Official feedback on OpenGL 4.4 thread

 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 http://www.opengl.org/registry.

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
(GL_ARB_query_buffer_object)
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.

OpenMP 4.0 Specifications Released

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.

Seven signs of dysfunctional engineering teams

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.

Why designed a front-end programming language from scratch

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.

Runtime

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.