FTL: WebKit’s LLVM based JIT

Over the past year, the WebKit project made tremendous progress on the ability to optimize JavaScript applications. A major part of that effort was the introduction of the Fourth Tier LLVM (FTL) JIT. The Fourth Tier JIT targets long-running JavaScript content and performs a level of optimization beyond WebKit's interpreter, baseline JIT, and high-level optimizing JIT. See the FTL Optimization Strategy section below for more on WebKit's tiered optimizations. The engineering advancements within WebKit that made the FTL possible were described by Filip Pizlo in the Surfin' Safari Blog post, Introducing the WebKit FTL JIT. On April 29, 2014, the WebKit team enabled FTL by default on trunk: r167958.

This achievement also represents a significant milestone for the LLVM community. FTL makes it clear that LLVM can be used to accelerate a dynamically type checked languages in a competitive production environment. This in itself is a tremendous success story and shows the advantage of the highly modular and flexible design of LLVM. It is the first time that the LLVM infrastructure has supported self-modifying code, and the first time profile guided information has been used inside the LLVM JIT. Even though this project pioneered new territory for LLVM, it was in no way an academic exercise. To be successful, FTL must perform at least as well as non-FTL JavaScript engines in use today across a range of workloads without compromising reliability. This post describes the technical aspects of that accomplishment that relate to LLVM and future opportunities for LLVM to improve JIT compilation and the LLVM infrastructure overall.

Read on for more information.

NVIDIA CUDA 4.1 Compiler Now Built on LLVM

From the NVIDIA CUDA compiler team:

CUDA is a parallel programming model and platform created by NVIDIA for harnessing the power of hundreds of cores in modern graphics processing units (GPUs). NVIDIA provides free support for CUDA C and C++ in the CUDA toolkit. The CUDA programming environment consists of a compiler targeting NVIDIA GPUs and has been adopted by thousands of developers.

At NVIDIA we have switched over to using LLVM inside the CUDA C/C++ compiler for Fermi and future architectures. We use LLVM for optimizations and PTX code generation and for generating debug information for CUDA debugging. From a developer's perspective the new compiler is functionally on par with the previous compilers and produces better code with better compile times. We have extended the LLVM core compiler to understand data parallel programming model. It is now available, as part of CUDA 4.1 and you can learn more here.

Our experience with the use of LLVM has been very positive, starting with a modern compiler infrastructure and with high quality optimizations contributed by a large community of developers. The effort required to learn LLVM infrastructure is quite small and reasonable.

LLVM in ClamAV

New in version 0.96, the ClamAV antivirus system extends its internal bytecode interpreter to support an LLVM JIT compiler. This JIT compiler allows for improved execution speeds, but also provides the ability to write virus checks directly in C code. For more details, see the Sourcefire Vulnerability Research Team's Blog.

-Chris

TCE project: Co-design of application-specific processors with LLVM-based compilation support

TTA-based Codesign Environment (TCE) is an application-specific instruction-set processor (ASIP) design toolset developed in Tampere University of Technology in several research projects since 2003. This blog post introduces the project and how LLVM is used in it to provide high-level language compiler support for the designed ASIPs.

The Glasgow Haskell Compiler and LLVM

If you read the LLVM 2.7 release notes carefully you would have noticed that one of the new external users is the Glasgow Haskell Compiler (GHC). As the author of the LLVM backend for GHC, I have been invited to write a post detailing the design of the backend and my experiences with using LLVM. This is that post :).