The Road to the JIT

December 01, 2020 · by Björn Gustavsson

As long as Erlang has existed, there has always been the need and the ambition to make it faster. This blog post is a history lesson that outlines the major Erlang implementations and attempts to improve the performance of Erlang.

The Prolog interpreter #

The first version of Erlang was implemented in Prolog in 1986. That version of Erlang was too slow for creating real applications, but it was useful for finding out which features of the language were useful and which were not. New language features could be added or deleted in a matter of hours or days.

It soon became clear that Erlang needed to be at least 40 times faster to be useful in real projects.

JAM (Joe’s Abstract Machine) #

In 1989 JAM (Joe’s Abstract Machine) was first implemented. Mike Williams wrote the runtime system in C, Joe Armstrong wrote the compiler, and Robert Virding wrote the libraries.

JAM was 70 times faster than the Prolog interpreter, but it turned out that this still wasn’t fast enough.

TEAM (Turbo Erlang Abstract Machine) #

Bogumil (“Bogdan”) Hausman created TEAM (Turbo Erlang Abstract Machine). It compiled the Erlang code to C code, which was then compiled to native code using GCC.

It was significantly faster than JAM for small projects. Unfortunately, compilation was very slow, and the code size of the compiled code was too big to make it useful for large projects.

BEAM (Bogdan’s Erlang Abstract Machine) #

Bogumil Hausman’s next machine was called BEAM (Bogdan’s Erlang Abstract Machine). It was a hybrid machine that could execute both native code (translated via C) and threaded code with an interpreter. That allowed customers to compile their time-critical modules to native code and all other modules to threaded BEAM code. The threaded BEAM in itself was faster than JAM code.

Lessons Learned from BEAM/C #

The modern BEAM only has the interpreter. The ability of BEAM to generate C code was dropped in OTP R4. Why?

C is not a suitable target language for an Erlang compiler. The main reason is that an Erlang function can’t simply be translated to a C function because of Erlang’s process model. Each Erlang process must have its own stack and that stack cannot be automatically managed by the C compiler.

BEAM/C generated a single C function for each Erlang module. Local calls within the module were made by explicitly pushing the return address to the Erlang stack followed by a goto to the label of the called function. (Strictly speaking, the calling function stores the return address to BEAM register and the called function pushes that register to the stack.)

Calls to other modules were done similarly by using the GCC extension that makes it possible to take the address of a label and later jumping to it. Thus an external call was made by pushing the return address to the stack followed by a goto to the address of a label in another C function.

Isn’t that undefined behavior?

Yes, it is undefined behavior even in GCC. It happened to work with GCC on Sparc, but not on GCC for X86. A further complication was the embedded systems having ANSI-C compilers without any GCC extensions.

Because of that, we had to maintain three distinct flavors of BEAM/C to handle different C compilers and platforms. I don’t remember any benchmarks from that time, but it is unlikely that BEAM/C was faster than interpreted BEAM on any other platform than Solaris on Sparc.

In the end, we removed BEAM/C and optimized the interpreted BEAM so that it could beat BEAM/C in speed.

HiPE #

HiPE (The High-Performance Erlang Project) was a research project at Uppsala University running for many years starting around 1996. It was “aimed at efficiently implementing concurrent programming systems using message-passing in general and the concurrent functional language Erlang in particular”.

One of the many outcomes of the project was the HiPE native code compiler for Erlang. HiPE became a part of the OTP distribution in OTP R8 in 2001. The HiPE native compiler is written in Erlang and translates the BEAM code to native code without the help of a C compiler, therefore avoiding many of the problems that BEAM/C ran into.

The HiPE native compiler can often speed up sequential code by a factor of two or three compared to interpreted BEAM code. We hoped that would speed up real-world huge application systems. Unfortunately, projects within Ericsson that tried HiPE found that it did not improve performance.

Why is that?

The main reason is probably that most huge Erlang applications don’t contain enough sequential code that HiPE could optimize. The runtime of those systems is typically dominated by some combination of message passing, calls to the ETS BIFs, and garbage collection, none of which HiPE can optimize.

Another reason could be that big systems typically have many small modules. The HiPE native compiler (in common with the Erlang compiler) cannot optimize code across module boundaries, thus being unable to do much type-based optimizations.

Also, for most big systems, compiling all Erlang modules to native code would lead to impractically long build times and the resulting code would consume too much memory. There is a small overhead of switching from native code to interpreted BEAM and vice versa. It is a non-trivial task to figure out which modules that would gain from being compiled to native code, and at the same time avoiding an excessive amount of context switches between native and interpreted code.

Because none of the Ericsson Erlang projects used the HiPE native compiler, the OTP team could only afford to spend a limited amount of time maintaining HiPE. Therefore, the documentation for HiPE includes this note:

HiPE and execution of HiPE compiled code only have limited support by the OTP team at Ericsson. The OTP team only does limited maintenance of HiPE and does not actively develop HiPE. HiPE is mainly supported by the HiPE team at Uppsala University.

Other Outcomes from the HiPE Project #

I think it is fair to say that Erlang/OTP would look very different today if it hasn’t been for the HiPE project. Here are the major contributions from the HiPE project to OTP:

  • A new staged tag scheme in OTP R7. The new tag scheme allowed the Erlang system to address the full 4GB address space (the previous tag scheme only supported addressing the lower 1 GB). Surprisingly, the new tag scheme also improved performance.

  • The Core Erlang intermediate representation is used in the Erlang compiler to this day. For more information, see An introduction to Core Erlang and Core Erlang by Example.

  • Dialyzer (DIscrepancy AnaLYZer for ERlang programs), started out as a type analysis pass for the HiPE native compiler, but soon become a tool for Erlang programmers to help find bugs and unreachable code in their applications.

  • Bit strings and binary comprehensions.

  • Introducing trycatch in OTP R10.

  • Implementing per function counters and the cprof module. The counters were originally meant to be used for finding hot functions and generating native code only for these. But the overhead in the context switch between interpreted and native code made this usage less useful.

  • Repeatedly suggesting that Erlang needed a literal pool for premade literal terms (instead of constructing them each time they are used). At one of our meetings between the HiPE team and the OTP team, I remember Richard Carlsson pointing out to me that it would be nice for Wings3D to have floating-point literals. The OTP team implemented literal pools in OTP R12.

The Tracing JIT projects (BEAMJIT) #

There have been three separate research projects that tried to develop a tracing JIT for Erlang. All of them have been led by Frej Drejhammar of RISE (formerly SICS).

A tracing JIT (Just In Time compiler) is a JIT that runs in two phases:

  • First it traces execution to find sequences of hot (frequently executed) code.

  • It then rewrites the found traces to native code.

The goals for the three JIT projects were:

  • The JIT should work automatically with no need for the user to identify which modules to compile to native code beforehand.

  • There should be total feature compatibility with the non-JIT BEAM. In particular, tracing, scheduling behavior, save calls, and hot code reloading should continue to work, and stack traces should be identical to the ones in the non-JIT BEAM.

  • The system should at least on average never be slower than the non-JIT BEAM.

There were some promising results when running some benchmarks, but ultimately it turned out to be impossible to fulfill the goal to never be slower than the non-JIT system. Here are the main reasons for the slowdowns:

  • To do the tracing (finding hot code), the BEAM interpreter needed tweaking. It was difficult to be able to do tracing without lowering the base speed of the BEAM interpreter.

  • It was also difficult to design the mechanism for context switching between the interpreted code and native code in a way that didn’t lower the base speed of the BEAM interpreter.

  • When a hot sequence of code has been found, the code needed to be compiled to native code. The compilation, that used LLVM, was slow.

  • When a hot sequence had finally been converted to native code, it could turn out that it would not be executed again. That was particularly a problem for the Erlang compiler that runs many passes. Typically, when some of the code for one pass had been converted to native code, the compiler was already running the next pass.

The later projects mitigated some of the issues in the previous projects. For example, the compilation time was reduced by doing more optimizations before invoking LLVM. Ultimately, though, it was decided to terminate the third and final tracing JIT project at the end of 2019.

For more information about BEAMJIT, see:

The new JIT (also known as BeamAsm) #

After the end of the third tracing JIT project, Lukas Larsson, having been involved in the last two tracing JIT projects, could not stop thinking about different approaches that might lead to a useful JIT. The things that slowed down the previous approaches were the tracing to find hot code and the generation of optimized native code using LLVM. Would it be possible to have a simpler JIT that didn’t do tracing and did no or little optimization?

In January 2020, salvaging some code from the third tracing JIT project, Lukas quickly built a prototype BEAM system that translated each BEAM instruction at load time to native code. The resulting code was less optimized than LLVM-generated code because it would still use BEAM’s stack and X registers (stored in memory), but the overhead for instruction unpacking and instruction dispatch was eliminated.

The initial benchmarks results were promising: about twice as fast compared to interpreted BEAM code, so Lukas extended the prototype so that it could handle more kinds of BEAM instructions.

John Högberg quickly became interested in the project and started to act as a sounding board. Some time later, probably in March, John suggested that the new JIT should translate all loaded code to native code. That way, there would be no need to support context switching between the BEAM interpreter and native code, which would make the design simpler and eliminate the cost for context switches.

That was a gamble, of course. After all, it could turn out that the native code could be too large to be practically useful or decrease performance because it fitted badly in the code cache. They decided that it was worth taking the risk and that it would probably be possible to optimize the size of the code later. (Spoiler: At the time of writing, the native code generated by the JIT is about 10 percent larger than interpreted BEAM code.)

Another change to the design was the tooling for generating the native code. In Lukas’s prototype, the native code template for each instruction was contained in text files similar to the other files used by the loader. That was inflexible, so it was decided to use some library that could generate native code. While some pure C libraries could have been used, the C++ library AsmJIT was more convenient in practical use than any of the C libraries. Also, some C libraries were excluded because they used a GNU license, which we can’t use in OTP. Therefore the part of the loader that translates BEAM instructions to native code needed to be written in C++, but the rest of the runtime system is still pure C code and will remain so.

John joined the practical work on the rejigged JIT project at the end of March.

On April 7, 2020, John reached the “prompt beer” milestone.

Prompt Beer #

When the Erlang system is started, a surprisingly large amount of code is executed before the prompt appears. On the one hand, that means that the translation of many instructions needs to be implemented before it would be possible to even start the Erlang system, let alone run any test suites or benchmarks.

On the other hand, when the prompt finally appears, it is a major milestone worth celebrating with some prompt beer or other appropriate beverage or by taking the rest of the evening off.

Maturing the new JIT #

On April 14 John got Dialyzer running with the JIT, and on April 17, after some improvements to the code generation, Dialyzer was only about 10 percent slower with the JIT than with HiPE. None of the tracing JITs had had any success in speeding up Dialyzer. (At the time of writing, Dialyzer runs roughly as fast with the JIT as it did with HiPE, although it has become increasingly difficult to do a fair comparison since HiPE doesn’t work beyond OTP 23.)

It was probably at that point we realized that we had a JIT that could finally be included in an OTP release.

The next major milestone was reached on May 6 when line numbers in stack traces were implemented. That meant that many more test cases now succeeded.

Soon after that, all test suites could be run successfully. During the summer and early fall Dan and I joined the project part-time and the following was done:

  • A major refactoring of the BEAM loader so that as much code as possible could be shared between the JIT and the BEAM interpreter. (The BEAM interpreter is only used on platforms that don’t support the JIT.)

  • Implementation and polishing of important but less used features such as tracing, and perf support, and save calls (see the flag save_calls for process_flag/2).

  • Shrinking of the code size of the generated native code.

  • Porting the JIT to Windows, which turned out to be relatively easy.

  • Making it possible to use the native stack pointer register and stack manipulation instructions. That improved perf support and slightly reduced the size of the native code.

The work culminated in a public pull request that Lukas created during his presentation of the new JIT on September 11.

The pull request was merged on September 22.

The Future #

Here are a few of the improvements that we have been thinking of for future releases:

  • Supporting ARM-64 (used by Raspberry Pi and Apple’s new Macs with Apple Silicon).

  • Implementing type-guided generation of native code. The new SSA-based compiler passes introduced in OTP 22 does a sophisticated type analysis. Frustratingly, not all of the type information can be leveraged to generate better code for the interpreted BEAM. We plan to modify the compiler so that some of the type information will be included in the BEAM files and then used by the JIT during code generation.

  • Introducing new instructions for binary matching and/or construction to help the JIT generate better code.