If at all possible, please read through this entire document before installing or using Loupe. This document is best read on GitHub, with a Markdown viewer, or Markdown editor.

TODO: We can get from the automatic build all the binaries being buit, and such we could pass them to BINARY without user input.

Solution: It is likely that the program is crashing during performance analysis, and that Loupe is consequently unable to measure the peak process memory usage. You should assume that those system calls cannot be faked or stubbed.

Loupe is the result of a collaboration between The University of Manchester, Liège Université, University Politehnica of Bucharest, and Unikraft.io. The paper describing Loupe has been accepted to appear in ASPLOS'24.

To contribute to this repository, please fork and submit a Pull-Request. If you introduce a new file, make sure to add an SPDX license header. If you do significant-enough changes, consider adding yourself to COPYING.md.

To get an optimized order of syscall implementation/faking/stubbing for a given OS (characterized by a set of already-supported system calls) towards a particular set of applications:

Loupe has been tested on Debian 12. While this is not a hard requirement (it will likely work on any Linux distribution), we have not tested other environments. Please do report an issue if Loupe does not run on your Linux-based system.

As a user, you do not need to understand this format; loupe search takes care of analyzing the data in the database for you.

This artifact is the first release of a research proof-of-concept for Loupe. Like any research prototype, it contains hacks, bugs, and TODOs. Please use it with a critical eye. We hope that it will be useful!

Step 2: write a test script that parses the output of the test suite and determines whether it is a success or a failure. It is generally a very simple task: run it once manually, and search for a string that indicates success.

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Solution: We likely forgot to add ARG DEBIAN_FRONTEND=noninteractive in the Dockerfile. Try to add it, it should address the issue.

This artifact would not exist without the infrastructure and hard work of the Unikraft community. We encourage interested researchers to visit the project's web page and GitHub.

In addition to this repository, we have archived this artifact on Zenodo. In order to make the Zenodo artifact as self-contained as possible, we included a copy of the Loupe database along with this repository. These are provided for the main purpose of archival. You can generate a new snapshot with make zenodo.

This is a very simple script. We first start a 3s benchmark on port 80 with wrk. Then we parse the output for a chain of characters that typically indicates success (here the throughput, which is absent if the benchmark fails). We return 0 if it is present, and 1 otherwise.

We tagged both repositories with asplos24-ae-v1 before submission. Future iterations on the artifact will be tagged with -v2, etc.

We further study our measurements and extract several novel key insights. Overall, we show that the burden of building compatibility layers is significantly less than what previous works suggest: in some cases, only as few as 20% of system calls reported by static analysis, and 50% of those reported by naive dynamic analysis need an implementation for an application to successfully run standard benchmarks.

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Step 2: write a test script that uses wrk. Test scripts are used by Loupe to determine whether or not the application (here Nginx) works. Test scripts return 0 if the application works, 1 if it doesn't.

Loupe can do primarily two things: (1) collect data about the system call usage of your application(s), and (2) analyze the data collected for your application(s). It can tell you what system calls you need to run them, and visualize these numbers in a variety of plots.

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Step 1: Add support to the test script. The test script may be passed an argument. If the value of the argument is benchmark, then the script is asked to output a performance number to stdout. If it doesn't, the performance evaluation will fail. Taking the previous example with Nginx and wrk, we obtain:

Once the analysis completed, you can check the database (loupedb in the previous examples). You will see a number of uncommitted changes - they are the results of the analysis. The database is formatted in a custom text-based layout documented here.

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In our case, the Dockerfile looks like the following (slightly modified to exclude coverage, which is not necessary here):

This is a list of known issues, along with their solution. If your issue is not in this list, feel free to open a bug report and contribute a fix. This README is not exhaustive, before opening a bug report, please check the help feature of the relevant binary or script with -h.

This is a simple container. We install wrk, and configure and build Nginx. Finally, we start explore.py. The arguments are quite important:

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In practice, you likely want to write your Docker containers a little bit more carefully to ensure that the analysis remains stable and reproducible over time. We provide recommendations in GOOD_DOCKERFILES.md.

Loupe is based on dynamic analysis, but it is also able to gather static analysis data. We put the emphasis on reproducible analysis: measurements are made in a Docker container.

Loupe considers two types of workloads: benchmarks and test suites. Both measurements are independent. In this example, we'll illustrate both for Nginx.

Solution: Although infrequently happening, this is a known issue. The reason varies; it can be due to the Loupe code having changed since the run was performed, to the Docker container not being perfectly reproducible (e.g., versions of the application, shared libraries not being fixed properly), to the kernel having changed, etc.

4Flute End Mill

Issue 6: I cannot reproduce certain measurements, despite of running the reproduce command as instructed in the README.md.

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Solution: This is a known issue - Loupe is not redirecting the output of all children to /dev/null during the analysis. You can safely ignore this message.

Step 3: Enter the container and generate coverage. It's as simple as running the instrumented software. For example, with Nginx:

We provide a script that integrates Loupe with debhelper. Our script automatically downloads the debian sources of a package, builds it and then runs the test suite with loupe (if supported by the package). We use dh_test_auto to run the testsuite. dh_test_auto returns 0 if the test suite executed successfully.

... iron cutting. Page 8. Insert Grades. A. A8. Chipbreaker Comparison Chart ... Realises high dimensional accuracy for continuous steel machining or finishing of ...

You may be wondering what portion of the code a particular workload is exercising under a full Loupe analysis. Here is how to obtain a precise report.

Loupe stores analysis results in a custom database. A Loupe database is nothing more than a git repository with a particular layout. We offer an online, open database maintained by the community. Feel free to pull request your analysis results!

We don't need to perform the actual system call measurement, we only need the container to be built, so we pass --only-build-docker.

Loupe has been developed over a span of 3 years, during which we generated various results, not all taking advantage of the full set of features that Loupe offers in its current state.

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2 flute end millvs 4flute

Abstract: Supporting mainstream applications is fundamental for a new OS to have impact, and is generally achieved by developing a layer of compatibility such that applications developed for a mainstream OS like Linux can work, unmodified, on the new OS. Building such a layer, as we will show, results in large inefficiencies in terms of engineering effort due to the lack of efficient methods to precisely measure the OS features required by a set of applications.

Step 3: create a Docker container that build nginx and wrk, and performs the system call analysis using Loupe. Use loupe-base as basis for your Docker container.

2 flute end Millfor aluminum

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We propose Loupe, a novel method based on dynamic analysis that determines the OS features that need to be implemented in a prototype OS to bring support for a target set of applications and workloads. Loupe guides and boosts OS developers as they build compatibility layers, prioritizing which features to implement in order to quickly support many applications as early as possible. We apply our methodology to 100+ applications and several OSes currently under development, demonstrating high engineering effort savings vs. existing approaches: for example, for the 62 applications supported by the OSv kernel, we show that using Loupe, would have required implementing only 37 system calls vs. 92 for the non-systematic process followed by OSv developers.

Step 3: create a Docker container that build nginx and wrk, and performs the system call analysis using Loupe. Use loupe-base as basis for your Docker container.

Solution: The APT command is likely missing a apt-get update -q -y && prefix - however you do not necessarily need to modify the Docker container. You can simply rebuild the base container with make rebuild-docker and re-run Loupe.

Step 1: Check the Dockerfile of the application. If it already contains a version of the software built with coverage, then proceed to step 2. Otherwise, build the software (or a copy of it if performance measurements are planned) with CFLAGS -fprofile-arcs -ftest-coverage and LDFLAGS -lgcov. An example is visible here.

Step 1: identify the test suite. In many cases it will be in the same repository as the software itself, but the case of Nginx it is located in a separate repository.

Note that support is not integrated in the loupe main program. In order to run this, manually start the Docker contain built previously while running the analysis, and manually run:

Here, is a newline-separated list of syscalls already supported by the target OS (see the *.syscalls files at the root of loupedb for examples), is the list of application to target, and is bench or suite.