Archive of posts from 2015

• Dec 8, 2015: Add Graal JIT Compilation to Your JVM Language in 5 Easy Steps, Step 5

  Step 5: Optimizing the Interpreter for Compilation

• Dec 1, 2015: Add Graal JIT Compilation to Your JVM Language in 5 Easy Steps, Step 4

  Step 4: Complete Support for Mandelbrot

• Nov 24, 2015: Add Graal JIT Compilation to Your JVM Language in 5 Easy Steps, Step 3

  Step 3: Interpreting a Simple Fibonacci Function with Golo+Truffle

• Nov 17, 2015: Add Graal JIT Compilation to Your JVM Language in 5 Easy Steps, Step 2

  Step 2: Adding Bit Operations To Golo

• Nov 10, 2015: Add Graal JIT Compilation to Your JVM Language in 5 Easy Steps, Step 1

  Over the course of the next four weeks, I plan to publish a new post every Tuesday to give a detailed introduction on how to use the Graal compiler and the Truffle framework to build fast languages. And this is the very first post to setup this series. The next posts are going to provide a bit of background on Golo, the language we are experimenting with, then build up the basic interpreter for executing a simple Fibonacci and later a Mandelbrot computation. To round off the series, we will also discuss how to use one of the tools that come with Graal to optimize the performance of an interpreter. But for today, let’s start with the basics.

• Oct 23, 2015: JIT Data Structures, Fully Reflective VMs, and Meta-Circular Meta-Tracing

  The year leading up to SPLASH has been pretty busy. Beside my own talks on Tracing vs. Partial Evaluation and Optimizing Communicating Event-Loop Languages with Truffle, there are going to be three other presentations on work I was involved in.

• Oct 21, 2015: Optimizing Communicating Event-Loop Languages with Truffle

  The past few month, I have been busy implementing a fast actor language for the JVM. The language is essentially Newspeak with a smaller class library and without proving access to the underlying platform, which can lead to violations of the language’s guarantees.

• Oct 19, 2015: Tracing vs. Partial Evaluation: Comparing Meta-Compilation Approaches for Self-Optimizing Interpreters

  Back in 2013 when looking for a way to show that my ideas on how to support concurrency in VMs are practical, I started to look into meta-compilation techniques. Truffle and RPython are the two most promising systems to build fast language implementations without having to implement a compiler on my own. While these two approaches have many similarities, from a conceptual perspective, they take two different approaches that can be seen as the opposite ends of a spectrum. So, I thought, it might be worthwhile to investigate them a little closer.

• Sep 11, 2015: Why Is Concurrent Programming Hard? And What Can We Do about It? #vmm2015

  Yesterday at the Virtual Machine Meetup, I was giving a talk about why I think concurrent programming is hard, and what we can do about it.

• Apr 28, 2015: Zero-Overhead Metaprogramming

  Runtime metaprogramming and reflection are slow. That’s a common wisdom. Unfortunately. Using refection for instance with Java’s reflection API, its dynamic proxies, Ruby’s #send or #method_missing, PHP’s magic methods such as __call, Python’s __getattr__, C#’s DynamicObjects, or really any metaprogramming abstraction in modern languages unfortunately comes at a price. The fewest language implementations optimize these operations. For instance, on Java’s HotSpot VM, reflective method invocation and dynamic proxies have an overhead of 6-7x compared to direct operations.

• Jan 31, 2015: FOSDEM 2015: Building High-Performance Language Implementations With Low Effort

  Today, I gave a talk on implementing languages based on the ideas behind RPython and Truffle at FOSDEM on the main track. Please find abstract and slides below.

• Jan 27, 2015: Partitioned Global Address Space Languages

  More than a decade ago, programmer productivity was identified as one of the main hurdles for future parallel systems. The so-called Partitioned Global Address Space (PGAS) languages try to improve productivity and explore a range of language design ideas. These PGAS languages are designed for large-scale high-performance parallel programming and provide the notion of a globally shared address space, while exposing the notion of explicit locality on the language level. Even so the main focus is high-performance computing, the language ideas are also relevant for the parallel and concurrent programming world in general.

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