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I am working on an application which supports many-core MIMD architectures (on consumer/desk-computers). I am currently worrying about the scaling behaviour of the application. Its designed to be massively parallel and addressing next-gen hardware. That's actually my problem. Does anyone know any software to simulate/emulate many-core MIMD Processors with >16 cores on a machine-code level? I've already implemented a software based thread sheduler with the ability to simulate multiple processors, by simple timing techniques.
I was curious if there's any software which could do this kind of simulation on a lower level preferably on an assembly language level to get better results. I want to emphasize once again that I'm only interested in MIMD Architectures. I know about OpenCL/CUDA/GPGPU but thats not what I'm looking for.
Any help is appreciated and thanks in advance for any answers.
You will rarely find all-purpose testing tools that are ALSO able to target very narrow (high-performance) corners - for a simple reason: the overhead of the "general-purpose" object will defeat that goal in the first place.
This is especially true with paralelism where locality and scheduling have a huge impact.
All this to say that I am affraid that you will have to write your own testing tool to target your exact usage pattern.
That's the price to pay for relevance.
If you are writing your application in C/C++, I might be able to help you out.
My company has created a tool that will take any C or C++ code and simulate its run-time behavior at bytecode level to find parallelization opportunities. The tool highlights parallelization opportunities and shows how parallel tasks behave.
The only mismatch is that our tool will also provide refactoring recipes to actually get to parallelization, whereas it sounds like you already have that.
My company currently runs a third-party simulation program (natural catastrophe risk modeling) that sucks up gigabytes of data off a disk and then crunches for several days to produce results. I will soon be asked to rewrite this as a multi-threaded app so that it runs in hours instead of days. I expect to have about 6 months to complete the conversion and will be working solo.
We have a 24-proc box to run this. I will have access to the source of the original program (written in C++ I think), but at this point I know very little about how it's designed.
I need advice on how to tackle this. I'm an experienced programmer (~ 30 years, currently working in C# 3.5) but have no multi-processor/multi-threaded experience. I'm willing and eager to learn a new language if appropriate. I'm looking for recommendations on languages, learning resources, books, architectural guidelines. etc.
Requirements: Windows OS. A commercial grade compiler with lots of support and good learning resources available. There is no need for a fancy GUI - it will probably run from a config file and put results into a SQL Server database.
Edit: The current app is C++ but I will almost certainly not be using that language for the re-write. I removed the C++ tag that someone added.
Numerical process simulations are typically run over a single discretised problem grid (for example, the surface of the Earth or clouds of gas and dust), which usually rules out simple task farming or concurrency approaches. This is because a grid divided over a set of processors representing an area of physical space is not a set of independent tasks. The grid cells at the edge of each subgrid need to be updated based on the values of grid cells stored on other processors, which are adjacent in logical space.
In high-performance computing, simulations are typically parallelised using either MPI or OpenMP. MPI is a message passing library with bindings for many languages, including C, C++, Fortran, Python, and C#. OpenMP is an API for shared-memory multiprocessing. In general, MPI is more difficult to code than OpenMP, and is much more invasive, but is also much more flexible. OpenMP requires a memory area shared between processors, so is not suited to many architectures. Hybrid schemes are also possible.
This type of programming has its own special challenges. As well as race conditions, deadlocks, livelocks, and all the other joys of concurrent programming, you need to consider the topology of your processor grid - how you choose to split your logical grid across your physical processors. This is important because your parallel speedup is a function of the amount of communication between your processors, which itself is a function of the total edge length of your decomposed grid. As you add more processors, this surface area increases, increasing the amount of communication overhead. Increasing the granularity will eventually become prohibitive.
The other important consideration is the proportion of the code which can be parallelised. Amdahl's law then dictates the maximum theoretically attainable speedup. You should be able to estimate this before you start writing any code.
Both of these facts will conspire to limit the maximum number of processors you can run on. The sweet spot may be considerably lower than you think.
I recommend the book High Performance Computing, if you can get hold of it. In particular, the chapter on performance benchmarking and tuning is priceless.
An excellent online overview of parallel computing, which covers the major issues, is this introduction from Lawerence Livermore National Laboratory.
Your biggest problem in a multithreaded project is that too much state is visible across threads - it is too easy to write code that reads / mutates data in an unsafe manner, especially in a multiprocessor environment where issues such as cache coherency, weakly consistent memory etc might come into play.
Debugging race conditions is distinctly unpleasant.
Approach your design as you would if, say, you were considering distributing your work across multiple machines on a network: that is, identify what tasks can happen in parallel, what the inputs to each task are, what the outputs of each task are, and what tasks must complete before a given task can begin. The point of the exercise is to ensure that each place where data becomes visible to another thread, and each place where a new thread is spawned, are carefully considered.
Once such an initial design is complete, there will be a clear division of ownership of data, and clear points at which ownership is taken / transferred; and so you will be in a very good position to take advantage of the possibilities that multithreading offers you - cheaply shared data, cheap synchronisation, lockless shared data structures - safely.
If you can split the workload up into non-dependent chunks of work (i.e., the data set can be processed in bits, there aren't lots of data dependencies), then I'd use a thread pool / task mechanism. Presumably whatever C# has as an equivalent to Java's java.util.concurrent. I'd create work units from the data, and wrap them in a task, and then throw the tasks at the thread pool.
Of course performance might be a necessity here. If you can keep the original processing code kernel as-is, then you can call it from within your C# application.
If the code has lots of data dependencies, it may be a lot harder to break up into threaded tasks, but you might be able to break it up into a pipeline of actions. This means thread 1 passes data to thread 2, which passes data to threads 3 through 8, which pass data onto thread 9, etc.
If the code has a lot of floating point mathematics, it might be worth looking at rewriting in OpenCL or CUDA, and running it on GPUs instead of CPUs.
For a 6 month project I'd say it definitely pays out to start reading a good book about the subject first. I would suggest Joe Duffy's Concurrent Programming on Windows. It's the most thorough book I know about the subject and it covers both .NET and native Win32 threading. I've written multithreaded programs for 10 years when I discovered this gem and still found things I didn't know in almost every chapter.
Also, "natural catastrophe risk modeling" sounds like a lot of math. Maybe you should have a look at Intel's IPP library: it provides primitives for many common low-level math and signal processing algorithms. It supports multi threading out of the box, which may make your task significantly easier.
There are a lot of techniques that can be used to deal with multithreading if you design the project for it.
The most general and universal is simply "avoid shared state". Whenever possible, copy resources between threads, rather than making them access the same shared copy.
If you're writing the low-level synchronization code yourself, you have to remember to make absolutely no assumptions. Both the compiler and CPU may reorder your code, creating race conditions or deadlocks where none would seem possible when reading the code. The only way to prevent this is with memory barriers. And remember that even the simplest operation may be subject to threading issues. Something as simple as ++i is typically not atomic, and if multiple threads access i, you'll get unpredictable results.
And of course, just because you've assigned a value to a variable, that's no guarantee that the new value will be visible to other threads. The compiler may defer actually writing it out to memory. Again, a memory barrier forces it to "flush" all pending memory I/O.
If I were you, I'd go with a higher level synchronization model than simple locks/mutexes/monitors/critical sections if possible. There are a few CSP libraries available for most languages and platforms, including .NET languages and native C++.
This usually makes race conditions and deadlocks trivial to detect and fix, and allows a ridiculous level of scalability. But there's a certain amount of overhead associated with this paradigm as well, so each thread might get less work done than it would with other techniques. It also requires the entire application to be structured specifically for this paradigm (so it's tricky to retrofit onto existing code, but since you're starting from scratch, it's less of an issue -- but it'll still be unfamiliar to you)
Another approach might be Transactional Memory. This is easier to fit into a traditional program structure, but also has some limitations, and I don't know of many production-quality libraries for it (STM.NET was recently released, and may be worth checking out. Intel has a C++ compiler with STM extensions built into the language as well)
But whichever approach you use, you'll have to think carefully about how to split the work up into independent tasks, and how to avoid cross-talk between threads. Any time two threads access the same variable, you have a potential bug. And any time two threads access the same variable or just another variable near the same address (for example, the next or previous element in an array), data will have to be exchanged between cores, forcing it to be flushed from CPU cache to memory, and then read into the other core's cache. Which can be a major performance hit.
Oh, and if you do write the application in C++, don't underestimate the language. You'll have to learn the language in detail before you'll be able to write robust code, much less robust threaded code.
One thing we've done in this situation that has worked really well for us is to break the work to be done into individual chunks and the actions on each chunk into different processors. Then we have chains of processors and data chunks can work through the chains independently. Each set of processors within the chain can run on multiple threads each and can process more or less data depending on their own performance relative to the other processors in the chain.
Also breaking up both the data and actions into smaller pieces makes the app much more maintainable and testable.
There's plenty of specific bits of individual advice that could be given here, and several people have done so already.
However nobody can tell you exactly how to make this all work for your specific requirements (which you don't even fully know yourself yet), so I'd strongly recommend you read up on HPC (High Performance Computing) for now to get the over-arching concepts clear and have a better idea which direction suits your needs the most.
The model you choose to use will be dictated by the structure of your data. Is your data tightly coupled or loosely coupled? If your simulation data is tightly coupled then you'll want to look at OpenMP or MPI (parallel computing). If your data is loosely coupled then a job pool is probably a better fit... possibly even a distributed computing approach could work.
My advice is get and read an introductory text to get familiar with the various models of concurrency/parallelism. Then look at your application's needs and decide which architecture you're going to need to use. After you know which architecture you need, then you can look at tools to assist you.
A fairly highly rated book which works as an introduction to the topic is "The Art of Concurrency: A Thread Monkey's Guide to Writing Parallel Application".
Read about Erlang and the "Actor Model" in particular. If you make all your data immutable, you will have a much easier time parallelizing it.
Most of the other answers offer good advice regarding partitioning the project - look for tasks that can be cleanly executed in parallel with very little data sharing required. Be aware of non-thread safe constructs such as static or global variables, or libraries that are not thread safe. The worst one we've encountered is the TNT library, which doesn't even allow thread-safe reads under some circumstances.
As with all optimisation, concentrate on the bottlenecks first, because threading adds a lot of complexity you want to avoid it where it isn't necessary.
You'll need a good grasp of the various threading primitives (mutexes, semaphores, critical sections, conditions, etc.) and the situations in which they are useful.
One thing I would add, if you're intending to stay with C++, is that we have had a lot of success using the boost.thread library. It supplies most of the required multi-threading primitives, although does lack a thread pool (and I would be wary of the unofficial "boost" thread pool one can locate via google, because it suffers from a number of deadlock issues).
I would consider doing this in .NET 4.0 since it has a lot of new support specifically targeted at making writing concurrent code easier. Its official release date is March 22, 2010, but it will probably RTM before then and you can start with the reasonably stable Beta 2 now.
You can either use C# that you're more familiar with or you can use managed C++.
At a high level, try to break up the program into System.Threading.Tasks.Task's which are individual units of work. In addition, I'd minimize use of shared state and consider using Parallel.For (or ForEach) and/or PLINQ where possible.
If you do this, a lot of the heavy lifting will be done for you in a very efficient way. It's the direction that Microsoft is going to increasingly support.
2: I would consider doing this in .NET 4.0 since it has a lot of new support specifically targeted at making writing concurrent code easier. Its official release date is March 22, 2010, but it will probably RTM before then and you can start with the reasonably stable Beta 2 now. At a high level, try to break up the program into System.Threading.Tasks.Task's which are individual units of work. In addition, I'd minimize use of shared state and consider using Parallel.For and/or PLINQ where possible. If you do this, a lot of the heavy lifting will be done for you in a very efficient way. 1: http://msdn.microsoft.com/en-us/library/dd321424%28VS.100%29.aspx
Sorry i just want to add a pessimistic or better realistic answer here.
You are under time pressure. 6 month deadline and you don't even know for sure what language is this system and what it does and how it is organized. If it is not a trivial calculation then it is a very bad start.
Most importantly: You say you have never done mulitithreading programming before. This is where i get 4 alarm clocks ringing at once. Multithreading is difficult and takes a long time to learn it when you want to do it right - and you need to do it right when you want to win a huge speed increase. Debugging is extremely nasty even with good tools like Total Views debugger or Intels VTune.
Then you say you want to rewrite the app in another lanugage - well this isn't as bad as you have to rewrite it anyway. THe chance to turn a single threaded Program into a well working multithreaded one without total redesign is almost zero.
But learning multithreading and a new language (what is your C++ skills?) with a timeline of 3 month (you have to write a throw away prototype - so i cut the timespan into two halfs) is extremely challenging.
My advise here is simple and will not like it: Learn multithreadings now - because it is a required skill set in the future - but leave this job to someone who already has experience. Well unless you don't care about the program being successfull and are just looking for 6 month payment.
If it's possible to have all the threads working on disjoint sets of process data, and have other information stored in the SQL database, you can quite easily do it in C++, and just spawn off new threads to work on their own parts using the Windows API. The SQL server will handle all the hard synchronization magic with its DB transactions! And of course C++ will perform a lot faster than C#.
You should definitely revise C++ for this task, and understand the C++ code, and look for efficiency bugs in the existing code as well as adding the multi-threaded functionality.
You've tagged this question as C++ but mentioned that you're a C# developer currently, so I'm not sure if you'll be tackling this assignment from C++ or C#. Anyway, in case you're going to be using C# or .NET (including C++/CLI): I have the following MSDN article bookmarked and would highly recommend reading through it as part of your prep work.
Calling Synchronous Methods Asynchronously
Whatever technology your going to write this, take a look a this must read book on concurrency "Concurrent programming in Java" and for .Net I highly recommend the retlang library for concurrent app.
I don't know if it was mentioned yet, but if I were in your shoes, what I would be doing right now (aside from reading every answer posted here) is writing a multiple threaded example application in your favorite (most used) language.
I don't have extensive multithreaded experience. I've played around with it in the past for fun but I think gaining some experience with a throw-away application will suit your future efforts.
I wish you luck in this endeavor and I must admit I wish I had the opportunity to work on something like this...
If you've bought into the functional programming paradigm, the chances are that you like both Erlang and Haskell. Both have purely functional cores and other goodness such as lightweight threads that make them a good fit for a multicore world. But there are some differences too.
Erlang is a commercially proven fault-tolerant language with a mature distribution model. It has a seemingly unique feature in its ability to upgrade its version at runtime via hot code loading. (Way cool!)
Haskell, on the otherhand, has the most sophisticated type system of any mainstream language. (Where I define 'mainstream' to be any language that has a published O'Reilly book so Haskell counts.) Its straightline single threaded performance looks superior to Erlang's and its lightweight threads look even lighter too.
I am trying to put together a development platform for the rest of my coding life and was wondering whether it was possible to mix Erlang and Haskell to achieve a best of breed platform. This question has two parts:
I'd like to use Erlang as a kind of fault tolerant MPI to glue GHC runtime instances together. There would be one Erlang process per GHC runtime. If "the impossible happened" and the GHC runtime died, then the Erlang process would detect that somehow and die too. Erlang's hot code loading and distribution features would just continue to work. The GHC runtime could be configured to use just one core, or all cores on the local machine, or any combination in between. Once the Erlang library was written, the rest of the Erlang level code should be purely boilerplate and automatically generated on a per application basis. (Perhaps by a Haskell DSL for example.) How does one achieve at least some of these things?
I'd like Erlang and Haskell to be able to share the same garabage collector. (This is a much further out idea than 1.) Languages that run on the JVM and the CLR achieve greater mass by sharing a runtime. I understand there are technical limitations to running Erlang (hot code loading) and Haskell (higher kinded polymorphism) on either the JVM or the CLR. But what about unbundling just the garbage collector? (Sort of the start of a runtime for functional languages.) Allocation would obviously still have to be really fast, so maybe that bit needs to be statically linked in. And there should be some mechansim to distinguish the mutable heap from the immutable heap (incuding lazy write once memory) as GHC needs this. Would it be feasible to modify both HIPE and GHC so that the garbage collectors could share a heap?
Please answer with any experiences (positive or negative), ideas or suggestions. In fact, any feedback (short of straight abuse!) is welcome.
Update
Thanks for all 4 replies to date - each taught me at least one useful thing that I did not know.
Regarding the rest of coding life thing - I included it slightly tongue in cheek to spark debate, but it is actually true. There is a project that I have in mind that I intend to work on until I die, and it needs a stable platform.
In the platform I have proposed above, I would only write Haskell, as the boilerplate Erlang would　be automatically generated. So how long will Haskell last? Well Lisp is still with us and doesn't look like it is going away anytime soon. Haskell is BSD3 open source and has achieved critical mass. If programming itself is still around in 50 years time, I would expect Haskell, or some continuous evolution of Haskell, will still be here.
Update 2 in response to rvirding's post
Agreed - implementing a complete "Erskell/Haslang" universal virtual machine might not be absolutely impossible, but it would certainly be very difficult indeed. Sharing just the garbage collector level as something like a VM, while still difficult, sounds an order of magnitude less difficult to me though. At the garbage collection model, functional languages must have a lot in common - the unbiquity of immutable data (including thunks) and the requirement for very fast allocation. So the fact that commonality is bundled tightly with monolithic VMs seems kind of odd.
VMs do help achieve critical mass. Just look at how 'lite' functional languages like F# and Scala have taken off. Scala may not have the absolute fault tolerance of Erlang, but it offers an escape route for the very many folks who are tied to the JVM.
While having a single heap makes
message passing very fast it
introduces a number of other problems,
mainly that doing GC becomes more
difficult as it has to be interactive
and globally non-interruptive so you
can't use the same simpler algorithms
as the per-process heap model.
Absolutely, that makes perfect sense to me. The very smart people on the GHC development team appear to be trying to solve part of the problem with a parallel "stop the world" GC.
http://research.microsoft.com/en-us/um/people/simonpj/papers/parallel-gc/par-gc-ismm08.pdf
(Obviously "stop the world" would not fly for general Erlang given its main use case.) But even in the use cases where "stop the world" is OK, their speedups do not appear to be universal. So I agree with you, it is unlikely that there is a universally best GC, which is the reason I specified in part 1. of my question that
The GHC runtime could be configured to
use just one core, or all cores on the
local machine, or any combination in
between.
In that way, for a given use case, I could, after benchmarking, choose to go the Erlang way, and run one GHC runtime (with a singlethreaded GC) plus one Erlang process per core and let Erlang copy memory between cores for good locality.
Alternatively, on a dual processor machine with 4 cores per processor with good memory bandwidth on the processor, benchmarking might suggest that I run one GHC runtime (with a parallel GC) plus one Erlang process per processor.
In both cases, if Erlang and GHC could share a heap, the sharing would probably be bound to a single OS thread running on a single core somehow. (I am getting out of my depth here, which is why I asked the question.)
I also have another agenda - benchmarking functional languages independently of GC. Often I read of results of benchmarks of OCaml v GHC v Erlang v ... and wonder how much the results are confounded by the different GCs. What if choice of GC could be orthogonal to choice of functional language? How expensive is GC anyway? See this devil advocates blog post
http://john.freml.in/garbage-collection-harmful
by my Lisp friend John Fremlin, which he has, charmingly, given his post title "Automated garbage collection is rubbish". When John claims that GC is slow and hasn't really sped up that much, I would like to be able to counter with some numbers.
A lot of Haskell and Erlang people are interested in the model where Erlang supervises distribution, while Haskell runs the shared memory nodes in parallel doing all the number crunching/logic.
A start towards this is the haskell-erlang library: http://hackage.haskell.org/package/erlang
And we have similar efforts in Ruby land, via Hubris: http://github.com/mwotton/Hubris/tree/master
The question now is to find someone to actually push through the Erlang / Haskell interop to find out the tricky issues.
You're going to have an interesting time mixing GC between Haskell and Erlang. Erlang uses a per-process heap and copies data between processes -- as Haskell doesn't even have a concept of processes, I'm not sure how you would map this "universal" GC between the two. Furthermore, for best performance, Erlang uses a variety of allocators, each with slightly tweaked behaviours that I'm sure would affect the GC sub-system.
As with all things in software, abstraction comes at a cost. In this case, I rather suspect you'd have to introduce so many layers to get both languages over their impedance mismatch that you'd wind up with a not very performant (or useful) common VM.
Bottom line -- embrace the difference! There are huge advantages to NOT running everything in the same process, particularly from a reliability standpoint. Also, I think it's a little naive to expect one language/VM to last you for the rest of your life (unless you plan on a.) living a short time or b.) becoming some sort of code monk that ONLY works on a single project). Software development is all about mental agility and being willing to use the best available tools to build fast, reliable code.
Although this is a pretty old thread, if readers are still interested then it's worth taking a look at Cloud Haskell, which brings Erlang style concurrency and distribution to the GHC stable.
The forthcoming distributed-process-platform library adds support for OTP-esque constructs like gen_servers, supervision trees and various other "haskell flavoured" abstractions borrowed from and inspired by Erlang/OTP.
You could use an OTP gen_supervisor process to monitor Haskell instances that you spawn with open_port(). Depending on how the "port" exited, you would then be able to restart it or decide that it stopped on purpose and let the corresponding Erlang process die, too.
Fugheddaboudit. Even these language-independent VMs you speak of have trouble with data passed between languages sometimes. You should just serialize data between the two somehow: database, XML-RPC, something like that.
By the way, the idea of a single platform for the rest of your life is probably impractical, too. Computing technology and fashion change too often to expect that you can keep using just one language forever. Your very question points this out: no one language does everything we might wish, even today.
As dizzyd mentioned in his comment not all data in messages is copied, large binaries exist outside of the process heaps and are not copied.
Using a different memory structure to avoid having separate per-process heaps is certainly possible and has been done in a number of earlier implementations. While having a single heap makes message passing very fast it introduces a number of other problems, mainly that doing GC becomes more difficult as it has to be interactive and globally non-interruptive so you can't use the same simpler algorithms as the per-process heap model.
As long as we use have immutable data-structures there is no problem with robustness and safety. Deciding on which memory and GC models to use is a big trade-off, and unfortunately there universally best model.
While Haskell and Erlang are both functional languages they are in many respects very different languages and have very different implementations. It would difficult to come up with an "Erskell" (or Haslang) machine which could handle both languages efficiently. I personally think it is much better to keep them separate and to make sure you have a really good interface between them.
The CLR supports tail call optimization with an explicit tail opcode (as used by F#), which the JVM doesn't (yet) have an equivalent, which limits the implementation of such a style of language. The use of separate AppDomains does allow the CLR to hot-swap code (see e.g. this blog post showing how it can be done).
With Simon Peyton Jones working just down the corridor from Don Syme and the F# team at Microsoft Research, it would be a great disappointment if we didn't eventually see an IronHaskell with some sort of official status. An IronErlang would be an interesting project -- the biggest piece of work would probably be porting the green-threading scheduler without getting as heavyweight as the Windows Workflow engine, or having to run a BEAM VM on top the CLR.
Why do people insist on using trivial mathematical problems like finding numbers in the Fibonacci sequence for language benchmarks? Don't these usually get optimized to relativistic speeds? Isn't the brunt of the bottlenecks usually in I/O, system API calls, operations on strings and structures, processing large quantities of data, abstract object-oriented stuff, etc?
It is a throwback to the old days, when compiler technology for what we would now call basic math was still evolving rapidly.
Now, compiler evolution is more focused on exploiting new instructions for niche operations, 64-bit math, and so on.
Micro-benchmarks such as the ones you mention were useful, though, when evaluating the efficiency of the hotspot compiler when Java was first launched, and in evaluating the efficiency of .NET versus C/C++.
Your suggestion that I/O and system calls are the likely bottlenecks is correct, at least for some space of problems. But I notice you suggested string operations. One person's irrelevant micro-benchmark is another person's critical performance metric.
EDIT: ps, I also remember using linpack and other micro-benchmarks to compare versions of the JVM, and to compare vendors of the JVM. From v4 to v5 there was a big jump in perf, I guess the JIT compiler got more effective. Also, IBM's JVM was ahead of Sun's at that time, on Windows-x86.
Because if you want to benchmark the language/compiler, these "math problems" are good indicators of the "bare speed" of the generated code. Either they use the iterative solution, which is a tight loop and indicates how well can the compiler push the instructions to the processor, or they use the recursive solution, which indicates how does it handle recursive calls of short functions (inlining, tail-recursion etc.) (although the Ackermann function is usually used for that too).
Usually, the benchmark suite for the language contain tests benchmarking other parts as well - eg. gzip compression, text searching, object creation, virtual function call, exception throw/catch benchmarks.
The other things you've noticed, syscalls and IO are usually not included because
syscalls are in fact not that slow - applications don't spend significant porion of the time in the kernel, except for test specifically targeted at them or when something is seriously wrong with the program
syscall and IO performance does not depend on the language, but rather on the OS & hardware
I'd think a simple, well-established algorithm would remove the possibility that the benchmark is biased (whether through ignorance or malice) to favor one language. It is very difficult to write a complex program in two different languages exactly the same. Testing something like the efficiency of a multithreaded application in c# vs java, for example, would require developers skilled in multithreaded development both languages, and there would still be questions as to whether the benchmark app properly represents the general case, or if it is misrepresenting a special case that only one language handles well.
Back when the sieve of eratosthanes was a popular benchmark for C compilers, I thought it would be funny if one of the compiler authors would recognize the sieve code and replace it with a pre-computed lookup.
we all writing code for single processor.
i wonder when we all are able to write code on multi processors?
what do we need (software tools, logic, algorithms) for this switching?
edit: in my view, as we do many task parallely, same way we need to convert those real life solutions(algorithms) to computer lang. just as OOPs coding did for procedural coding. OOPs is more real life coding style than procedural one. so i hope for that kind of solutions.
I think the most important requirement is a good language that has native constructs that support parallelism or one that can automatically generate parallel code. There are quite a few languages that fit that description, but none of them is popular enough to really be considered for mainstream use. That, in turn is caused by several things:
By their very nature, these languages are very different from today's imperative languages, and are therefor harder to learn (or at least seem that way).
They often lack good tools and libraries, making them unusable for any "real" project.
Of course, if it were more popular more people would be willing to learn it and there would be more support, so it's a kind of cycle that's pretty hard to break out of. I guess all we can do is hope. :)
An example of a language designed with heavy parallelization in mind is Erlang - and it's actually used in commercial projects.
What we need are natural abstractions for highly-concurrent algorithms. Actors (think: Erlang) go a long way in this direction, but they aren't a one-size-fits-all solution. Some more specific abstractions like fork/join or map/reduce can be even easier to apply to common problems.
The trick with all of these concurrency abstractions is they require functional-style programming. Concurrency doesn't mesh well with shared mutable state. As they say, "Locks considered harmful". Since most developers come from a strictly imperative background, switching to a shared-nothing continuation passing approach is often extremely challenging.
Incidentally, with respect to concurrency abstractions, Clojure has some very interesting features in this direction. Not only does it have sort-of actors, but it also defines a transactional memory model (think: databases) along with a global, atomic references mechanism. These two features allow concurrent operations to share "mutable" state without ever having to worry about locking or race conditions.
In the end, it comes down to education. Much of the needed theoretical work into concurrency abstractions has already been done, we just need to accept it. Unfortunately, as Erlang and Haskell prove, sometimes the best ideas remain relegated to an extremely fringe demographic. Hopefully efforts like Scala and Clojure will succeed in bringing the more advanced abstractions into the mainstream by sneaking them onto an existing, well-supported platform (the JVM).
Unfortunately for massive concurrent programming - unless there is a breakthrough in compilers to help, we will be throwing out a lot of what we know about algorithms (I think Don Knuth even said that). Read about Erlang for a glimpse of this possible future.
There are several tools/languages that are popular or are gaining popularity. If you use FORTRAN, C, or C++, you can use OpenMP (not too hard to implement) or the Message Passing Interface (MPI) libraries (powerful and greatest speedup potential, but also complex and difficult). OpenMP uses preprocessor directives to mark areas that can be parallelized, especially loops. MPI uses messages that pass data back and forth between processes, and the greatest difficulty is keeping everything synchronized without hitting bottlenecks and keeping processes waiting. I would say MPI is definitely on the way out, however. It's become clear in the scientific/high-performance computing communities that the speedup is rarely worth the additional development time.
As for up and coming languages, check out Fortress. It's still being designed, but the goal is to create a language even easier for scientific computing than FORTRAN. Programs will be specified in a very high level mathematical syntax. Additionally, parallelism will be implicit; the programmer will have to work to do things in serial. Plus, it's being championed by Sun and is based on java, so it will be portable.
There is no simple answer, and in many ways even the complex answers are currently inadequate or incomplete. You'll get a better answer if you are more specific about the replies you want: pointers to dev libraries and tools, instructional materials, pointers to current research projects and issues in this area, or something else?
The most important requirement is to be able to split your problem into smaller problems that can be solved independently of each other. Once you've worked out how you're going to do that, everything else is easier to think about and further questions of implementation (e.g. "parts of my calculation depend on other parts - how do I wait for them to have finished?") become concrete, specific things you can research or ask here about.
for java you can now look to Parallel Java Library or DPJ(deterministic Parallel Java!)
It will offer you great help in extracting parallelism from codes!!