Tag Archives: Programming

Cross-Language Compiler Benchmarking: Are We Fast Yet?

Research on programming languages is often more fun when we can use our own languages. However, for research on performance optimizations that can be a trap. In the end, we need to argue that what we did is comparable to state-of-the-art language implementations. Ideally, we are able to show that our own little language is not just a research toy, but that it is, at least performance-wise, competitive with for instance Java or JavaScript VMs.

Over the last couple of years, it was always a challenge for me to argue that SOM or SOMns are competitive. There were those 2-3 paragraphs in every paper that never felt quite as strong as they should be. And the main reason was that we don’t really have good benchmarks to compare across languages.

I hope we finally have reasonable benchmarks for exactly that purpose with our Are We Fast Yet? project. To track performance of benchmarks, we also set up a Codespeed site, which shows the various results. The preprint has already been online for a bit, but next week, we are finally going to present the work at the Dynamic Languages Symposium in Amsterdam.

Please find abstract and details below:


Comparing the performance of programming languages is difficult because they differ in many aspects including preferred programming abstractions, available frameworks, and their runtime systems. Nonetheless, the question about relative performance comes up repeatedly in the research community, industry, and wider audience of enthusiasts.

This paper presents 14 benchmarks and a novel methodology to assess the compiler effectiveness across language implementations. Using a set of common language abstractions, the benchmarks are implemented in Java, JavaScript, Ruby, Crystal, Newspeak, and Smalltalk. We show that the benchmarks exhibit a wide range of characteristics using language-agnostic metrics. Using four different languages on top of the same compiler, we show that the benchmarks perform similarly and therefore allow for a comparison of compiler effectiveness across languages. Based on anecdotes, we argue that these benchmarks help language implementers to identify performance bugs and optimization potential by comparing to other language implementations.

  • Cross-Language Compiler Benchmarking: Are We Fast Yet? Stefan Marr, Benoit Daloze, Hanspeter Mössenböck; In Proceedings of the 12th Symposium on Dynamic Languages (DLS ’16), ACM, 2016.
  • Paper: HTML, PDF, DOI
  • BibTex: BibSonomy

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.

The talk is very much related to an earlier blog post with the same title. My main point is that concurrent programming is hard because on the one hand there is not a single concurrency abstraction that fits all problems, and on the other hand the various different abstractions are rarely designed to be used in combination with each other. First work in that direction makes us hopeful that we can actually adapt interesting sets of concurrency abstractions to work together without causing combination issues as for instance unexpected deadlocks or data races.

The blog post, and the slide set below hopefully give a little more insights on what we got in mind.

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.

As part of our research in the field of parallelism and concurrency, we studied the PGAS languages more closely to get a better understanding of the specific concepts they explore and to get a feeling for the tradeoffs of the various language design options. The result is a survey of the major PGAS languages, which was very recently accepted for publication in the ACM Computing Surveys.

The preprint of the paper is available as PDF and HTML version. The final edited version will probably take another eternity to appear, but oh well, that’s academia.


The Partitioned Global Address Space (PGAS) model is a parallel programming model that aims to improve programmer productivity while at the same time aiming for high performance. The main premise of PGAS is that a globally shared address space improves productivity, but that a distinction between local and remote data accesses is required to allow performance optimizations and to support scalability on large-scale parallel architectures. To this end, PGAS preserves the global address space while embracing awareness of non-uniform communication costs.

Today, about a dozen languages exist that adhere to the PGAS model. This survey proposes a definition and a taxonomy along four axes: how parallelism is introduced, how the address space is partitioned, how data is distributed among the partitions and finally how data is accessed across partitions. Our taxonomy reveals that today’s PGAS languages focus on distributing regular data and distinguish only between local and remote data access cost, whereas the distribution of irregular data and the adoption of richer data access cost models remain open challenges.

  • Partitioned Global Address Space Languages; Mattias De Wael, Stefan Marr, Bruno De Fraine, Tom Van Cutsem, Wolfgang De Meuter; ACM Computing Surveys, to appear.
  • Paper: PDF, HTML
  • DOI: TBA
  • BibTex: BibSonomy

Fork/Join Parallelism in the Wild: Documenting Patterns and Anti-Patterns in Java Programs using the Fork/Join Framework

Parallel programming is frequently claimed to be hard and all kind of approaches have been proposed to solve the complexity issues. The Fork/Join programming style introduced with Cilk enables the parallel decomposition of problems in a recursive divide-and-conquer style, and on the surface looks very simple with its minimalistic approach of having a fork and a join language construct. But is it actually simple to use? To find out, Mattias started to dig through the Java open source projects on GitHub and tried to identify common patterns. Next week, he will present our findings at PPPJ’14.

The preprint of the paper is available below. Additionally, Mattias made the information on the corpus and how to obtain it available.


Now that multicore processors are commonplace, developing parallel software has escaped the confines of high-performance computing and enters the mainstream. The Fork/Join framework, for instance, is part of the standard Java platform since version 7. Fork/Join is a high-level parallel programming model advocated to make parallelizing recursive divide-and-conquer algorithms particularly easy. While, in theory, Fork/Join is a simple and effective technique to expose parallelism in applications, it has not been investigated before whether and how the technique is applied in practice. We therefore performed an empirical study on a corpus of 120 open source Java projects that use the framework for roughly 362 different tasks.

On the one hand, we confirm the frequent use of four best-practice patterns (Sequential Cutoff, Linked Subtasks, Leaf Tasks, and avoiding unnecessary forking) in actual projects. On the other hand, we also discovered three recurring anti-patterns that potentially limit parallel performance: sub-optimal use of Java collections when splitting tasks into subtasks as well as when merging the results of subtasks, and finally the inappropriate sharing of resources between tasks. We document these anti-patterns and study their impact on performance.

  • Fork/Join Parallelism in the Wild: Documenting Patterns and Anti-Patterns in Java Programs using the Fork/Join Framework; Mattias De Wael, Stefan Marr, Tom Van Cutsem; in ‘Proceedings of the 2014 International Conference on Principles and Practices of Programming on the Java Platform: Virtual Machines, Languages, and Tools’ , pp. 39-50.
  • Paper: PDF
  • BibTex: BibSonomy
  • Corpus and additional material: online appendix

Why is Concurrent Programming hard?

In short, I think, it is hard because on the one hand there is not a single concurrency abstraction that fits all problems, and on the other hand the various different abstractions are rarely designed to be used in combination with each other.

But let us start at the beginning. The terminology might get otherwise in the way. For the purpose of this discussion, I distinguish concurrent programming and parallel programming.

Concurrent programming and its corresponding programming abstractions focus on the correctness of a computation and the consistency of state in the context of parallel or interleaved execution.

Parallel programming and its corresponding programming abstractions focus on structuring an algorithm in a way that parallel computational resources are used efficiently.

Thus, while both programming approaches are related and are often used in combination, their goals and consequently their main abstractions are different. I am not claiming that parallel programming is solved and widely understood, but it is comparably easy to apply it to an isolated problem when performance is an issue. The emphasize here is on isolated, because the integration into an existing system is the hard part and can expose all kind of concurrency issues for which concurrent programming techniques are required as a solution.

When do Concurrency Programming Abstractions Break Down?

The first questions might be why and when are we using concurrent programming? The main reason is the desire to increase “performance”, either by increasing throughput, by reducing latency, or improving interactivity by moving operations off the critical path. Sometimes, a concurrent design also happens to map best on a problem by aligning the program structure with the domain model for instance in terms of tasks or processes and thus is chosen as solution.

A classic example calling for concurrent execution is user interfaces. Independent of a particular solution, the overall goal is to move a computational or I/O task out of the loop that processes user-generated events to maintain the interactivity of an application. This offloading can either be done by using some form of asynchronous I/O and computation library. To give but a few examples, C#’s async/await is frequently used for this purpose, as well as Java’s ExecutorService, or Clojure’s future.

In another simple scenario, the application is already parallel and some form of execution monitoring needs to be added. Either to stear optimizations or even for billing purposes. Depending on the concrete scenario, various solution approaches are available. In a non-performance-critical scenario, a simple atomically modified counter can be sufficient. When performance matters, it might be more appropriate to gather the initial counts local to a single thread, however, this requires later communication to build the sum of all thread-local values, which might lead to consistency issue because it is harder to get one globally consistent snapshot of all local counters. Depending on the requirements all kind of different solutions in-between could be devised. If the count itself for instance does not matter, a scalable non-zero indicator might suffice. Either way, the problem remains the same. An existing parallel program needs to be changed, which potentially introduces concurrency issues.

Keeping something like an independent counter consistent is however rather trivial compared to making parallel or concurrent operations on a large and complex shared data structure yield consistent and correct results. Imagine a tree or graph representation for a program as frequently employed by IDEs. In such a scenario various subsystems might want to change or annotate the graph. For instance to include inferred types, add test coverage, or history information, apply refactorings, or simply account for the change done by the user in the editor. Often the relevant subsystems work concurrently. One could of course the graph immutable and ‘updates’ produce strictly new versions of it. However, for various reasons other choices might be made and then the question arises how consistent updates are possible. Solutions could potentially include locks or software transactional memory (STM).

When making the decision for how to manage the consistency for such a graph that is updated concurrently, the rest of the system has unfortunately to be considered as well. Suddenly our inconspicuous counter might need to take into account that the STM might retry transactions. Similarly, the library for asynchronous tasks might suddenly need to retract a task from the run queue when a transaction is retried. This is the point that makes concurrent programming really hard. Such ‘design’ decision are not strictly local anymore and the question arises not only for STM but for all concurrent programming abstractions: do they compose well?

Huge Number of Different Abstractions

The question of whether concurrent programming abstractions compose is not at all straightforward to answer. As indicated in the discussion in the previous section, there are many different possible requirements in any given situation, so that even the design of a simple counter becomes a complex undertaking. Over the decades, the huge amount of tradeoffs resulted in many different variations of few at least superficially related concepts. The tradeoffs are also not only about performance for instance in terms of how much guarantees a framework may provide. Often somewhat philosophical points come into the discussion. For instance, some people argue that blocking operations preserve better the local sequential view on a system and therefore are simpler to program, often however at the cost of potential deadlocks. On the other hand, a completely asynchronous non-blocking design might be deadlock free, but depending on how the language exposes it, one might end up in callback hell and code becomes hardly maintainable. Yet another aspect might be whether to allow non-determinism or not. It can be easier to reason about a strictly deterministic system. However, such a language or framework might restrict the expressiveness so much that not all conceivable applications can be expressed in it.

To give a few examples, the futures of Clojure and Java are blocking, which always introduces the risk for deadlocks when other blocking abstractions are used in conjunction. The futures offered by AmbientTalk and E (called promises) are inherently non-blocking to fit into the overall nature of these two languages as being non-blocking and deadlock free. Consequently however, both types of futures are used differently and one might argue that one is preferable over the other in certain situations.

Similar is the situation when it comes to the concrete implementation of communicating sequential processes. Personally, I consider the strict isolation between processes, and therefore the enforcement that any form of communication has to go via the explicit channels, as a major property that can simplify reasoning about the concurrent semantics and for instance makes sure that programs are free from low-level data races. However, Go for instance chose to adopt the notion of communicating via channels but its goroutines are not isolated. JCSP, a Java library goes the same way. The occam-pi language on the other hand chose to stick with the notion of fully isolated processes. The same design discussion could be had for implementations of the actor model. AmbientTalk and Erlang go with fully isolated processes, while for instance Akka makes the pragmatic decision that it cannot guarantee isolation because it is running on top of a JVM.

This discussion could go on for quite a while. Wikipedia lists currently more than 60 concurrent programming languages of which most will implement some specific variation of a concept. In previous work, we identified roughly a hundred concepts that are related to concurrent programming.

It can now of course be argued that a single language will not support all of them and thus applications will perhaps only have to cope with a handful concurrent programming abstractions. However, looking at large open source applications such as IDEs, it seems that the various subsystems from time to time start to introduce their own abstractions. NetBeans for instance has various representations of asynchronous task or future like abstractions and there are at least two implementations of somewhat ‘transactional’ systems, one in the refactoring subsystem and another one in the profiling library. They seem to implement something along the lines of STM in different degrees of complexity. And this again raises the question how are these different abstractions interacting with each other. A look at NetBeans bug track yields more than 4000 bugs that contain the word “deadlock” and more than 500 bugs with the phrase “race condition”. While most of these bugs are marked as closed, it is probably a good indication that concurrent programming is hard and error prone.

Concurrent Programming Abstractions Not Designed for use in Combination

Usually concurrent programming is considered hard because low-level abstractions such as threads and locks are used. While NetBeans uses these to a significant extent, it uses also considerably more high-level concepts such as futures, asynchronous tasks, and STM. Now I would argue that it is not necessarily the abstraction level but that various concurrent programming abstractions are used together while they have not been designed for that purpose. While each abstraction in isolation is well tailored for its purpose, and thus reduced the accidental complexity, concurrency often does not remain confined to modules or subsystem and thus the interaction between the abstractions causes significant accidental complexity.

As far as I am aware, the fewest languages have been designed from the ground up with concurrency in mind, and even fewer languages are designed with the interaction of concurrent programming abstractions in mind. While for instance Java was designed with threads in mind and has the synchronized keyword to facilitate thread-based programming, its memory model and the java.util.concurrent libraries were only added in Java 5. Arguable, Java’s libraries are so low-level that languages such as Clojure and Scala try to close the gap. Clojure was consequently designed from the start concurrent programming in mind. It started out with atoms, agents, and STM to satisfy the different use case for concurrency. However, even so Clojure was design with them from the start, they do not interact well. Atoms are considered low-level and do not regard transactions or agents at all. STM on the other hand accounts for agents by deferring message sends until the end of a transaction to make sure that a transaction can be retried safely. With only these three abstractions, Clojure actually could be considered a fine example. However, these abstractions were apparently not sufficient to cover all use cases equally well and futures and promises as well as CSP in form of the core.async library got added. Unfortunately, the abstractions were not designed to integrate well with the existing ones. Instead, there were merely added and interactions can cause for instance unexpected deadlocks or race conditions (for more details see this paper).

In order to give a more academic example, which might not be governed by mostly pragmatic concerns, Haskell might be a reasonable candidate. Unfortunately, even in Haskell the notion of adding instead of integrating seems to be the prevalent one. I am not a Haskell expert, but the STM shows the same symptoms Clojure has, however, in a slightly different way. The standard Control.Concurrent package comes for instance with MVar and Chan as abstractions for mutable state and communication channels. But instead of integrating the STM with these, it introduces its own variants TMVar and TChan. It might be performance reasons that led to this situation. However, from the perspective of engineering large applications this can hardly be ideal, because the question of whether these abstractions can be used without problems in the same application remains unanswered.


I think that concurrent programming is hard because the abstractions we use today are not prepared for the job. They are good for one specific task, but they are not easily used in conjunction with each other. Instead, interactions can lead for instance to unexpected race conditions or deadlocks. And just to support the claim that interaction is an issue, it is not just NetBeans that uses are variety of concurrent programming concepts. Eclipse looks similar, and so do MonoDevelop and SharpDevelop. A study in the Scala world suggests also that application developer chose to combine the actor model with other abstractions for instance for performance reasons.

So, what’s the solution? I think, we need to design languages and libraries that properly integrate a variety of concurrent programming abstractions. How that should look concretely, I don’t know yet. The work of Joeri De Koster shows how solutions could look like for actor languages, and together with Janwillem Swallens, we are extending this work to a wider set of languages. Personally, I still belief that the ownership-based metaobject protocol is a useful foundation to experiment with various different concurrent programming abstractions on top of one language. But, we will see.

For comments, suggestions, ideas, or complains that I did not consider your language that already solves to problem, please catch me on Twitter @smarr or send me a mail.