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mirror of https://github.com/RPCS3/llvm-mirror.git synced 2024-10-19 11:02:59 +02:00
llvm-mirror/examples/Kaleidoscope/MCJIT/cached
Mehdi Amini ea195a382e Remove every uses of getGlobalContext() in LLVM (but the C API)
At the same time, fixes InstructionsTest::CastInst unittest: yes
you can leave the IR in an invalid state and exit when you don't
destroy the context (like the global one), no longer now.

This is the first part of http://reviews.llvm.org/D19094

From: Mehdi Amini <mehdi.amini@apple.com>
llvm-svn: 266379
2016-04-14 21:59:01 +00:00
..
genk-timing.py
README.txt
split-lib.py
toy-jit.cpp Remove every uses of getGlobalContext() in LLVM (but the C API) 2016-04-14 21:59:01 +00:00
toy.cpp Remove every uses of getGlobalContext() in LLVM (but the C API) 2016-04-14 21:59:01 +00:00

//===----------------------------------------------------------------------===/
//                          Kaleidoscope with MCJIT
//===----------------------------------------------------------------------===//

The files in this directory are meant to accompany the first in a series of
three blog posts that describe the process of porting the Kaleidoscope tutorial
to use the MCJIT execution engine instead of the older JIT engine.

When the blog post is ready this file will be updated with a link to the post.

The source code in this directory demonstrates the third version of the
program, now modified to accept an input IR file on the command line and,
optionally, to use a basic caching mechanism to store generated object images.

The toy-jit.cpp file contains a version of the original JIT-based source code
that has been modified to support the input IR file command line option.

This directory contain a Makefile that allow the code to be built in a
standalone manner, independent of the larger LLVM build infrastructure. To build
the program you will need to have 'clang++' and 'llvm-config' in your path. If
you attempt to build using the LLVM 3.3 release, some minor modifications will
be required.

This directory also contains a Python script that may be used to generate random
input for the program and test scripts to capture data for rough performance
comparisons.  Another Python script will split generated input files into
definitions and function calls for the purpose of testing the IR input and
caching facilities.