Giter VIP home page Giter VIP logo

intel_mpx_explained's Introduction

Intel MPX Explained

A repository containing complete experimental setup of our "Intel MPX Explained" paper:

  • raw_results: complete unprocessed measurements, in the form of .csv files.
  • asm_measurements: scripts used to measure MPX instruction latencies and throughputs. Also, a set of scripts that prove existence of contention on Port 1.
  • src: sources of the tested benchmark suits and case studies. (SPEC was excluded for licencing reasons)
  • experiments/exp_*_*/run.py: scripts defining the experiment procedure
  • experiments/makefiles/: build types
  • install: installation scripts

Running the experiments

The interface is the same for all the benchmarks:

./fex.py run -n benchamark_name -t build_type --stats measurement_tool

For example, to measure performance on Phoenix:

./fex.py run -n phoenix_perf -t gcc_native icc_native clang_native gcc_asan gcc_asan_only_write clang_asan gcc_mpx gcc_mpx_only_write gcc_mpx_no_narrow_bounds gcc_mpx_no_narrow_bounds_only_write icc_mpx icc_mpx_only_write icc_mpx_no_narrow_bounds icc_mpx_no_narrow_bounds_only_write softbound_native softbound_enabled safecode_native safecode_enabled --stats perf

For the details of how to run the experiments, refer to the documentation of the underlying Fex framework. Note that this repository uses a bit outdated version of Fex and some things may mismatch. In such occasions, please, create an issue or contact me directly.

Publications

Full description of this work can be found in one of the follwing:

Cite us!

Technical Report:

@Article{Oleksenko:2017,
  author = {Oleksenko, Oleksii and Kuvaiskii, Dmitrii and Bhatotia, Pramod and Felber, Pascal and Fetzer, Christof},,
  title = {{Intel MPX Explained: An Empirical Study of Intel MPX and Software-based Bounds Checking Approaches}},
  journal   = "",
  archivePrefix = "arXiv",
  eprint = {1702.00719},
  primaryClass = "",
  year = {2017},
}

intel_mpx_explained's People

Contributors

oleksiioleksenko avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.