Is there any real advantage from Bytecode JIT execution over native code beside possible implementation of platform independency?
Apparently languages that use "virtual machines" over Bytecode JIT execution are attributed several advantages. But in what extend would this really affect a discussion concerning advantages/disadvantages of native code and JIT execution?
Here's a list of attributes that I identify, the question is in what extend does this also apply to native code - if the compiler supports it...
Security
VM runtime environments can observe the running application for e.g. buffer overflows.
So first question is if this is done by the "runtime environment", means e.g. by the class library or during JIT execution?
Memory bounds-checking exist also for native code compilers. Any other/general restrictions here?
Optimization
Classical optimization should be possible in native code compilers, too. See LLVM which in fact uses the generated bitcode to run optimization on, before compiling to native code.
Maybe there would be something like a dynamic optimization in JIT by e.g. identifying things to optimize related to the execution context. Could maybe be possible for a native compiler, too, to generate some code to optimize the execution during runtime. But dont't know if something like this is implemented.
Popular VM implementations do this - the question is if this really excuses a real advantages over native code.
Threading
I don't cound this while threading also in a VM is dependent on the native Thread implementation of the OS.
If we identify that there is no real advantage over native code and that there is always a runtime drawback in JIT... than this leads to the next question:
Does an operating system design based on JIT execution (e.g. Singularity, Cosmos, ...) make sense?
I could maybe identify one advantages: An OS with this design needs no MMU. Means there is no process seperation that makes use of the MMU, but a seperation between objects/components in software. But is it worth it?
Best regards ;-)
Theoretically, they could take advantage of the platform/CPU they run on, to compile faster code.
In practice, I personally haven't come across any case in which that actually happens.
But there's also other issues. Higher-level languages that compile into bytecode also happen to have garbage collection, which is very useful in some domains. It's not because of the JIT compiler per se, but having a JITter makes it a lot easier practically because often the language is easier for a JITter to analyze and figure out e.g. where pointers go on the stack, etc.
Related
I've read through the llvm Kaleidoscope tutorial, but it's about how to use their tools. I'm looking for a way to write my own code that allow me to take an abstract syntax tree and generate the llvm IR.
Unfortunately, I'm a little lost on how to go about doing that. My current idea was to have each node of my AST do a fill in the blank style string generation. However this seems inelegant, and there is probably a better way to do this.
I read this question which is simalar to mine, but from my understanding of the llvm IR, which could be completely off, is that it behaves similar to a higher level language than traditional assembly languages, with it having functions, and variables(infinite registers). So I think different techniques may apply.
Not inelegant at all. Generally speaking: at this point most compilers are likely going to generate some form of IR or VM instruction set which likely gets optimized based on well documented approaches. At the end, the compiler translates that result/outcome to the target machine code.
Think of LLVM-IR as that internal IR that you are going to generate and let the toolchain take care of optimizing and creating machine code.
I am going through Scala book by Martin Odersky.
It states that Scala language is highly scalable,reason being that it allows users to add new features which can be utilised as native language support.
It has got me confused with the term 'Scalability'.
I understand that scalability means ability of a software to handle huge amount of data.
So what's the difference here?
In the context of Scala, Odersky usually means that it is scalable in the sense that it can be used for a wide range of tasks, from simple scripting to large libraries to behemoth enterprise applications.
It's good for scripting because of its type inference, relatively low verbosity (compared to Java), and functional style (which generally lends itself to more concise code).
It's good for medium size applications and libraries because of its powerful type system, which means it is possible to write code that mostly or only produces errors at compile time rather than runtime (to the extent that is possible). The Play! framework in particular is founded on this philosophy. Furthermore, Scala runs on the JVM and therefore can harness any of the many, many Java libraries out there.
And it's good for enterprise software because it compiles to JVM bytecode, which already has a great track record in enterprise software; further, the fact that it's statically typed makes the maintenance of very large codebases much easier.
Scala is also applicable to a number of other areas, making it even more "scalable": concurrency/parallelism and domain-specific languages come to mind.
Here is a presentation by Odersky, if you start at slide 6 and go forward, you'll see him explain some other uses of Scala as well.
I'm doing some research on multicore processors; specifically I'm looking at writing code for multicore processors and also compiling code for multicore processors.
I'm curious about the major problems in this field that would currently prevent a widespread adoption of programming techniques and practices to fully leverage the power of multicore architectures.
I am aware of the following efforts (some of these don't seem directly related to multicore architectures, but seem to have more to do with parallel-programming models, multi-threading, and concurrency):
Erlang (I know that Erlang includes constructs to facilitate concurrency, but I am not sure how exactly it is being leveraged for multicore architectures)
OpenMP (seems mostly related to multiprocessing and leveraging the power of clusters)
Unified Parallel C
Cilk
Intel Threading Blocks (this seems to be directly related to multicore systems; makes sense as it comes from Intel. In addition to defining certain programming-constructs, it also seems have features that tell the compiler to optimize the code for multicore architectures)
In general, from what little experience I have with multithreaded programming, I know that programming with concurrency and parallelism in mind is definitely a difficult concept. I am also aware that multithreaded programming and multicore programming are two different things. in multithreaded programming you are ensuring that the CPU does not remain idle (on a single-CPU system. As James pointed out the OS can schedule different threads to run on different cores -- but I'm more interested in describing the parallel operations from the language itself, or via the compiler). As far as I know you cannot truly do parallel operations. In multicore systems, you should be able to perform truly-parallel operations.
So it seems to me that currently the problems facing multicore programming are:
Multicore programming is a difficult concept that requires significant skill
There are no native constructs in today's programming languages that provide a good abstraction to program for a multicore environment
Other than Intel's TBB library I haven't found efforts in other programming-languages to leverage the power of multicore architectures for compilation (for example, I don't know if the Java or C# compiler optimizes the bytecode for multicore systems or even if the JIT compiler does that)
I'm interested in knowing what other problems there might be, and if there are any solutions in the works to address these problems. Links to research papers (and things of that nature) would be helpful. Thanks!
EDIT
If I had to condense my question down to one sentence, it would be this: What are the problems that face multicore programming today and what research is going on in the field to solve these problems?
UPDATE
It also seems to me that there are three levels where multicore needs to be concerned:
Language level: Constructs/concepts/frameworks that abstract parallelization and concurrency and make it easy for programmers to express the same
Compiler level: If the compiler is aware of what architecture it is compiling for, it can optimize the compiled code for that architecture.
OS level: The OS optimizes the running process and perhaps schedules different threads/processes to run on different cores.
I've searched on ACM and IEEE and have found a few papers. Most of them talk about how difficult it is to think concurrently and also how current languages don't have a proper way to express concurrency. Some have gone so far as to claim that the current model of concurrency that we have (threads) is not a good way to handle concurrency (even on multiple cores). I'm interested in hearing other views.
I'm curious about the major problems in this field that would currently prevent a widespread adoption of programming techniques and practices to fully leverage the power of multicore architectures.
Inertia. (BTW: that's pretty much the answer to all "what does prevent the widespread adoption" questions, whether that be models of parallel programming, garbage collection, type safety or fuel-efficient automobiles.)
We have known since the 1960s that the threads+locks model is fundamentally broken. By ~1980, we had about a dozen better models. And yet, the vast majority of languages that are in use today (including languages that were newly created from scratch long after 1980), offer only threads+locks.
The major problems with multicore programming is the same as writing any other concurrent applications, but whereas before it was uncommon to have multiple cpus in a computer, now it is hard to find any modern computer with only one core in it, so, to take advantage of multicore, multiple cpu architectures there are new challenges.
But, this problem is an old problem, whenever computer architectures go beyond compilers then it seems the fallback solution is to move back toward functional programming, as that programming paradigm, if strictly followed, can make very parallelizable programs, as you don't have any global mutable variables, for example.
But, not all problems can be done easily using FP, so the goal then is how to easily get other programming paradigms to be easy to use on multicores.
The first thing is that many programmers have avoided writing good mulithreaded applications, so there isn't a strongly prepared number of developers, as they learned habits that will make their coding harder to do.
But, as with most changes to the cpu, you can look at how to change the compiler, and for that you can look at Scala, Haskell, Erlang and F#.
For libraries you can look at the parallel framework extension, by MS as a way to make it easier to do concurrent programming.
It is at work, but I recently either IEEE Spectrum or IEEE Computer had articles on multicore programming issues, so look at what IEEE and ACM articles have been written on these issues, to get more ideas as to what is being looked at.
I think the biggest impediment will be the difficulty to get programmers to change their language as FP is very different than OOP.
One place for research besides developing languages that will work well this way, is how to handle multiple threads accessing memory, but, as with much in this area, Haskell seems to be at the forefront in testing ideas for this, so you can look at what is going on with Haskell.
Ultimately there will be new languages, and it may be that we have DSLs to help abstract the developer more, but how to educate programmers on this will be a challenge.
UPDATE:
You may find Chapter 24. Concurrent and multicore programming of interest, http://book.realworldhaskell.org/read/concurrent-and-multicore-programming.html
One of the answers mentioned the Parallel Extensions for the .NET Framework and since you mentioned C#, it's definitely something I would investigate. Microsoft has done something interesting things there, though I have to think many of their efforts seem more suited for language enhancements in C# than a separate and distinct library for concurrent programming. But I think their efforts are worth applauding and respect that we're early here. (Disclaimer: I used to be the marketing director for Visual Studio about 3 years ago)
The Intel Thread Building Blocks are also quite interesting (Intel recently released a new version, and I'm excited to head down to Intel Developer Forum next week to learn more about how to use it properly).
Lastly, I work for Corensic, a software quality startup in Seattle. We've got a tool called Jinx that is designed to detect concurrency errors in your code. A 30-day trial edition is available for Windows and Linux, so you might want to check it out. (www.corensic.com)
In a nutshell, Jinx is a very thin hypervisor that, when activated, slips in between the processor and operating system. Jinx then intelligently takes slices of execution and runs simulations of various thread timings to look for bugs. When we find a particular thread timing that will cause a bug to happen, we make that timing "reality" on your machine (e.g., if you're using Visual Studio, the debugger will stop at that point). We then point out the area in your code where the bug was caused. There are no false positives with Jinx. When it detects a bug, it's definitely a bug.
Jinx works on Linux and Windows, and in both native and managed code. It is language and application platform agnostic and can work with all your existing tools.
If you check it out, please send us feedback on what works and doesn't work. We've been running Jinx on some big open source projects and already are seeing situations where Jinx can find bugs 50-100 times faster than simply stress testing code.
The bottleneck of any high-performance application (written in C or C++) designed to make efficient use of more than one processor/core is the memory system (caches and RAM). A single core usually saturates the memory system with its reads and writes so it is easy to see why adding extra cores and threads causes an application to run slower. If a queue of people can pass through a door one a time, adding extra queues will not only clog the door but also make the passage of any one individual through the door less efficient.
The key to any multi-core application is optimization of and economizing on memory accesses. This means structuring data and code to work as much as possible inside their own caches where they don't disturb the other cores with acceses to the common cache (L3) or RAM. Once in a while a core needs to venture there but the trick is to reduce those situations as much as possible. In particular, data needs to be structured around and adapted to cache lines and their sizes (currently 64 bytes) and code needs to be compact and not call and jump all over the place which also disrupts pipelines.
My experience is that efficient solutions are unique to the application in question. The generic guidelines (above) are a basis on which to construct code but the tweak changes resulting from profiling conclusions will not be obvious to those who were not themselves involved in the optimizing work.
Look up fork/join frameworks and work-stealing runtimes. Two names for the same, or at least related, approaches, which is to recursively subdivide large tasks into lightweight units, such that all available parallelism is exploited, without having to know in advance how much parallelism there is. The idea is that it should run at serial speed on a uniprocessor, but get a linear speedup with multiple cores.
Sort of a horizontal analogue of cache-oblivious algorithms if you look at it right.
But i'd say the main problem facing multicore programming is that the great majority of computations remain stubbornly serial. There's just no way to throw multiple cores at those computations and make them stick.
I've worked on a number of products that make use of code generation. It seems to be the only way to achieve both a high degree of user-customizability and high execution speed.
The downside is that we are requiring users to install a compiler (primarily on MS Windows).
This has been an on-going headache, because vendors like MS keep obsoleting compilers, and some users tend to have more than one compiler installed.
We're considering using GNU C, and possibly C++, but even there, there are continual version issues.
I've considered possibly generating assembly language, in an effort to get off the compiler-version-treadmill, but assembly languages are all machine-specific.
Ideally there would be some way to produce generated code that would be flexible, run fast, and not expose us to the whims of third-party providers.
Maybe I'm overlooking something simple, like Java. Any ideas would be appreciated. Thanks.
If you're considering C and even assembler, take a look at LLVM first: http://llvm.org
I might be missing some context here, but could you just pin yourself to a specific version? E.g., .NET 2.0 can be installed side by side with .NET 1.1 and .NET 3.5, as well as other versions that will come out in the future. So as long as your code makes use of a specific version of a compiler, what's the problem?
I've considered possibly generating assembly language, in an effort to get off the compiler-version-treadmill, but assembly languages are all machine-specific.
That would be called a compiler :)
Why don't you stick to C90?
I haven't heard much of severe violations of standards from gcc's side, if you don't use extensions.
And you can always distribute a certain version of gcc along with your product, say, 4.3.2, giving an option to users to use their own compiler at their own risk.
As long as all code is generated by you (i. e. you don't embed your instructions into other's code), there shouldn't be any problems in testing against this version and using it to compile your libraries.
If you want to generate assembly language code, you may take a look at asmjit.
One option would be to use a language/environment that provides access to the compiler in code; For example, here is a C# example.
Why not ship a GNU C compiler with your code generator? That way you have no version issues, and the client can constantly generate code that is usable.
It sounds like you're looking for LLVM.
Start here: The Code Generation conference
In the spirit of "might not be to late to add my 2 cents" as in #Alvin's answer's case, here is something I'd think about: if your application is meant to last for some years, it is going to face several changes in how applications and systems work.
For instance, let's say you were thinking about this 10 years ago. I was watching Dexter back then, but I guess you actually have memories of how things were at that time. From what I can tell, multithreading was not much of an issue to developers of 2000, and now it is. So Moore's law broke for them. Before that people didn't even care about what will happen in "Y2K".
Speaking of Moore's law, processors are indeed getting quite fast, so maybe certain optimizations won't be even that necessary. And possibly the array of optimizations will be much bigger, some processors are getting optimizations for several server-centric stuff (XML, cryptography, compression and regex! I am surprised such things can get done on a chip) and also spend less energy (which is probably very important for warfare hardware...).
My point being that focusing on what exist today as a platform for tomorrow is not a good idea. Make it work today, and surely it will work tomorrow (backward-compatibility is especially valued by Microsoft, Apple is not bad it seems and Linux is very liberal about making it work as you want).
There is, yes, one thing that you can do. Attach your technology to something that just won't (likely) die, such as Javascript. I'm serious, Javascript VMs are getting terribly efficient nowdays and are just going to get better, plus everyone loves it so it's not going to dissappear suddenly. If needing more efficiency/features, maybe target the CRL or JVM?
Also I believe multithreading will become more and more of an issue. I have a gut feeling the number of processor cores will have a Moore's law of their own. And architectures are more than likely to change, from the looks of the cloud buzz.
PS: In any case, I belive C optimizations of the past are still quite valid under modern compilers!
I would stick to that language that you use for generating that language. You can generate and compile Java code in Java, Python code in Python, C# in C#, and even Lisp in Lisp, etc.
But it is not clear whether such languages are sufficiently fast for you. For top speed I would choose to generate C++ and use GCC for compilation.
Why not use something like SpiderMonkey or Rhino (JavaScript support in Java or C++). You can export your objects to JavaScript namespaces, and your users don't have to compile anything.
Embed an interpreter for a language like Lua/Scheme into your program, and generate code in that language.
I'm taking an introductory course (3 months) about real time systems design, but any implementation.
I would like to build something that let me understand better what I'll learn in theory, but since I have never done any real time system I can't estimate how long will take any project. It would be a concept proof project, or something like that, given my available time and knowledge.
Please, could you give me some idea? Thank you in advance.
I programm in TSQL, Delphi and C#, but I'll not have any problem in learning another language.
Suggest you consider exploring the Real-Time Specification for Java (RTSJ). While it is not a traditional environment for constructing real-time software, it is an up-and-coming technology with a lot of interest. Even better, you can witness some of the ongoing debate about what matters and what doesn't in real-time systems.
Sun's JavaRTS is freely available for download, and has some interesting demonstrations available to show deterministic behavior, and show off their RT garbage collector.
In terms of a specific project, I suggest you start simple: 1) Build a work-generator that you can tune to consume a given amount of CPU time; 2) Put this into a framework that can produce a distribution of work-generator tasks (as threads, or as chunks of work executed in a thread) and a mechanism for logging the work produced; 3) Produce charts of the execution time, sojourn time, deadline, slack/overrun of these tasks versus their priority; 4) demonstrate that tasks running in the context of real-time threads (vice timesharing) behave differently.
Bonus points if you can measure the overhead in the scheduler by determining at what supplied load (total CPU time produced by your work generator tasks divided by wall-clock time) your tasks begin missing deadlines.
Try to think of real-time tasks that are time-critical, for instance video-playing, which fails if tasks are not finished (e.g. calculating the next frame) in time.
You can also think of some industrial solutions, but they are probably more difficult to study in your local environment.
You should definitely consider building your system using a hardware development board equipped with a small processor (ARM, PIC, AVR, any one will do). This really helped remove my fear of the low-level when I started developing. You'll have to use C or C++ though.
You will then have two alternatives : either go bare-metal, or use a real-time OS.
Going bare-metal, you can learn :
How to initalize your processor from scratch and most importantly how to use interrupts, which are the fastest way you have to respond to an externel event
How to implement lightweight threads with fast context switching, something every real-time OS implements
In order to ease this a bit, look for a dev kit which comes with lots of documentation and source code. I used Embedded Artists ARM boards and they give you a lot of material.
Going with the RT OS :
You'll fast-track your project, and will be able to learn how to fine-tune a RT OS
You may try your hand at an open-source OS, such as Linux or the BSDs, and learn a lot from the source code
Either choice is good, you will get a really cool hands-on project to show off and hopefully better understand your course material. Good luck!
As most realtime systems are still implemented in C or C++ it may be good to brush up your knowledge of these programming languages. Many realtime systems are also embedded systems, so you might want to play around with a cheap open source one like BeagleBoard (http://beagleboard.org/). This will also give you a chance to learn about cross compiling etc.