LNT’s data model is pretty simple, and just following the Quickstart Guide can get you going with performance testing. Moving beyond that, it is useful to have an understanding of some of the core concepts in LNT. This can help you get the most out of LNT.
Orders Machines and Tests¶
LNT’s data model was designed to track the performance of a system in many configurations over its evolution. In LNT, an Order is the x-axis of your performance graphs. It is the thing that is changing. Examples of common orders are software versions, Subversion revisions, and time stamps. Orders can also be used to represent treatments, such as a/b. You can put anything you want into LNT as an order, as long as it can be sorted by Python’s sort function.
A Machine in LNT is the logical bucket which results are categorized by. Comparing results from the same machine is easy, across machines is harder. Sometimes machine can literally be a machine, but more abstractly, it can be any configuration you are interested in tracking. For example, to store results from an Arm test machine, you could have a machine call “ArmMachine”; but, you may want to break machines up further for example “ArmMachine-Release” “ArmMachine-Debug”, when you compile the thing you want to test in two modes. When doing testing of LLVM, we often string all the useful parameters of the configuration into one machines name:
Tests are benchmarks, the things you are actually testing.
Runs and Samples¶
Samples are the actual data points LNT collects. Samples have a value, and belong to a metric, for example a 4.00 second (value) compile time (metric). Runs are the unit in which data is submitted. A Run represents one run through a set of tests. A run has a Order which it was run on, a Machine it ran on, and a set of Tests that were run, and for each Test one or more samples. For example, a run on ArmMachine at Order r1234 might have two Tests, test-a which had 4.0 compile time and 3.5 and 3.6 execution times and test-b which just has a 5.0 execution time. As new runs are submitted with later orders (r1235, r1236), LNT will start tracking the per-machine, per-test, per-metric performance of each order. This is how LNT tracks performance over the evolution of your code.
LNT uses the idea of a Test Suite to control what metrics are collected. Simply, the test suite acts as a definition of the data that should be stored about the tests that are being run. LNT currently comes with two default test suites. The Nightly Test Suite (NTS) (which is run far more often than nightly now), collects 6 metrics per test: compile time, compile status, execution time, execution status, score and size. The Compile (compile) Test Suite, is focused on metrics for compile quality: wall, system and user compile time, compile memory usage and code size. Other test suites can be added to LNT if these sets of metrics don’t match your needs.
Any program can submit results data to LNT, and specify any test suite. The data format is a simple JSON file, and that file needs to be submitted to the server using either the lnt import or submit commands, see The lnt Tool, or HTTP POSTed to the submitRun URL.
The most common program to submit data to LNT is the LNT client application
lnt runtest nt command can run the LLVM test suite, and submit
data under the NTS test suite. Likewise the
lnt runtest compile command
can run a set of compile time benchmarks and submit to the Compile test suite.