You are currently browsing the Programming category
Displaying 1 - 10 of 12 entries.

OpenMP 4.0 Specifications Released

  • Posted on June 22, 2017 at 8:49 pm

The OpenMP 4.0 API Specification is released with Significant New Standard Features

The OpenMP 4.0 API supports the programming of accelerators, SIMD programming, and better optimization using thread affinity

The OpenMP Consortium has released OpenMP API 4.0, a major upgrade of the OpenMP API standard language specifications. Besides several major enhancements, this release provides a new mechanism to describe regions of code where data and/or computation should be moved to another computing device.

Bronis R. de Supinski, Chair of the OpenMP Language Committee, stated that “OpenMP 4.0 API is a major advance that adds two new forms of parallelism in the form of device constructs and SIMD constructs. It also includes several significant extensions for the loop-based and task-based forms of parallelism already supported in the OpenMP 3.1 API.

The 4.0 specification is now available on the 

Standard for parallel programming extends its reach

With this release, the OpenMP API specifications, the de-facto standard for parallel programming on shared memory systems, continues to extend its reach beyond pure HPC to include DSPs, real time systems, and accelerators. The OpenMP API aims to provide high-level parallel language support for a wide range of applications, from automotive and aeronautics to biotech, automation, robotics and financial analysis.

New features in the OpenMP 4.0 API include:

· Support for accelerators. The OpenMP 4.0 API specification effort included significant participation by all the major vendors in order to support a wide variety of compute devices. OpenMP API provides mechanisms to describe regions of code where data and/or computation should be moved to another computing device. Several prototypes for the accelerator proposal have already been implemented.

· SIMD constructs to vectorize both serial as well as parallelized loops. With the advent of SIMD units in all major processor chips, portable support for accessing them is essential. OpenMP 4.0 API provides mechanisms to describe when multiple iterations of the loop can be executed concurrently using SIMD instructions and to describe how to create versions of functions that can be invoked across SIMD lanes.

· Error handling. OpenMP 4.0 API defines error handling capabilities to improve the resiliency and stability of OpenMP applications in the presence of system-level, runtime-level, and user-defined errors. Features to abort parallel OpenMP execution cleanly have been defined, based on conditional cancellation and user-defined cancellation points.

· Thread affinity. OpenMP 4.0 API provides mechanisms to define where to execute OpenMP threads. Platform-specific data and algorithm-specific properties are separated, offering a deterministic behavior and simplicity in use. The advantages for the user are better locality, less false sharing and more memory bandwidth.

· Tasking extensions. OpenMP 4.0 API provides several extensions to its task-based parallelism support. Tasks can be grouped to support deep task synchronization and task groups can be aborted to reflect completion of cooperative tasking activities such as search. Task-to-task synchronization is now supported through the specification of task dependency.

· Support for Fortran 2003. The Fortran 2003 standard adds many modern computer language features. Having these features in the specification allows users to parallelize Fortran 2003 compliant programs. This includes interoperability of Fortran and C, which is one of the most popular features in Fortran 2003.

· User-defined reductions. Previously, OpenMP API only supported reductions with base language operators and intrinsic procedures. With OpenMP 4.0 API, user-defined reductions are now also supported.

· Sequentially consistent atomics. A clause has been added to allow a programmer to enforce sequential consistency when a specific storage location is accessed atomically.

This represents collaborative work by many of the brightest in industry, research, and academia, building on the consensus of 26 members. We strive to deliver high-level parallelism that is portable across 3 widely-implemented common General Purpose languages, productive for HPC and consumers, and delivers highly competitive performance. I want to congratulate all the members for coming together to create such a momentous advancement in parallel programming, under such tight constraints and industry challenges.
With this release, the OpenMP API will move immediately forward to the next release to bring even more usable parallelism to everyone.
 – Michael Wong, CEO OpenMP ARB.

Why designed a front-end programming language from scratch

  • Posted on June 2, 2017 at 8:14 am

Today’s programming languages have traditionally been created by the tech giants. These languages are made up of millions of lines of code, so the tech giants only invest in incremental, non-breaking changes that address their business concerns. This is why innovation in popular languages like C, Java, and JavaScript is depressingly slow.

Open-source languages like Python and Ruby gained widespread industrial use by solving backend problems at startup scale. Without the constraints of legacy code and committee politics, language designers are free to explore meaningful language innovation. And with compile-to-VM languages, it has become cheap enough for individuals and startups to create the future of programming languages themselves.

Open-source language innovation has not yet disrupted front-end programming. We still use the same object-oriented model that took over the industry in the 1980s. The tech giants are heavily committed to this approach, but open-source has made it possible to pursue drastically different methods.

Two years ago, I began to rethink front-end programming from scratch. I quickly found myself refining a then-obscure academic idea called Functional Reactive Programming. This developed into Elm, a language that compiles to JavaScript and makes it much easier to create highly interactive programs.

Since the advent of Elm, a lively and friendly community has sprung up, made up of everyone from professional developers to academics to beginners who have never tried functional programming before. This diversity of voices and experiences has been a huge help in guiding Elm towards viability as a production-ready language.

The community has already created a bunch of high quality contributions that are shaping the future of Elm and are aiming to shape the future of front-end programming.

Dev tools

Early on, I made it a priority to let people write, compile, and use Elm programs directly from their browser. No install, no downloads. This interactive editor made it easy for beginners and experts alike to learn Elm and start using it immediately.

In-browser compilation triggered lots of discussion, ideas, and ultimately contributions. Mads Flensted-Urech added in-line documentation for all standard libraries. Put your cursor over a function, and you get the type, prose explanation, and link to the library it comes from. Laszlo Pandy took charge of debugging tools. He is focusing on visualizing the state of an Elm program as time passes, even going so far as pausing, rewinding, and replaying events.


I designed Elm to work nicely with concurrency. Unfortunately, JavaScript’s concurrency support is quite poor with questionable prospects for improvement. I decided to save the apparent implementation quagmire for later, but John P. Mayer decided to make it happen. He now has a version of the runtime that can automatically multiplex tasks across many threads, all implemented in JavaScript.

Common to all of these cases are driven individuals who knew they could do it better. This is how Elm got started and how it caught the attention of Prezi, a company also not content to accept JavaScript as the one and only answer for front-end development. I have since joined the company for the express purpose of furthering work on Elm.

We do not need to sit and hope that the tech giants will someday do an okay job. We can create the future of front-end programming ourselves, and we can do it now.


Attend Meeting C++ 2013

  • Posted on June 2, 2017 at 7:03 am

Boost Dependency Analyzer

I have something special to announce today. A tool I’ve build over the last 2 weeks, which allows to analyze the dependencies in boost. With boost 1.53 this spring, I had the idea to build this, but not the time, as I was busy writing a series over the Papers for Bristol. Back then I realized, how easy it could be to build such a tool, as the dependencies could be read & listed by boosts bcp tool. I already had a prototype for the graphpart from 2010. But lets have a look at the tool:

The tool is very easy to handle, it is based on the out of bcp, which is a tool coming with boost. Actually bcp can help you with ripping libraries out of boost, so that you don’t have to add all of boost to your repository when you would like to use smartpointers. But bcp also has a listing mode, where it only shows the dependencies thats whats my tool build up upon. Lets have a short look at the results, the dependencies of boost 1.54:

A few words on how to read this graph. The libraries in the middle of the “starshape” are the ones with the most dependencies, each line between the nodes is a dependency. A dependency can be one or multiple files. The graphlayout is not weighted.

How to

A short introduction on what you need to get this tool to run. First boost, as this tool is build to analyze boost. I’ve tested with some versions (1.49 – 1.54) of boost. You also need a version of bcp, which is quite easy to build (b2 tools/bcp). Then you simply need to start the tool, if BOOST_ROOT is set, the tool will try to read it, other wise you will be asked to choose the location of boost when clicking on Read dependencies. Next thing is selecting the location of bcp. That is the setup, and the tool will now run for some time. On my machine its 90 seconds to 2 minutes the analysis takes, it might be lot longer on yours, depending on how much cores you got. The tool will spawn for each boost library (~112) a bcp process, and analyze this output in a thread pool. After this is done, the data is loaded into the tool, and then saved to a SQLITE database, which will be used if you start the tool a second time and select this version of boost. Loading from the database is far faster.

A screenshot to illustrate this:


To the left are all the boost libraries, the number of dependencies is shown in the braces. To the right is a Tabwidget showing all the dependencies, the graph is layouted with boost graph. When you click on show all you’ll get the full view of all dependencies in boost. The layouting is done in the background, so this will take some time to calculate, and is animated when its done. The results of the layouting are good, but not perfect, so that you might have to move some nodes. Exporting supports images, which are transparent PNGs, not all services/tools are happy with that (f.e. facebook, twitter nor G+ could handle the perfectly fine images), this can be fixed by postprocessing the images and adding a white background.

Inner workings

I’ve already written a little about the tools inside, its build with Qt5.1 and boost. Where boost is mostly used for the graph layouting. As I choose to work with Qt5, it has a few more dependencies, for windows this sums up to a 18 mb download, which you’ll find at the end. The tool depends on 3 libraries from my company Code Node: ProcessingSink, a small wrapper around QProcess, that allows to just start a bunch of processes, and lets you connect to the finished and error slot. This was necessary, as I could only spawn 62 parallel processes under windows, so this library does take care of spawning the parallel processes now. Which are currently 50 at a time. GraphLayout is the code that wraps the innerworkings of boost::graph, its a bit dirty, but lets me easily process the graphlayouting. The 3rd library is NodeGraph, which is the Graph UI, based on Qts GraphicsView Framework.
I plan to release the tool and its libraries under GPL later on github, for now I don’t have the time to polish everything.


One of the earliest questions I had when thinking about building such a tool, was where to get a list of the boost libraries? This sounds easy. But I need to have this readable by machine, not human, so HTML is a great format, but I refused to write a parser for this list yet. I talked to some people about this at C++Now, and most agreed, that the second option would be best: maintainers.txt. Thats what the tool reads currently to find the boost libraries. Unfortunately at least lexical_cast is missing in this list. So, the tool isn’t perfect yet, while lexical_cast is already patched, I’m not sure if anything else is missing. A candidate could be signals, as its not maintained anymore. Currently the tool analyzes for 1.54 112 libraries.

boost dependencies

Working for 2 weeks on this tool has given me some inside knowledge about the dependencies in boost. First, the way it is shown in the tool, is the view of bcp. Some dependencies will not affect the user, as they are internal. f.e. a lot of libraries have a dependency to boost::test, simply because they provide their tests with it. The bcp tool really gets you ALL the dependencies. Also most (or was it all?) libraries depend on boost::config. I plan to add filtering later, so that the user has the ability to filter some of the libraries in the GraphView.

The tool

Here is how to get the tool for now: there is a download for the binaries for windows and linux. I’ll try to get you a deb package as soon as I have time, but for now its only the binaries for linux, you’ll have to make sure to have Qt5.1 etc. on linux too, as I do not provide them. For Windows, its 2 archives you’ll need to download: the programm itself, and needed dlls for Qt5.1 if you don’t have the SDK installed ( in this case you also could copy them from the bin directory)

Note on linux: this is a one day old beta version. Will update this later.

Official feedback on OpenGL 4.4 thread

  • Posted on March 30, 2017 at 5:20 pm

 SIGGRAPH – Anaheim, CA – The Khronos™ Group today announced the immediate release of the OpenGL® 4.4 specification,bringing the very latest graphics functionality to the most advanced and widely adopted cross-platform 2D and 3D graphics API (application programming interface). OpenGL 4.4 unlocks capabilities of today’s leading-edge graphics hardware while maintaining full backwards compatibility, enabling applications to incrementally use new features while portably accessing state-of-the-art graphics processing units (GPUs) across diverse operating systems and platforms. Also, OpenGL 4.4 defines new functionality to streamline the porting of applications and titles from other platforms and APIs. The full specification and reference materials are available for immediate download at

In addition to the OpenGL 4.4 specification, the OpenGL ARB (Architecture Review Board) Working Group at Khronos has created the first set of formal OpenGL conformance tests since OpenGL 2.0. Khronos will offer certification of drivers from version 3.3, and full certification is mandatory for OpenGL 4.4 and onwards. This will help reduce differences between multiple vendors’ OpenGL drivers, resulting in enhanced portability for developers.

New functionality in the OpenGL 4.4 specification includes:

Buffer Placement Control (GL_ARB_buffer_storage)
Significantly enhances memory flexibility and efficiency through explicit control over the position of buffers in the graphics and system memory, together with cache behavior control – including the ability of the CPU to map a buffer for direct use by a GPU.

Efficient Asynchronous Queries
Buffer objects can be the direct target of a query to avoid the CPU waiting for the result and stalling the graphics pipeline. This provides significantly boosted performance for applications that intend to subsequently use the results of queries on the GPU, such as dynamic quality reduction strategies based on performance metrics.

Shader Variable Layout (GL_ARB_enhanced_layouts)
Detailed control over placement of shader interface variables, including the ability to pack vectors efficiently with scalar types. Includes full control over variable layout inside uniform blocks and enables shaders to specify transform feedback variables and buffer layout.

Efficient Multiple Object Binding (GL_ARB_multi_bind)
New commands which enable an application to bind or unbind sets of objects with one API call instead of separate commands for each bind operation, amortizing the function call, name space lookup, and potential locking overhead. The core rendering loop of many graphics applications frequently bind different sets of textures, samplers, images, vertex buffers, and uniform buffers and so this can significantly reduce CPU overhead and improve performance.

Streamlined Porting of Direct3D applications

A number of core functions contribute to easier porting of applications and games written in Direct3D including GL_ARB_buffer_storage for buffer placement control, GL_ARB_vertex_type_10f_11f_11f_rev which creates a vertex data type that packs three components in a 32 bit value that provides a performance improvement for lower precision vertices and is a format used by Direct3D, and GL_ARB_texture_mirror_clamp_to_edge that provides a texture clamping mode also used by Direct3D.Extensions released alongside the OpenGL 4.4 specification include:

Bindless Texture Extension (GL_ARB_bindless_texture)
Shaders can now access an effectively unlimited number of texture and image resources directly by virtual addresses. This bindless texture approach avoids the application overhead due to explicitly binding a small window of accessible textures. Ray tracing and global illumination algorithms are faster and simpler with unfettered access to a virtual world’s entire texture set.

Sparse Texture Extension (GL_ARB_sparse_texture)
Enables handling of huge textures that are much larger than the GPUs physical memory by allowing an application to select which regions of the texture are resident for ‘mega-texture’ algorithms and very large data-set visualizations.

OpenGL BOF at SIGGRAPH, Anaheim, CA July 24th 2013
There is an OpenGL BOF “Birds of a Feather” Meeting on Wednesday July 24th at 7-8PM at the Hilton Anaheim, California Ballroom A & B, where attendees are invited to meet OpenGL implementers and developers and learn more about the new OpenGL 4.4 specification.

Parallel and Concurrent Programming in Haskell

  • Posted on February 14, 2017 at 1:35 pm

As one of the developers of the Glasgow Haskell Compiler (GHC) for almost 15 years, I have seen Haskell grow from a niche research language into a rich and thriving ecosystem. I spent a lot of that time working on GHC’s support for parallelism and concurrency. One of the first things I did to GHC in 1997 was to rewrite its runtime system, and a key decision we made at that time was to build concurrency right into the core of the system rather than making it an optional extra or an add-on library. I like to think this decision was founded upon shrewd foresight, but in reality it had as much to do with the fact that we found a way to reduce the overhead of concurrency to near zero (previously it had been on the order of 2%; we’ve always been performance-obsessed). Nevertheless, having concurrency be non-optional meant that it was always a first-class part of the implementation, and I’m sure that this decision was instrumental in bringing about GHC’s solid and lightning-fast concurrency support.

Haskell has a long tradition of being associated with parallelism. To name just a few of the projects, there was the pH variant of Haskell derived from the Id language, which was designed for parallelism, the GUM system for running parallel Haskell programs on multiple machines in a cluster, and the GRiP system: a complete computer architecture designed for running parallel functional programs. All of these happened well before the current multicore revolution, and the problem was that this was the time when Moore’s law was still giving us ever-faster computers. Parallelism was difficult to achieve, and didn’t seem worth the effort when ordinary computers were getting exponentially faster.

Around 2004, we decided to build a parallel implementation of the GHC runtime system for running on shared memory multiprocessors, something that had not been done before. This was just before the multicore revolution. Multiprocessor machines were fairly common, but multicores were still around the corner. Again, I’d like to think the decision to tackle parallelism at this point was enlightened foresight, but it had more to do with the fact that building a shared-memory parallel implementation was an interesting research problem and sounded like fun. Haskell’s purity was essential—it meant that we could avoid some of the overheads of locking in the runtime system and garbage collector, which in turn meant that we could reduce the overhead of using parallelism to a low-single-digit percentage. Nevertheless, it took more research, a rewrite of the scheduler, and a new parallel garbage collector before the implementation was really usable and able to speed up a wide range of programs. The paper I presented at the International Conference on Functional Programming (ICFP) in 2009 marked the turning point from an interesting prototype into a usable tool.

All of this research and implementation was great fun, but good-quality resources for teaching programmers how to use parallelism and concurrency in Haskell were conspicuously absent. Over the last couple of years, I was fortunate to have had the opportunity to teach two summer school courses on parallel and concurrent programming in Haskell: one at the Central European Functional Programming (CEFP) 2011 summer school in Budapest, and the other the CEA/EDF/INRIA 2012 Summer School at Cadarache in the south of France. In preparing the materials for these courses, I had an excuse to write some in-depth tutorial matter for the first time, and to start collecting good illustrative examples. After the 2012 summer school I had about 100 pages of tutorial, and thanks to prodding from one or two people (see the Acknowledgments), I decided to turn it into a book. At the time, I thought I was about 50% done, but in fact it was probably closer to 25%. There’s a lot to say! I hope you enjoy the results.


You will need a working knowledge of Haskell, which is not covered in this book. For that, a good place to start is an introductory book such as Real World Haskell (O’Reilly), Programming in Haskell (Cambridge University Press), Learn You a Haskell for Great Good! (No Starch Press), or Haskell: The Craft of Functional Programming (Addison-Wesley).

How to Read This Book

The main goal of the book is to get you programming competently with Parallel and Concurrent Haskell. However, as you probably know by now, learning about programming is not something you can do by reading a book alone. This is why the book is deliberately practical: There are lots of examples that you can run, play with, and extend. Some of the chapters have suggestions for exercises you can try out to get familiar with the topics covered in that chapter, and I strongly recommend that you either try a few of these, or code up some of your own ideas.

As we explore the topics in the book, I won’t shy away from pointing out pitfalls and parts of the system that aren’t perfect. Haskell has been evolving for over 20 years but is moving faster today than at any point in the past. So we’ll encounter inconsistencies and parts that are less polished than others. Some of the topics covered by the book are very recent developments: Chapters 4, 5, 6, and pass:[14 cover frameworks that were developed in the last few years.

The book consists of two mostly independent parts: Part I and Part II. You should feel free to start with either part, or to flip between them (i.e., read them concurrently!). There is only one dependency between the two parts: Chapter 13 will make more sense if you have read Part I first, and in particular before reading “The ParIO monad”, you should have read Chapter 4.

While the two parts are mostly independent from each other, the chapters should be read sequentially within each part. This isn’t a reference book; it contains running examples and themes that are developed across multiple chapters.

Seven signs of dysfunctional engineering teams

  • Posted on February 13, 2017 at 3:48 am

I’ve been listening to the audiobook of Heart of Darkness this week, read by Kenneth Branagh. It’s fantastic. It also reminds me of some jobs I’ve had in the past.

There’s a great passage in which Marlow requires rivets to repair a ship, but finds that none are available. This, in spite of the fact that the camp he left further upriver is drowning in them. That felt familiar. There’s also a famous passage involving a French warship that’s blindly firing its cannons into the jungles of Africa in hopes of hitting a native camp situated within. I’ve had that job as well. Hopefully I can help you avoid getting yourself into those situations.

There are several really good lists of common traits seen in well-functioning engineering organizations. Most recently, there’s Pamela Fox’s list of What to look for in a software engineering culture. More famous, but somewhat dated at this point, is Joel Spolsky’s Joel Test. I want to talk about signs of teams that you should avoid.

This list is partially inspired by Ralph Peters’ Spotting the Losers: Seven Signs of Non-Competitive States. Of course, such a list is useless if you can’t apply it at the crucial point, when you’re interviewing. I’ve tried to include questions to ask and clues to look for that reveal dysfunction that is deeply baked into an engineering culture.

Preference for process over tools. As engineering teams grow, there are many approaches to coordinating people’s work. Most of them are some combination of process and tools. Git is a tool that enables multiple people to work on the same code base efficiently (most of the time). A team may also design a process around Git — avoiding the use of remote branches, only pushing code that’s ready to deploy to the master branch, or requiring people to use local branches for all of their development. Healthy teams generally try to address their scaling problems with tools, not additional process. Processes are hard to turn into habits, hard to teach to new team members, and often evolve too slowly to keep pace with changing circumstances. Ask your interviewers what their release cycle is like. Ask them how many standing meetings they attend. Look at the company’s job listings, are they hiring a scrum master?

Excessive deference to the leader or worse, founder. Does the group rely on one person to make all of the decisions? Are people afraid to change code the founder wrote? Has the company seen a lot of turnover among the engineering leader’s direct reports? Ask your interviewers how often the company’s coding conventions change. Ask them how much code in the code base has never been rewritten. Ask them what the process is for proposing a change to the technology stack. I have a friend who worked at a growing company where nobody was allowed to introduce coding conventions or libraries that the founding VP of Engineering didn’t understand, even though he hardly wrote any code any more.

Unwillingness to confront technical debt. Do you want to walk into a situation where the team struggles to make progress because they’re coding around all of the hacks they haven’t had time to address? Worse, does the team see you as the person who’s going to clean up all of the messes they’ve been leaving behind? You need to find out whether the team cares about building a sustainable code base. Ask the team how they manage their backlog of bugs. Ask them to tell you about something they’d love to automate if they had time. Is it something that any sensible person would have automated years ago? That’s a bad sign.

Not invented this week syndrome. We talk a lot about “not invented here” syndrome and how it affects the competitiveness of companies. I also worry about companies that lurch from one new technology to the next. Teams should make deliberate decisions about their stack, with an eye on the long term. More importantly, any such decisions should be made in a collaborative fashion, with both developer productivity and operability in mind. Finding out about this is easy. Everybody loves to talk about the latest thing they’re working with.

Disinterest in sustaining a Just Culture. What’s Just Culture? This post by my colleague John Allspaw on blameless post mortems describes it pretty well. Maybe you want to work at a company where people get fired on the spot for screwing up, or yelled at when things go wrong, but I don’t. How do you find out whether a company is like that? Ask about recent outages and gauge whether the person you ask is willing to talk about them openly. Do the people you talk to seem ashamed of their mistakes?

Monoculture. Diversity counts. Gender diversity is really important, but it’s not the only kind of diversity that matters. There’s ethnic diversity, there’s age diversity, and there’s simply the matter of people acting differently, or dressing differently. How homogenous is the group you’ve met? Do they all remind you of you? That’s almost certainly a serious danger sign. You may think it sounds like fun to work with a group of people who you’d happily have as roommates, but monocultures do a great job of masking other types of dysfunction.

Lack of a service-oriented mindset. The biggest professional mistakes I ever made were the result of failing to see that my job was ultimately to serve other people. I was obsessed with building what I thought was great software, and failed to see that what I should have been doing was paying attention to what other people needed from me in order to succeed in their jobs. You can almost never fail when you look for opportunities to be of service and avail yourself of them. Be on the lookout for companies where people get ahead by looking out for themselves. Don’t take those jobs.

There are a lot of ways that a team’s culture can be screwed up, but those are my top seven.

Lambda Expressions Backported to Java 7, 6 and 5

  • Posted on January 9, 2017 at 1:46 am

Do you want to use lambda expressions already today, but you are forced to use Java and a stable JRE in production? Now that’s possible with Retrolambda, which will take bytecode compiled with Java 8 and convert it to run on Java 7, 6 and 5 runtimes, letting you use lambda expressions andmethod references on those platforms. It won’t give you the improved Java 8 Collections API, but fortunately there are multiple alternative libraries which will benefit from lambda expressions.

Behind the Scenes

A couple of days ago in a café it popped into my head to find out whether somebody had made this already, but after speaking into the air, I did it myself over a weekend.

The original plan of copying the classes from OpenJDK didn’t work (LambdaMetafactory depends on some package-private classes and would have required modifications), but I figured out a better way to do it without additional runtime dependencies.

Retrolambda uses a Java agent to find out what bytecode LambdaMetafactory generates dynamically, and saves it as class files, after which it replaces the invokedynamic instructions to instantiate those classes directly. It also changes some private synthetic methods to be package-private, so that normal bytecode can access them without method handles.

After the conversion you’ll have just a bunch of normal .class files – but with less typing.

P.S. If you hear about experiences of using Retrolambda for Android development, please leave a comment.

5 Coding Hacks to Reduce GC Overhead

  • Posted on November 29, 2016 at 9:46 pm

In this post we’ll look at five ways in roomates efficient coding we can use to help our garbage collector CPU spend less time allocating and freeing memory, and reduce GC overhead. Often Long GCs can lead to our code being stopped while memory is reclaimed (AKA “stop the world”). Duke_GCPost

Some background

The GC is built to handle large amounts of allocations of short-lived objects (think of something like rendering a web page, where most of the objects allocated Become obsolete once the page is served).

The GC does this using what’s called a “young generation” – a heap segment where new objects are allocated. Each object has an “age” (placed in the object’s header bits) defines how many roomates collections it has “survived” without being reclaimed. Once a certain age is reached, the object is copied into another section in the heap called a “survivor” or “old” generation.

The process, while efficient, still comes at a cost. Being Able to reduce the number of temporary allocations can really help us increase of throughput, especially in high-scale applications.

Below are five ways everyday we can write code that is more memory efficient, without having to spend a lot of time on it, or reducing code readability.

1. Avoid implicit Strings

Strings are an integral part of almost every structure of data we manage. Being much heavier than other primitive values, they have a much stronger impact on memory usage.

One of the most important things to note is that Strings are immutable. They can not be modified after allocation. Operators such as “+” for concatenation actually allocate a new String containing the contents of the strings being joined. What’s worse, is there’s an implicit StringBuilder object that is allocated to actually do the work of combining them.

For example –

a = a + b; / / a and b are Strings
The compiler generates code comparable behind the scenes:

StringBuilder temp = new StringBuilder (a).
temp.append (b);
a = temp.toString () / / a new string is allocated here.
/ / The previous “a” is now garbage.
But it gets worse.

Let’s look at this example –

String result = foo () + arg;
result + = boo ();
System.out.println (“result =” + result);
In this example we have 3 StringBuilders allocated in the background – one for each plus operation, and two additional Strings – one to hold the result of the second assignment and another to hold the string passed into the print method. That’s 5 additional objects in what would otherwise Appear to be a pretty trivial statement.

Think about what happens in real-world scenarios such as generating code a web page, working with XML or reading text from a file. Within a nested loop structures, you could be looking at Hundreds or Thousands of objects that are implicitly allocated. While the VM has Mechanisms to deal with this, it comes at a cost – one paid by your users.

The solution: One way of reducing this is being proactive with StringBuilder allocations. The example below Achieves the same result as the code above while allocating only one StringBuilder and one string to hold the final result, instead of the original five objects.

StringBuilder value = new StringBuilder (“result =”);
value.append (foo ()). append (arg). append (boo ());
System.out.println (value);
By being mindful of the way Strings are implicitly allocated and StringBuilders you can materially reduce the amount of short-term allocations in high-scale code locations.

2. List Plan capacities

Dynamic collections such as ArrayLists are among the most basic dynamic structures to hold the data length. ArrayLists and other collections such as HashMaps and implemented a Treemaps are using the underlying Object [] arrays. Like Strings (Themselves wrappers over char [] arrays), arrays are also immutable. Becomes The obvious question then – how can we add / put items in their collections if the underlying array’s size is immutable? The answer is obvious as well – by allocating more arrays.

Let’s look at this example –

List <Item> <Item> items = new ArrayList ();

for (int i = 0; i <len; i + +)
Item item = readNextItem ();
items.add (item);
The value of len Determines the ultimate length of items once the loop finishes. This value, however, is unknown to the constructor of the ArrayList roomates allocates a new Object array with a default size. Whenever the internal capacity of the array is exceeded, it’s replaced with a new array of sufficient length, making the previous array of garbage.

If you’re executing the loop Welcome to Thunderbird times you may be forcing a new array to be allocated and a previous one to be collected multiple times. For code running in a high-scale environment, these allocations and deallocations are all deducted from your machine’s CPU cycles.

Hunger Games themed semi-iterated prisoner’s dilemma tournament

  • Posted on November 26, 2016 at 1:14 pm

With all the talk surrounding it, crowdsourcing science might seem like a new concept and it might be true for citizen science efforts, but it is definitely an old trick to source your research to other researchers. In fact, evolutionary game theory was born (or at least popularized) by one such crowdsourcing exercise; in 1980, Robert Axelrod wanted to find out the best strategy for iterated prisoner’s dilemma and reached out to prominent researchers for strategy submissions to around-robin tournmanet. Tit-for-tat was the winning strategy, but the real victor was Axelrod. His 1981 paper with Hamilton analyzing the result went on to become a standard reference in applications of game theory to social questions (at least outside of economics), agent-based modeling, and — of course — evolutionary game theory. Of Axelrod’s sizeable 47,222 (at time of writing) citations, almost half (23,370) come from this single paper. The tradition of tournaments continues among researchers, I’ve even discussed an imitation tournament on imitation previously.

The cynical moral of the tale: if you want to be noticed then run a game theory tournament. The folks at— a website offering weekly olympiad-style challange problems in math and physics — took this message to heart, coupled it to the tried-and-true marketing technique of linking to a popular movie/book franchise, and decided to run a Hunger Games themed semi-iterated Prisoner’s dillema tournament. Submit a quick explanation of your strategy and Python script to play the game, and you could be one of the 5 winners of the $1,000 grand prize. Hooray! The submission deadline is August 18th, 2013 and all you need is a Brilliant account and it seems that these are free. If you are a reader of TheEGG blog then I recommend submitting a strategy, and discussing it in the comments (either before or after the deadline); I am interested to see what you come up with.

I will present the rules in m

Integrating C++ with QML

  • Posted on November 14, 2016 at 9:15 pm


Qt Quick’s QML language makes it easy to do many things, especially fancy animated user interfaces. However, some things either can’t be done or are not suitable for implementing in QML, such as:

  1. Getting access to functionality outside of the QML/JavaScript environment.
  2. Implementing performance critical functions where native code is desired for efficiency.
  3. Large and/or complex non-declarative code that would be tedious to implement in JavaScript.

As we’ll see, Qt makes it quite easy to expose C++ code to QML. In this blog post I will show an example of doing this with a small but functional application.

The example is written for Qt 5 and uses the Qt Quick Components so you will need at least Qt version 5.1.0 to run it.


To expose a C++ type having properties, methods, signals, and/or slots to the QML environment, the basic steps are:

  1. Define a new class derived from QObject.
  2. Put the Q_OBJECT macro in the class declaration to support signals and slots and other services of the Qt meta-object system.
  3. Declare any properties using the Q_PROPERTY macro.
  4. Call qmlRegisterType() in your C++ main program to register the type with the Qt Quick engine.

For all the details I refer you to the Qt documentation section Exposing Attributes of C++ Types to QML and the Writing QML Extensions with C++ tutorial.

Ssh Key Generator

For our code example, we want a small application that will generate ssh public/private key pairs using a GUI. It will present the user with controls for the appropriate options and then run the program ssh-keygen to generate the key pair.

I implemented the user interface using the new Qt Quick Controls since it was intended as a desktop application with a desktop look and feel. I initially developed the UX entirely by running the qmlscene program directly on the QML source.

The UI prompts the user for the key type, the file name of the private key to generate and an optional pass phrase, which needs to be confirmed.

The C++ Class

Now that have the UI, we will want to implement the back end functionality. You can’t invoke an external program directly from QML so we have to write it in C++ (which is the whole point of this example application).

First, we define a class that encapsulates the key generation functionality. It will be exposed as a new class KeyGenerator in QML. This is done in the header file KeyGenerator.h below.


#include <QObject>
#include <QString>
#include <QStringList>

// Simple QML object to generate SSH key pairs by calling ssh-keygen.

class KeyGenerator : public QObject
    Q_PROPERTY(QString type READ type WRITE setType NOTIFY typeChanged)
    Q_PROPERTY(QStringList types READ types NOTIFY typesChanged)
    Q_PROPERTY(QString filename READ filename WRITE setFilename NOTIFY filenameChanged)
    Q_PROPERTY(QString passphrase READ filename WRITE setPassphrase NOTIFY passphraseChanged)


    QString type();
    void setType(const QString &t);

    QStringList types();

    QString filename();
    void setFilename(const QString &f);

    QString passphrase();
    void setPassphrase(const QString &p);

public slots:
    void generateKey();

    void typeChanged();
    void typesChanged();
    void filenameChanged();
    void passphraseChanged();
    void keyGenerated(bool success);

    QString _type;
    QString _filename;
    QString _passphrase;
    QStringList _types;

Next, we need to derive our class from QObject. We declare any properties that we want and the associated methods. Notify methods become signals. In our case, we want to have properties for the selected key type, the list of all valid ssh key types, file name and pass phrase. I arbitrarily made the key type a string. It could have been an enumerated type but it would have made the example more complicated.

Incidentally, a new feature of the Q_PROPERTY macro in Qt 5.1.0 is the MEMBER argument. It allows specifying a class member variable that will be bound to a property without the need to implement the setter or getter functions. That feature was not used here.

We declare methods for the setters and getters and for signals. We also declare one slot called generateKey(). These will all be available to QML. If we wanted to export a regular method to QML, we could mark it with Q_INVOCABLE. In this case I decided to make generateKey() a slot since it might be useful in the future but it could have just as easily been an invocable method.

Finally, we declare any private member variables we will need.

C++ Implementation

Now let’s look at the implementation in KeyGenerator.cpp. Here is the source code:

#include <QFile>
#include <QProcess>
#include "KeyGenerator.h"

    : _type("rsa"), _types{"dsa", "ecdsa", "rsa", "rsa1"}


QString KeyGenerator::type()
    return _type;

void KeyGenerator::setType(const QString &t)
    // Check for valid type.
    if (!_types.contains(t))

    if (t != _type) {
        _type = t;
        emit typeChanged();

QStringList KeyGenerator::types()
    return _types;

QString KeyGenerator::filename()
    return _filename;

void KeyGenerator::setFilename(const QString &f)
    if (f != _filename) {
        _filename = f;
        emit filenameChanged();

QString KeyGenerator::passphrase()
    return _passphrase;

void KeyGenerator::setPassphrase(const QString &p)
    if (p != _passphrase) {
        _passphrase = p;
        emit passphraseChanged();

void KeyGenerator::generateKey()
    // Sanity check on arguments
    if (_type.isEmpty() or _filename.isEmpty() or
        (_passphrase.length() > 0 and _passphrase.length() < 5)) {
        emit keyGenerated(false);

    // Remove key file if it already exists
    if (QFile::exists(_filename)) {

    // Execute ssh-keygen -t type -N passphrase -f keyfileq
    QProcess *proc = new QProcess;
    QString prog = "ssh-keygen";
    QStringList args{"-t", _type, "-N", _passphrase, "-f", _filename};
    proc->start(prog, args);
    emit keyGenerated(proc->exitCode() == 0);
    delete proc;

The constructor initializes some of the member variables. For fun, I used the new initializer list feature of C++11 to initialize the _types member variable which is of type QStringList. The destructor does nothing, at least for now, but is there for completeness and future expansion.

Getter functions like type() simply return the appropriate private member variable. Setters set the appropriate variables, taking care to check that the new value is different from the old one and if so, emitting the appropriate signal. As always, please note that signals are created by the Meta Object Compiler and do not need to be implemented, only emitted at the appropriate times.

The only non-trivial method is the slot generateKey(). It does some checking of arguments and then creates a QProcess to run the external ssh-keygen program. For simplicity and because it typically executes quickly, I do this synchronously and block on it to complete. When done, we emit a signal that has a boolean argument that indicates the key was generated and whether it succeeded or not.

QML Code

Now let’s look at the QML code in main.qml:

// SSH key generator UI

import QtQuick 2.1
import QtQuick.Controls 1.0
import QtQuick.Layouts 1.0
import QtQuick.Dialogs 1.0
import com.ics.demo 1.0

ApplicationWindow {
    title: qsTr("SSH Key Generator")

    statusBar: StatusBar {
    RowLayout {
        Label {
            id: status

    width: 369
    height: 166

    ColumnLayout {
        x: 10
        y: 10

        // Key type
        RowLayout {
            Label {
                text: qsTr("Key type:")
            ComboBox {
                id: combobox
                Layout.fillWidth: true
                model: keygen.types
                currentIndex: 2

        // Filename
        RowLayout {
            Label {
                text: qsTr("Filename:")
            TextField {
                id: filename
                implicitWidth: 200
                onTextChanged: updateStatusBar()
            Button {
                text: qsTr("&Browse...")
                onClicked: filedialog.visible = true

        // Passphrase
        RowLayout {
            Label {
                text: qsTr("Pass phrase:")
            TextField {
                id: passphrase
                Layout.fillWidth: true
                echoMode: TextInput.Password
                onTextChanged: updateStatusBar()


        // Confirm Passphrase
        RowLayout {
            Label {
                text: qsTr("Confirm pass phrase:")
            TextField {
                id: confirm
                Layout.fillWidth: true
                echoMode: TextInput.Password
                onTextChanged: updateStatusBar()

        // Buttons: Generate, Quit
        RowLayout {
            Button {
                id: generate
                text: qsTr("&Generate")
                onClicked: keygen.generateKey()
            Button {
                text: qsTr("&Quit")
                onClicked: Qt.quit()


    FileDialog {
        id: filedialog
        title: qsTr("Select a file")
        selectMultiple: false
        selectFolder: false
        selectedNameFilter: "All files (*)"
        onAccepted: {
            filename.text = fileUrl.toString().replace("file://", "")

    KeyGenerator {
        id: keygen
        filename: filename.text
        passphrase: passphrase.text
        type: combobox.currentText
        onKeyGenerated: {
            if (success) {
                status.text = qsTr('<font color="green">Key generation succeeded.</font>')
            } else {
                status.text = qsTr('<font color="red">Key generation failed</font>')

    function updateStatusBar() {
        if (passphrase.text != confirm.text) {
            status.text = qsTr('<font color="red">Pass phrase does not match.</font>')
            generate.enabled = false
        } else if (passphrase.text.length > 0 && passphrase.text.length < 5) {
            status.text = qsTr('<font color="red">Pass phrase too short.</font>')
            generate.enabled = false
        } else if (filename.text == "") {
            status.text = qsTr('<font color="red">Enter a filename.</font>')
            generate.enabled = false
        } else {
            status.text = ""
            generate.enabled = true

    Component.onCompleted: updateStatusBar()

The preceding code is a little long, however, much of the work is laying out the GUI components. The code should be straightforward to follow.

Note that we import com.ics.demo version 1.0. We’ll see where this module name comes from shortly. This makes a new QML type KeyGeneratoravailable and so we declare one. We have access to it’s C++ properties as QML properties, can call it’s methods and act on signals like we do withonKeyGenerated.

A more complete program should probably do a little more error checking and report meaningful error messages if key generation fails (we could easily add a new method or property for this). The UI layout could also be improved to make it properly resizable.

Our main program is essentially a wrapper like qmlscene. All we need to do to register our type with the QML engine is to call:

    qmlRegisterType<KeyGenerator>("com.ics.demo", 1, 0, "KeyGenerator");

This makes the C++ type KeyGenerator available as the QML type KeyGenerator in the module com.ics.demo version 1.0 when it is imported.

Typically, to run QML code from an executable, in the main program you would create a QGuiApplication and a QQuickView. Currently, to use the Qt Quick Components there is some additional work needed if the top level element is an ApplicationWindow or Window. You can look at the source code to see how I implemented this. I basically stripped down the code from qmlscene to the minimum of what was needed for this example.

Here is the full listing for the main program, main.cpp:

#include <QApplication>
#include <QObject>
#include <QQmlComponent>
#include <QQmlEngine>
#include <QQuickWindow>
#include <QSurfaceFormat>
#include "KeyGenerator.h"

// Main wrapper program.
// Special handling is needed when using Qt Quick Controls for the top window.
// The code here is based on what qmlscene does.

int main(int argc, char ** argv)
    QApplication app(argc, argv);

    // Register our component type with QML.
    qmlRegisterType<KeyGenerator>("com.ics.demo", 1, 0, "KeyGenerator");

    int rc = 0;

    QQmlEngine engine;
    QQmlComponent *component = new QQmlComponent(&engine);

    QObject::connect(&engine, SIGNAL(quit()), QCoreApplication::instance(), SLOT(quit()));


    if (!component->isReady() ) {
        qWarning("%s", qPrintable(component->errorString()));
        return -1;

    QObject *topLevel = component->create();
    QQuickWindow *window = qobject_cast<QQuickWindow *>(topLevel);

    QSurfaceFormat surfaceFormat = window->requestedFormat();

    rc = app.exec();

    delete component;
    return rc;

In case it is not obvious, when using a module written in C++ with QML you cannot use the qmlscene program to execute your QML code because the C++ code for the module will not be linked in. If you try to do this you will get an error message that the module is not installed.