Christian’s corner on HPC

Whenever Prof. Christian Bischof, the head of our institute, is on duty to give the Introduction to Programming (de) lecture for first-year Computer Science students, he is keen on giving the students a glimpse on parallel programming. Same as in 2006, I was the guest lecturer this task has been assigned to. While coping with parallelism in various aspects consumes most time of my work day, these students just started to learn Java as their first programming language. Same as in 2006, I worried about how to motivate the students and what level of detail would be reasonable, and what tools and techniques to present within a timeframe of just 1.5 hours. In the following paragraphs I briefly explain what I did, and why. The slides used in the lecture are available online: Introduction to Parallel Programming; and my student Christoph Rackwitz made a screen cast of the lecture available here (although the slides are English, I am speaking German).

Programming Language: As stated above, the target audience are first-year Computer Science students attending the Introduction to Programming course. The programming language taught in the course is Java. In a previous lecture we once tried to present the examples and exercises in C, assuming that C is very similar to Java, but the students did not like that very much. Although they were able to follow the lecture and were mostly successful in the exercises, C just felt kind of foreign to most of them. Furthermore, C is not well-used in other courses later on in the studies, except for System Programming. The problem with Java is, however, that it is not commonly used in technical computing and the native approach to parallel programming in Java is oriented more towards building concurrent (business) applications than reasoning about parallel algorithms. Despite this issue we decided to use Java in the Introduction to Parallel Programming lecture in order to keep the students comfortable, and to not mess around with the example and exercise environment already provided for them. The overall goal of the lecture was to give the students an idea of the fundamental change towards parallelism, to explain the basic concepts, and to motivate them to develop an interest in this topic. We thought this is independent from the programming language.

Parallelization Model: We have Shared-Memory parallelization and Message-Passing for Clusters. It would be great to teach both, and of course we do that in advanced courses, but I do not think it is reasonable to cover both in an introductory session. In order to motivate the growing need for parallel programming at all, the trend towards Multi-Core and Many-Core is an obvious foundation. Given that, and the requirement to allow the students to work on examples and exercises on their systems at home, we decided to discuss multicore architectures and  present one model for Shared-Memory parallel programming in detail, and just provide an overview of what a Cluster is. Furthermore, we hoped that the ability to speed-up the example programs by experiencing parallelism on their very own desktops or laptops would add some motivation. This feels more real than logging in to a remote system in our cluster. In addition, providing instructions to set up a Shared-Memory parallelization tool on a student’s laptop was expected to be simpler than for a Message-Passing environment (this turned out to be true).

Parallelization Paradigm: Given our choice to cover Shared-Memory parallelization, and the requirement to use Java and to provide a suitable environment to work on examples and exercises, we basically had three choices: (i) Java-Threads and (ii) OpenMP for Java, (iii) Parallel Java (PJ) – maybe we could have looked at some other more obscure paradigms as well, but I do not think they would have contributed any new aspects. In essence, Java-Threads are similar to Posix-Threads and Win32-Threads and are well-suited for building server-type programs, but not good for parallelizing algorithms or to serve in introductory courses. Using this model, you first have to talk about setting up threads and implementing synchronization before you can start to think parallel . I like OpenMP a lot for this purpose, but there is no official standard of OpenMP for Java. We looked at two implementations:

1. JOMP, by the Edinburgh Parallel Computing Center (EPCC). To our knowledge, this was the first implementation of OpenMP for Java. It comes as a preprocessor and is easy to use. But the development has long stopped, and it does not work well with Java 1.4 and later.
2. JaMP, by the University of Erlangen. This implementation is based on the Eclipse compiler and even extends Java for OpenMP to provide more constructs than the original standard, while still not providing full support for OpenMP 2.5. Anyhow, it worked fine with Java 1.6, was easy to install and distribute among the students and thus we used it in the lecture.

Parallel Java (short: PJ), by Alan Kaminsky at the Rochester Institute of Technology, also provides means for Shared-Memory parallelization, but in principle it is oriented towards Message-Passing. Since it provides a very nice and simplified MPI-style API, we would have used it if we included Cluster programming, but sticking to Shared-Memory parallelization we went for JaMP.

Content: What should be covered in just 1.5 hours? Well, of course we need a motivation in the beginning of why parallel programming will be more and more important in the future. We also explained why the industry is shifting towards multicore architectures, and what implications this will or may have. As explained above, the largest part of the lecture was spent on OpenMP for Java along with some examples. We started with a brief introduction on how to use JaMP and how OpenMP programs look like, then covered Worksharing and Data Scoping with several examples. I think experiencing a Data Race is a very important thing every parallel programmer should have made , as well as learning about reductions. This was about it for the OpenMP part then. The last minutes of the lecture were spent on clusters and their principle ideas, followed by a Summary.

Given the constraints and our reasoning outlined above, we ended up using Java as the programming language and JaMP as the paradigm to teach Shared-Memory parallelization; just mentioning that there are Clusters as well. Although the official course evaluation is not done yet, we got pretty positive feedback regarding the lecture itself, and the exercises were well-accepted.What unnerves me is the fact, that there is no real OpenMP for Java. The Erlangen team provided a good implementation along with a compiler to serve our example and exercises, but it does not provide full OpenMP 2.5 support, not to speak of OpenMP 3.0. Having a full OpenMP for Java implementation at hand would be a very valuable tool for teaching parallel programming to first-year students, since Java is the language of choice not only at RWTH Aachen University.

Do you have other opinions, experiences, or ideas? I am always in for a discussion.

Not yet carved in stone, but the current plan of the OpenMP Language Committee (LC) is to publish a draft OpenMP 3.1 standard for public comment by IWOMP 2010 and to have the OpenMP 3.1 specification finished for SC 2010 – given that the Architecture Review Board (ARB) accepts the new version. Bronis R. de Supinski (LLNL) has taken on the duty of acting as the chair of the LC and since introduced some process changes. Besides weekly telephone conference calls, there are three face-to-face meetings per year and attendance is required for voting rights. The first face-to-face meeting was held on June 1st and 2nd in Dresden attached to IWOMP 2009, the second one was on September 22nd and 23rd in Chicago. This blog post is intended to report on this last meeting and to present an overview of what is going on with OpenMP right now, obviously from my personal point of view.

In the course of resuming work on OpenMP after the 3.0 specification was published, the LC voted on the priority of (small) extensions and clarifications for 3.1 as well as new topics for 4.0. We ended up with 12 major topics and 5 subcommittees, as outlined in Bronis talk during IWOMP 2009, which are still in use as identifiers of the different topics people are working on.

1: Development of an OpenMP Error Model. This is the feature the LC people think OpenMP is missing most desperately, but in contrast to that it did not receive too much effort yet. A subcommittee has been formed to be lead by Tim Mattson (Intel) and Michael Wong (IBM), and currently there are three proposals on the table for discussion: (i) an extension of the API routines and some constructs to return error codes or the introduction of a global error indication variable, (ii) an exception-based mechanism to catch errors, and (iii) a callback-based mechanism allowing to react on errors based on the severity and origin. The absence of an error model is clearly a reason for not using OpenMP in applications with certain requirements on reliability, but introducing the wrong error model could easily spoil OpenMP for that audience. It seems that most LC people do not like error codes too much (I don’t either), using exceptions is not suitable for C and FORTRAN, so the third approach seems most promising by allowing a program to react on errors depending on the severity and to still allow the compiler to ignore OpenMP if it is not enabled. In fact, this mechanism has been proposed back in 2006 by Alex Duran (BSC) and friends already. Since nothing has been decided yet, I guess the error model is targeted for OpenMP 4.0.

2: Interoperability and Composability. This subcommittee is lead by myself (RWTH) and Bronis R. de Supinski (LLNL) and is looking for ways of allowing OpenMP to coexist with other threading packages, maybe even with other OpenMP runtime environments in the same application. We are also looking into how to allow the creation of parallel software components that can safely be plugged together, which I consider prominently missing in virtually all threading paradigms. This is a very broad topic and there is no OpenMP version number I would assign this topic as target for being solved to, but with a little bit of luck we can make some progress even for version 3.1. We have some ideas on the table of how to specify some basic aspects of OpenMP interacting with the native threading packages (POSIX-Threads on Linux/Unix, Win32-Threads on Windows), driven by application observations and known deficiencies in current OpenMP implementations. We might also attack the problem of orphaned reductions. I am not so certain of solving the issue of allowing or detecting nested Worksharing constructs, respectively.

3: Incorporating Tools Support into the OpenMP Specification. This has been on the feature wishlist for OpenMP 3.0 already, but there is hardly any activity regarding this topic. Most vendors provide their own tools to analyze the performance (or correctness) of OpenMP programs by making their own runtime talk to their specific tool, but this situation is far from optimal for research / academia tools. As early as back in 2004 there were some proposal (i.e. POMP by Bernd Mohr and friends), but they did not made it into the specification or into actual implementations.

4: Associating Computation or Memory across Workshares. Today, the world of OpenMP is flat (memory), so this topic is mostly about supporting cc-NUMA architectures in OpenMP. There are two subcommittees working on this issue, the first is lead by Dieter an Mey (RWTH) and the goal is to standardize common practices (used in today’s applications) of dealing with cc-NUMA optimizations. If nothing comes in between, OpenMP 3.1 will allow the user to bind threads to cores by either specifying an explicit mapping, or by telling the runtime a strategy (like compact vs. scatter). Of course there are more ideas (and features needed), like influencing the memory allocation scheme or using page migration if supported by the operating system or interacting with resource management systems (batch queuing systems), but these are very hard to specify in a portable and extensible fashion. The other subcommittee is lead by Barbara Chapman (UH) and deals with thread team control. Using the Worksharing in OpenMP, it is very hard to dedicate a special task (i.e. I/O) to just one thread of the Parallel Region. There are applications asking for that, but I don’t see a proposal that the LC would agree on for 3.1. Nevertheless, they presented some interesting ideas at the last F2F based based on HPCS language capabilities, which hopefully have the potential to influence OpenMP 4.0.

5: Accelerators, GPUs and More. Of course we have to follow the trend / hype . But since no one knows for sure in which directions the hardware is evolving, there are so many different ideas on how to deal with this. Out of my head I can enumerate that PGI has some directives loosely based on OpenMP Worksharing (plus they have CUDA for FORTRAN), IBM has OpenMP for cell with several ideas on extensions, BSC has a proposal that is in principle based on their *SS concept, and CAPS Entreprise has the HMPP constructs + compiler. In summary: No clear direction yet, nothing for OpenMP in the scope of 3.1.

6: Transactional Memory and Thread Level Speculation. Some people thought that OpenMP might need something for Transactional Memory. To the best of my knowledge no one from the LC did any work on this regard.

7: Refinements to the OpenMP Tasking Model. There are two things that most people agree Tasks are missing: Dependencies and Reductions. With respect to the former, there were three proposals on the table from Grant Haab (Intel), Federico Massaioli (Caspur) and Alex Duran (BSC) and the BSC proposal looks most promising because it avoid deadlocks. It employs existing program variables to define the dependencies between tasks, i.e. the result of a computation can be the input of another task. With a good portion of luck, Task Dependencies could actually make it into OpenMP 3.1, I think. With respect to the latter thing, namely Task Reductions, there has been only little progress so far.

8: Extending OpenMP to C++0x and FORTRAN 2003. Since the C++0x standard dropped Concepts, the work that Michael Wong (IBM) and myself (RWTH) made so far became obsolete. To the best of my knowledge there has been no progress made with respect to investigate the opportunities or issues that could arise with FORTRAN 2003.

9: Extending OpenMP to Additional Languages. Well, there are Java and C#, and at least for Java there are some implementations of OpenMP available (incomplete, though). Anyhow, there was never any real attempt to write a formal specification of OpenMP for Java, nor for C#, and I don’t think there is one now.

10: Clarifications to the Existing Specifications. The LC already approved several minor corrections (i.e. mistakes in the examples, improvements in the wording, and the like) that will make their way into OpenMP 3.1. Nothing spectacular, though, but this is something that has to be done.

11: Miscellaneous Extensions. I might be wrong, but I think that User-defined Reductions (UDR) belong to this topic. Yes, there is a chance that UDRs will make it into OpenMP 3.1! This will bring obvious things like min and max for C and C++, but we are aiming higher: The goal is to enable the programmer to write any type of reduction operation for any type in the base language (including non-PODs) and this is achieved by introducing an OpenMP declare statement to define a reduction operation that can be specified in a reduction clause. There are two problems that are under discussion right now: (i) C++ templates and (ii) pointers / arrays. The first can be addressed by an extension of the current proposal and I got the feeling that most LC people like the new approach, but the second is a bit more complex. If you want to reduce an array that is described by a pointer, you need to know how much space to allocate for the thread private copy and how many elements the array consists of. There has been some discussion on this, but no strong agreement on how to solve this issue in general, as it also arises with the private, firstprivate, … clauses. We only agreed that we need a one-fits-all solution. With some good portion of luck we can solve this issue, otherwise we hopefully get UDRs with some limitations in OpenMP 3.1 and the full functionality in a later version of the specification.

12: Additional Task / Threads Synchronization Mechanisms. Again I might be wrong, but I think that the Atomic Extension proposal by Grant Haab (Intel) belongs in here. This is a feature you will also find in threading-aware languages (such as C++0x), but the current base languages of OpenMP are not of that kind. This will almost certainly make it into OpenMP 3.1 and will allow for a portable way to write atomic updates that capture a value and atomic writes. This is already supported by most machines and using an atomic operations can be so much more efficient than using a Critical Region.

If you are interested in more details, you are invited to stop by the OpenMP booth at SC 2009 in Portland and ask the nice guy on booth duty some good questions .

As announced in a previous post already, I was involved in two workshops attached to the HPCS 2009, hosted by the HPCVL in Kinston, ON, Canada. Being back in the office now I found some time to upload my slide sets. Obviously I can only make my own slides public.

Using OpenMP 3.0 for Parallel Programming on Multicore Systems [abstract]

Ruud van der Pas, Sun Microsystems; Dieter an Mey and Christian Terboven, RWTH Aachen University.

• Tasking in OpenMP 3.0 (Christian Terboven).
• Data Race Detection in OpenMP programs using the Sun Thread Analyzer (Christian Terboven).
• OpenMP in the Real World (Christian Terboven and Dieter an Mey).

Parallel Programming in Visual Studio 2008 on Windows HPC Server 2008 [abstract]

Christian Terboven, RWTH Aachen University.

• Windows HPC Server 2008: Overview (Christian Terboven).
• Windows HPC Server 2008: user’s point of view (Christian Terboven).
• Using Microsoft Visual Studio 2008 (Christian Terboven).
• HPC Tools Portfolio: Shared-Memory Parallelization on Windows (Christian Terboven).
• HPC Tools Portfolio: Message-Passing with MPI on Windows (Christian Terboven).
• Case Studies … and an Outlook into the Future (Christian Terboven).

Although it is high time to deliver the second part of this blog post series, I decided to squeeze in one additional post which I named part “1.5″, as it will cover some experiments with SMXV in C#. Since I am currently preparing a lecture named Multi-Threading for Desktop Systems (it will be held in German, though) in which C# plays an important role, we took a closer look into how parallelism has made it’s way into the .NET framework version 3.5 and 4.0. The final post will then cover some more tools and performance experiments (especially regarding cc-NUMA architectures) with the focus back on native coding.

First, let us briefly recap how the SMXV was implemented and examine how this can look like in C#. As explained in my previous post, the CRS format stores just the nonzero elements of the matrix in three vectors: The val-vector contains the values of all nonzero elements, the col-vector contains the column indices for each nonzero element and the row-vector points to the first nonzero element index (in val and col) for each matrix row. Having one class to represent a CRS matrix and using an array of doubles to represent a vector, the SMXV operation encapsulated by the operator* can be implemented like this, independent of whether you use managed or unmanaged arrays:

public static double[] operator *(matrix_crs lhs, double[] rhs)
{

   double[] result = new double[lhs.getNumRows()];

   for (long i = 0; i < lhs.getNumRows(); ++i)

   {

      double sum = 0;

      long rowbeg = lhs.row(i);

      long rowend = lhs.row(i + 1);

      for (long nz = rowbeg; nz < rowend; ++nz)

         sum += lhs.val(nz) * rhs[ lhs.col(nz) ];

      result[i] = sum;

   }

   return result;
}

We have several options to parallelize this code, which I wil present and briefly discuss in the rest of this post.

Threading. In this approach, the programmer is responsible for managing the threads and distributing the work onto the threads. It is not too hard to implement a static work-distribution for any given number of threads, but implementing a dynamic or adaptive work-distribution is a lot of work and also error-prone. In order to implement the static approach, we need an array of threads, have to compute the iteration chunk for each thread, put the threads to work and finally wait for the threads to finish their computation.

//Compute chunks of work:Thread[] threads = new Thread[lhs.NumThreads];

long chunkSize = lhs.getNumRows() / lhs.NumThreads;

//Start threads with respective chunks:

for (int t = 0; t < threads.Length; ++t)

{

   threads[t] = new Thread(delegate(object o)

   {

      int thread = (int)o;

      long firstRow = thread * chunkSize;

      long lastRow = (thread + 1) * chunkSize;

      if (thread == lhs.NumThreads - 1) lastRow = lhs.getNumRows();

      for (long i = firstRow; i < lastRow; ++i)

      { /* ... SMXV ... */ }

   });

   //Start the thread and pass the ID:
   threads[t].Start(t);

}

//Wait for all threads to complete:

for(int t = 0; t < threads.Length; ++t) threads[t].Join();
return result;

Instead of managing the threads on our own, we could use the thread pool of the runtime system. From a usage point of view, this is equivalent to the version shown above, so I will not discuss this any further.

Tasks. The problem of the approach discussed above is the static work-distribution that may lead to load imbalances, and implementing a dynamic work-distribution is error-prone and depending on the code it also may be a lot of work. The goal should be to distribute the workload into smaller packages, but doing this with threads is not optimal: Threads are quite costly in the sense that creating or destroying a thread takes quite a lot of time (in computer terms) since the OS is involved, and threads also need some amount of memory. A solution for this problem are Tasks. Well, tasks are quite “in” nowadays with many people thinking on how to program multicore systems and therefore there are many definitions of what a task really is. I have given mine in previous posts on OpenMP and repeat it here briefly: A task is a small package consisting of some code to execute and some private data (access to shared data is possible, of course) which the runtime schedules for execution by a team of threads. Actually it is pretty simple to parallelize the code from above using tasks: We have to manage a list of tasks and have to decide how much work a task should do (in terms of matrix lines), and of course we have to create and start the tasks and finally wait for them to finish. See below:

//Set the size of the tasks:

List<Task> taskList = new List<Task>();

int chunkSize = 1000;

//Create the tasks that calculate the parts of the result:

for (long i = 0; i < lhs.getNumRows(); i += chunkSize)

{

   taskList.Add(Task.Create(delegate(object o)

   {

      long chunkStart = (long)o;

      for(long index = (long)chunkStart;

      index < System.Math.Min(chunkStart + chunkSize, lhs.getNumRows()); index++)

      { /* ... SMXV ... */ }

   }, i));

}

//Wait for all tasks to finish:

Task.WaitAll(taskList.ToArray());
return result;

Using the TPL. The downside of the approach discussed so far is that we (= the programmer) has to distribute the work manually. In OpenMP, this is done by the compiler + runtime – at least when Worksharing constructs can be employed. In the case of for-loops, one would use Worksharing in OpenMP, With the upcoming .NET Framework version 4.0 there will be something similar (but not so powerful) available for C#: The Parallel class allows for the parallelization of for-loops, when certain conditions are fulfilled (always think about possible Data Races!). Using it is pretty simple thanks to support for delegates / lambda expressions in C#, as you can see below:

Parallel.For(0, (int)lhs.getNumRows(), delegate(int i)

{
   /* ... SMXV ... */

});
return result;

Nice? I certainly like this! It is very similar to Worksharing in the sense that you instrument your code with further knowledge to (incrementally) add parallelization, while it is also nicely integrated in the core language (which OpenMP isn’t). But you have to note that this Worksharing-like functionality is different from OpenMP in certain important aspects:

• Tasks are used implicitly. There is a significant difference between using tasks underneath to implement this parallel for-loop, and Worksharing in OpenMP: Worksharing uses explicit threads that can be bound to cores / numa nodes, while tasks are scheduled onto threads on the behalf of the runtime system. Performance will be discussed in my next blog post, but tasks can easily be moved between numa nodes and that can spoil your performance really. OpenMP has no built-in support for affinity, but the tricks how to deal with Worksharing on cc-NUMA architectures are well-known.
• Runtime system has full control. To my current knowledge, there is no reliably way of influencing how many threads will be used to execute the implicit tasks. Even more: I think this is by design. While it is probably nice for many users and applications when the runtime figures out how many threads should be used, this is bad for the well-educated programmer as he often has better knowledge of the application than the compiler + runtime could ever figure out (about data access pattern, for instance). If you want to fine-tune this parallelization, you have hardly any option (note: this is still beta and the options may change until .NET 4.0 will be released). In OpenMP, you can influence the work-distribution in many aspects.

PLINQ. LINQ stands for language-integrated query and allows for declarative data access. When I first heard about this technology, it was demonstrated in the context of data access and I found it interesting, but not closely related to the parallelism I am interested in. Well, it turned out that PLINQ (+ parallel) can be used to parallelize a SMXV code as well (the matrix_crs class has to implement the IEnumerable / IParallelEnumerable interface):

public static double[] operator *(matrix_crs_plinq lhs, double[] rhs)

{

   var res = from rowIndices in lhs.AsParallel().AsOrdered()

             select RowSum(rowIndices, lhs, rhs);

   double[] result = res.ToArray();

   return result;
}

public static double RowSum(long[] rowIndices, matrix_crs_plinq lhs, double[] rhs)

{

   double rowSum = 0;

   for (long i = rowIndices[0]; i < rowIndices[1]; i++)

   {

      rowSum += lhs.val(i) * rhs[lhs.col(i)];

   }

   return rowSum;
}

Did you recognized the AsParallel() in there? That is all you have to do, once the required interfaces have been implemented. Would I recommend using PLINQ for this type of code? No, it is meant to parallelize queries on object collections and more general data sources (think of databases). But (for me at least) it is certainly interesting to see this paradigm applied to a code snippet from the scientific-technical world. As PLINQ uses TPL internally, you will probably have the same issues regarding locality, although I did not look into this too closely yet.

Let me give credit to Ashwani Mehlem, who is one of the student workers in our group. He did some of the implementation work (especially the PLINQ version) and code maintenance of the experiment framework.

Some people are complaining that OpenMP’s approach of using pragmas to annotate a program is not very nice, as pragmas / the OpenMP directives are not well-integrated into the language. Personally, I like the OpenMP approach and think it has some specific advantages. But I am also very interested in researching how the OpenMP language bindings could be improved, especially for C++. This post is about using C++0x features to build parallelization constructs that have been praised in the context of other approaches (e.g. Parallel Extensions for C#, or Intel’s Threading Building Blocks), but using OpenMP constructs.

Let’s consider the following sequential loop which is very similar to the example used in the Microsoft Parallel Extensions to the .NET Framework 3.5 (June 2008) documentation:

01   double dStart, dEnd;
02   for (int rep = 0; rep < iNumRepetitions; rep++)
03   {04       dStart = omp_get_wtime();
05       for (int i = 0; i < iNumElements; i++)
06       {
07           vec[i] = compute(vec[i], iNumIterations);
08       }
09       dEnd = omp_get_wtime();10   }

The experiment loop (line 05 to line 08) is executed iNumRepetitions times, the time is taken in line 04 and 09 using OpenMP time measurement functions (portability!), and the time required for each element can be controlled via iNumIterations. I will use that parametrization for my performance experiments – for now let’s just look at how this would be parallelized in OpenMP:

#pragma omp parallel for shared(iNumElements, vec, iNumIterations)
        for (int i = 0; i < iNumElements; i++)
        {
            vec[i] = compute(vec[i], iNumIterations);
        }

Pretty straight forward – as this parallel loop is perfectly balanced, we do not need the schedule clause here. How could that loop look like without using pragmas? Maybe as shown here:

omp_pfor (0, iNumElements, [&](int i)
{
    vec[i] = compute(vec[i], iNumIterations);
});

Do you like that? No OpenMP pragma is visible in the user’s code, he just has to specify the loop iteration space and the loop variable, the parallelization is done “under the hood”. The implementation of this is pretty simple using lambda functions of C++0x:

template<typename F>
void omp_pfor(int start, int end, F x)
{
#pragma omp parallel for
    for(int __i = start; __i < end; __i++)
    {
        x(__i);
    }
}

Of course I am still using OpenMP directives here, but they are hidden as an implementation detail. The actual loop body is passed as an argument to the omp_pfor lambda function, as well as the loop boundaries. Please note that this is just a very simple example, of course one can handle all types of loops that are currently supported in OpenMP 3.0 (any maybe even more) and STL-type algorithms!

In this post I only talked about syntax, but there is more to it. A part of my research is looking into how programmers (especially from the background of computation engineering science in Aachen) can be provided with more powerful language-based tools to ease writing parallel and reusable code / components. I am always happy to discuss on such a topic – if you like the Live Space comment functionality as little as I do, just drop me a mail at christian@terboven.com.

You can download this example code from my website. In order to compile that code, I recommend using the latest Intel 11.0 beta compiler.