes and then trace the particles through the detector, and write out events. The interaction of particles in the detector materials is simulated with the geant software package. The simulated looks like the real data will, except it also contains the truth of what happened in the interactions.

The output of simulation would typically be data events in the raw formats that we will see produced by the detector readout. These are typically ADC values indicating energy deposited, or TDC values indicating the time of a energy deposit. In the reconstruction or reco process, we run this raw data through modules that analyze the raw data and look for patterns that can be identified as evidence of particular particles. For example, hits in the tracker are reconstructed into individual particle paths, and energy in the calorimeter crystals is clustered into showers caused by individual electrons. Exactly how a reconstruction module does its work is called its algorithm. A lot of the work of physicists is invested in these algorithms, because they are fundamental to the quality of the experimental results.

Coding

The Mu2e simulation and reconstruction code is written in c++. We write modules which create simulated data, or read data out of the event, process it, and write the results back into the event. The modules plug into a framework called art, and this framework calls the modules to do the actual work, as the framework reads an input file and writes an output file. The primary data format is determined by the framework, so it is called the art format and the file will have an extension .art.

We use the git code management system to store and version our code. Currently, we have one main git repository which contains all our simulation and reconstruction code. You can check out this repository, or a piece of it, and build it locally. In this local area you can make changes to code and read, write and analyze small amounts of data. We build the code with a make system called scons. The code may be built optimized (prof) or non-optimized and prepared for running a debugger (debug).

At certain times, the code is tagged, built, and published as a stable release. These releases are available on the /cvmfs disk area. cmfvs is a sophisticated distributed disk system with layers of servers and caches, but to us it just looks like a read-only local disk, which can be mounted almost anywhere. We often run large projects using these tagged releases. cmvfs is mounted on the interactive nodes, at remote institutions, on some desktops, and all the many farm nodes we use.

You can read more about accessing and building code, git, scons and cvmfs.

Executables

Which modules are run and how they are configured is determined by a control file, written in fcl (pronounced fickle). This control file can change the random seeds for the simulation and the input and output file names, for example. A typical run might be to create a new simulation file. For various reasons, we often do our simulation in several stages, writing out a file between each run of the executable, or stage, and reading it in to start the next stage. A second type of job might be to run one of the simulation stages with a variation of the detector design, for example. Another typical run might be to take a simulation file as input and test various reconstruction algorithms, and write out reconstruction results.

Data products

The data in an event in a file is organized into data products. Examples of data products include straw tracker hits, tracks, or clusters in the calorimeter. The fcl is often used to decide which data products to read, which one to make, and which ones to write out. There are data products which contain the information of what happened during the simulation, such as the main particle list, SimParticles.

UPS Products

Disambiguation of "products" - please note that we have both data products and UPS products which unfortunately are both referred to as "products" at times. Please be aware of the difference, which you can usually determine from the context.

The art framework and fcl control language are provided as a product inside the UPS software release management system. There are several other important UPS products we use. This software is distributed as UPS products because many experiments at the lab use these utilities. You can control which UPS products are available to you ( which you can recognize as a setup command like "setup root v6_06_08") but most of this is organized as defaults inside of a setup script.

You can read more about how UPS works.

Histogramming

Once you have an art file, how to actually make plots and histograms of the data? There are many ways to do this, so it is important to consult with the people you work with, and make sure you are working in a style that is consistent with their expertise and preferences, so you can work together effectively.

In any case, we always use the root UPS product for making and viewing histograms. There are two main ways to approach it. The first is to insert the histogram code into a module and write out a file which contains the histograms. The second method is to use a module to write out an ntuple, also called a tree. This is a summary of the data in each event, so instead of writing out the whole track data product, you might just write out the momentum and the number of hits in the nutple. The ntuple is very compact, so you can easily open this and make histogram interactively very quickly.

Read more about ways to histogram or ntuple data for analysis.

Workflows

Designing larger jobs

After understanding data on a small level by running interactive jobs, you may want to run on larger datasets. If a job is working interactively, it is not too hard to take that workflow and adapt it for running on large datasets on the compute farms. First, you will need to understand the mu2egrid UPS product which is a set of scripts to help you submit jobs and organize the output. mu2egrid will call the jobsub UPS product to start your job on the farm. You data will be copied back using the ifdh UPS product, which is a wrapper to data transfer software. The output will go to dCache, which is a high-capacity and high-throughput distributed disk system. We have 100's of terabytes of disk space here, divided into three types (a scratch area, a persistent disk area, and a tape-backed area). Once the data is written, there are procedures to check it and optionally concatenate the files and write them tape. We track our files in a database that is part of the SAM UPS product. You can see the files in dCache by looking under the /pnfs filesystem. Writing and reading files to dCache can have consequences, so please understand how to use dCache and also consult with an experienced user before running a job that uses this disk space.

Grid resources

Mu2e has access to a compute farm at Fermilab, called Fermigrid. This farm is several thousand nodes and Mu2e is allocated a portion of the nodes (our dedicated nodes). Once you have used the interactive machines to build and test your code, you can submit a large job to the compute farms. You can get typically get 1000 nodes for a day before your priority goes down and you get fewer. If the farm is not crowded, which is not uncommon, you can get several times that by running on the idle or opportunistic nodes.

Mu2e also has access to compute farms at other institutions through a collaboration called Open Science Grid (OSG). It is easy to modify your submit command to use these resources. We do not have a quota here, we can only access opportunistic nodes, so we don't really know how many nodes we can get, but it is usually at least as much as we can get on Fermigrid. This system is less reliable than Fermigrid so we often see unusual failure modes or jobs restarting.

Your workflow

Hopefully you now have a good idea of the concepts and terminology of the Mu2e offline. What part of the offline system you will need to be familiar with will depend on what tasks you will be doing. Let's identify four nominal roles. In all cases, you will need to understand the accounts and authentication.

1. ntuple user. This is the simplest case. You probably will be given a ntuple, or a simple recipe to make an ntuple, then you will want to analyze the contents. You will need to have a good understanding of c++ and root, but not much else.
2. art user. In this level you would be running art executables, so you will also need to understand modules, fcl, and data products. Probably also how to make histograms or ntuples from the art file.
3. farm user. In this level you would be running art executables on the compute farms, so you will also need to understand the farms, workflows, dCache, and possibly uploading files to tape.
4. developer. In this case, you will be writing modules and algorithms, so you need to understand the art framework, data products, geometry, c++, and standards in some detail, as well as the detector itself.