python的gpu编程_Lift 编程语言：高性能、可移植的函数式 GPU 编程语言

最新推荐文章于 2025-10-01 14:57:11 发布

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本文指导如何设置Lift编程环境，包括安装git、git-lfs、Java SDK，克隆Lift仓库，安装ArithExpr依赖，配置IntelliJ IDEA并安装Scala插件。此外，还详细说明了构建Executor库的步骤，并提供了调整JDK路径和环境变量的指南，以便运行OpenCL计算。最后，文章提及了执行测试和基准测试的方法。

The Lift language

Setup

Install git, git-lfs, dot and the latest Java SDK

Clone the "lift" repository to a directory of you choice

Install the ArithExpr dependency by running ./updateSubmodules.sh

Download & Install IDEA IntelliJ (http://www.jetbrains.com/idea/)

Launch IntelliJ. On the Welcome Screen select "Configure" and install the "Scala" Plugin form the Plugin Repository. After the installation restart IntelliJ.

On the IntelliJ Welcome Screen select "Open Project" and select the "build.sbt" file in the "lift" folder you just checked out.

To be able to execute computations in OpenCL you need to build the Executor library. The script buildExecutor.sh builds the executor.

./buildExecutor.sh

Both scripts are also called by sbt when running sbt compile

Finally add the Executor build directory to the Java library path:

In IntelliJ select "Run -> Edit Configurations ..."

On the left side select "Defaults -> JUnit"

On the right side add to the "VM options": "-Djava.library.path=$LIFT_ROOT/lib/Executor/build/" (with the proper path from your system)

Alternatively, you can add the Executor build directory to your LD_LIBRARY_PATH. That will also make it possible to run tests and benchmarks from the command line using "sbt test" and scripts in the "scripts" folder, respectively.

Documentation